Titanic.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'PassengerId', u'Survived', u'Pclass', u'Name', u'Sex', u'Age',\n",
       "       u'SibSp', u'Parch', u'Ticket', u'Fare', u'Cabin', u'Embarked'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#coding:utf-8\n",
    "# 这个ipython notebook主要是我解决Kaggle Titanic问题的思路和过程\n",
    "\n",
    "import pandas as pd #数据分析\n",
    "import numpy as np #科学计算\n",
    "from pandas import Series,DataFrame\n",
    "\n",
    "data_train = pd.read_csv(\"/Users/yumh/project/kaggle/kaggle_titanic/Train.csv\")\n",
    "data_train.columns\n",
    "#data_train[data_train.Cabin.notnull()]['Survived'].value_counts()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            714 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          204 non-null object\n",
      "Embarked       889 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.6+ KB\n"
     ]
    }
   ],
   "source": [
    "data_train.info()\n",
    "#我们发现有一些列，比如说Cabin，有非常多的缺失值\n",
    "#另外一些我们感觉在此场景中会有影响的属性，比如Age，也有一些缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EGFMTLBEEmAM5B9idvdtNi6G50Lqvx/UDr6av548fLKN320a8MPgaAH7aUaTN\negen2WL3oz8z8+4zadc0htveWsi7P2+u9vMwNUtEYlyDxl0qIg+IyEsi8po4kkRkqoi8JSL9/R2r\n8T5LBAGmsKL4+NhAhTI3Q9ZmSKn4spCq8vTnv/H0579xWfck3rjldE5P6kZsRGzx/gSN2kNCa1g7\nh6axUbw3qg/9OjTm0RnL+ednv1JQoN58asa3Hgbex/lO6KmqdwPLcYahHoEz1tCtwEh/BWh8xxJB\ngNmYtREoY4yh4/UD5VcU5xcof565glfT13NDr2T+de2pRISFEBoSSu/E3szbPu/EsNQizlnBxm8h\n7xgxkWFMujmVG3sn89q3G7jrncUcPpbn7adovC8OWAXsAuKxYaiDjiWCALM+cz0RIRG0qN+i+IKM\nHyC6gdMZzI3c/AL+8N4Spv60mdHntOOpwV0JDTlxGalvUl92Htl5YlhqgA7nQe4Rp/4BCAsN4YnL\nu/KXS7vw+crfufiF71m0aZ9Xn6PxuligN3CD66+pq9yGoQ4SlggCzPqs9aTEpxAaUqIT0aYfILlv\nmdNSAuTlF3Dfe0uYtXQ7D13QiUcu6lyq1dGZSc7ZxA/bfjhRmNIPQiNh7ZzjRSLCiLPaMG1kb/IL\nlKsnzOMfH6+ys4Paa5uq3ge8A7wMLHQNQ90N+AF4A/gjzhDUNgx1APJaInBVNv1HRF4XkaFFyru6\nKpqmum53EpHJIvKmiPhmWMYgtj5zPe0T2hcvzNoG+zPc9h/Iyy/g/veXMnvZDv50cWfu6t++zPUS\n6yfSJr4NP27/8URhRD2n3mHtl6XW79W2EZ+N6ceNvVvz5o8bOf/57/hmtbUqqq1U9d+q+omqjlPV\nMao6Wh3bVfVmVR2uqv/zd5zG+7x5RnAlMF1VRwJFZ8seA9wF3Anco6qrXW+oYUAvL+4/6B3KOcSO\nwztKJ4JN7usHVJVHZyxn1tLtPHJRZ0adXf7QE2cmncminYs4mlekd2qH82HvWti3sdT6sVHh/OPy\nrkwf3YfoiFCGvbmAMdN+Yc8hG5rCmNrCm4mgJbDFdbvoPIbxqpqpqlk41yIBEJHrgNI/I08st8qn\nSiqcjKZUIsj4ASLjoXm3Uo954et1/HfRVsac24HR51Q8/lDfpL4cyz/Gop2LThR2OM/5f92csh8E\nnNa6IbPvPYv7Bnbg0+U7GDjuW75c+XvFT8oY43PlJgIRSXbz16CM1bfiJIOS280SkXgRiQMOurZ7\nHdBaVSeWHH93AAAdE0lEQVS427dVPlXe+kw3iWDTXEjuDSXqDT5Ztp3n56zhyp4tuG9gB4/2kdo8\nlYiQCOZun3uisFE7aNi2wmGpI8NCuW9gRz69tx/JDesxesoiZiy2UTN9YfPmzWX+7d+/39+hmVqo\nojOCV4D2QIcSf0+Xse4MYIiIvAp8LCJvu8rHAy8CLwEvikgP4P8BbURkXPWfgim0dv9aokKjaBFb\npMXQwd+dOQhK9B/YtPcwj3ywnNNaN2Dslad4PChadFg0qc1T+W7rdyeakQK0Pw82fge5FQ9o1qFZ\nLO+O7E2vNo148L9L+d9vOz3at/HcnXfeybp161i7dm2xv4cftmo5U1pFYw3tKKtyyPWLvhhVPQwM\nK1I01VW+gtLznbbEeN36TGcympCirfwK6weKVBTn5Rdw77QlhAiMv87pJ1AZ5yafyxPzn2Bt5lo6\nNujoFHY4H35+zbkM1aHi0UhjIsOYdEsq106cx93v/MKMO/vSuXlcpeIw7iUmJjJgwIBS5dOmTfND\nNKa2q+gbwF3XUOsyWguty1xXRv3AXIiIhebdjxf9+8cMlm7J5J9XnkLLBvUqvZ8ByQMQhDmbitQJ\npJzl7OfXjzzeTkxkGG/ccjoxkWHc884vZOfkV/wg4xF3Z3i+HA7b1F0VnRFcJSLzgcJ3j7pup/gy\nKFN5Wcey2J292039QC8IdQ719sxsxn21hnM7N+Xibs2rtK/G0Y3p0bQHX236ijtPvdMpDI+CThfB\nrx/DJeMg1LMhqZvFRfHc1d25efLPPDl7FU9dUbpC21Te9OnT6d279/HLdyKCqpKRkeHfwEytVFEi\n+EBVJ5csFJHePorHVNGa/WsA6NCgSKXv4T2w+zc45drjRf/36a8UqPK3QSdX69fhea3P4+kFTx8f\n8hqAk6+A5e87dQXtz/V4W2d3bMKos9sy8bsNnH9yc87paI0DqmvIkCEMHz68VPn8+fPLWNsEu6pe\nGjK1zMo9KwHo0qjIEBLH6weciuIV27L4ZNkObjurLa0aVv6SUFEXpFxAqITy0boil4LaDXAuD638\nsNLbu/+8jrRtEsNjHy7nSI71QK6uSib5KBGZICLTReQOEbnB1TH0LVdH0TI7i5rAUdEZQaaIDMe5\nHFSYFASY59OoTKWt2ruK5jHNaRjV8ERhxlwIrwdJPQB47svVxEeHM/LsMoaorqQm9ZrQr0U/Zq2f\nxT097iEsJKz05aGwCI+3FxUeytgrT+Ga1+bx/FdreOwS92MimYolJCQwefJkVPV4UlBV+vTpU9bq\nR1V1tGssobeASFW9WkQuxekoCk5n0Y9F5D1cDUFM4Cg3EajqH2sqEFM9q/at4uRGJxcvzPgeWvWC\n0HB+2byfb1bv5o8XdiI+2jtTSl7R4QrSt6bz3dbvGJDsaqHS7Wrn8tCaz6HLoPI3UMIZbRpy/RnJ\nvPHDRgZ1b0G3ljbbWVU988wzlVpfRAYBdwBvA4NdxZtwxhsCZ0hqKN5ZtOjjRwGjAJKTkysZrfE3\nG3QuABzMOcimA5uKXxY6vAd2rYI2/QB4/fsNxEWFcUufFK/tt1/LfjSt15Qpv045UdhuAMQmwS9v\nu39gOR65qDON6kfy6IfLyMt3O+qx8TJVnaWqFwFFL/0UG33UVVbmd4Z1AK3bLBEEgFV7VwEl6gcy\nvnf+TzmbTXsP8/mK3xnauzUxkd6bpjo8JJxbutzCgt8XsHT3UqcwNAxOvcEZbiJrW6W3GR8dzuOX\ndWHFtgO8NW+T12I15YoVkRdE5DXgU2Cmq2Po1TgdRYt1FvVjnMZHLBEEgMU7FyMIpzQ55UThxu+c\nitukHkz+YSOhIcKtfVO8vu+rOl5FXEQck5YVGZ24x42gBbCkapeSL+mWSFqnJjz35Wp2ZGV7KVJT\njoOqeq+q3q6qL6vqO6p6h6reqqqHXX/DXGVWPxCALBEEgEU7F9GpYSfiIor0zN34PbTuw8Fc5f2F\nWxnUvQXN4qK8vu964fW49eRbSd+azoLfFziFDds4l4gWTIK8yo8yKiI8cXlX8lX526yVXo7YGFOS\nJYI6Ljc/l6W7l5LaLPVE4YEdzrDQKf34eOkOsnPzualPa5/FcFOXm2ge05xnFzxLQeFshn3vgUM7\nYfl/q7TNVg3rMebcjnyxcidfrbKxiIzxJUsEddzS3Us5mn+U05qddqKwsH6gTT/eW7iFTs1i6e7D\nFjhRYVHc1/M+ft33K/9d7frib9vfGfZ67gtQULVK39v6taFTs1ge/2iFzW5mjA9ZIqjjvt36LWEh\nYfROLNLZe0M6RCWwmjYs3ZLJNae38vkYMxe3uZheib14fvHz/H74d2di+7P+AHtWO81JqyA8NIT/\nu7Ir27OO8q85a7wcsTGmkCWCOi59SzpnND+D+hH1nYKCAmdegPbn8t6i7YSHClf0aFH+RrxARHi8\nz+MUaAFPzH/CGeOmyxWQeCr870mPhqcuy2mtG3JDr2Qmz81gxbYsL0dtjAFLBHXa6n2ryTiQQVqr\ntBOFO5bA4V3kth3Ih79s5fwuzWkY43kP3+poFduKe3rcw3dbv+PTjZ9CSAic9w/I2gJzx1d5uw9f\n0JkG9cJ57MPl5BfYqCfGeJslgjrsw3UfEh4SzkUpF50oXPsVIHyTfwr7j+RyzemtajSmGzrfwClN\nTuHpn58m82gmtD0Hul4F3z0LO6vWAii+Xjh/ubQLS7dmMfUn61tgjLdZIqijso5lMXPdTAa2HkhC\nVMKJBWu/hBan8faywyTFR3FW+8Y1GldoSCh/7f1XDuQc4PnFzzuFFz0D0QkwYxTkHK7Sdgd1T6Jf\nh8Y88/lqdh6o2mUmY0zZLBHUUROWTuBI7hFGdht5ojBrG2xbSFbyAH5Yt4erUlsRGlLzE5F0atiJ\nm7vczIy1M5xJ7mMaweAJzpAXM++sUisiEeHJwV3JzS/gHx+v8kHUxgQvryUCd0PVikhXEZnq+uvq\nKntNRJa735opzxcZXzD116lc3fHq4vMPrJoJwIc5ZwBw9Wn+mxF0dPfRJMUk8cS8J8jNz3Wmrxz4\nNyfGTx+oUjJo3SiGewa0Z/byHTbPsXcluD6374nI+SLygIi85PqciogkuT6/b4lIf38Ha7zPewPP\nOMPVljVU7RjgLpxhrJ8BblfV20Vkuhf3HXBy83P5ZMMn/Lj9Rw7lHiIhMoF6YfXYdmgbc7fP5dQm\np/JA6gPFH7TyQ7R5N15fGcqZ7RpXe86B6qgXXo9Hez3KPf+7h/fXvM/Qk4ZC33shez/88Dzk5cCl\nz1dqqGqAUWe3Y9bS7fz5wxV8eX8j6ntx7KQglqmqI0WkATAOiFDVoSJyN3AWkAaMBVYCU4Bv/Bap\n8QlvXhpqCWxx3S46VG28qmaqahYQ6+nGRGSUiCwUkYW7d+/2Ypi1357sPQz9dCh//fGvLNm9hH1H\n9/HLrl+Ys3kOOw7v4I7udzDx/InUCy/yRb9vI2xdQEbzC9iWmV3jlcRlOaflOfRO7M2rS1/lQM4B\np2/BuY/DOQ/Dkinw1iA4VLljGxEWwtghp7DjwFH+3xerfRR50PozMAkoPCibcD7XLYEtqur2NC6Y\nP6+BwJs/pwqHql1C8QSTJSLxOGcEBz3dmKpOBCYCpKamBk2bwey8bEZ/NZrNBzfzfNrznJt8rmed\nwRb9GySUSVlnEB8tnN+lmc9jrYiI8EDqA1zz8TVMWjaJ+1Pvd5JB/z9B447w0V0w8Ry45i1omVrx\nBl16Jjfglj4p/GdeBpd1T+K01g189ySChIg8DXwGLMCZlwCcYaiX4fpsi8gBd48P1s9roPDmGUGx\noWpFpHBA+vHAi8BLrv8RkaeAHq7p8SK9GEOd98LiF1i9fzXPnfMcA1sP9CwJ5B2DX6aQ0+4C/rum\ngCt6tCAqPNT3wXqgc8PODGo3iCm/TmHboSLDUne7CkZ8CSFhMPlCZ4A69fz748ELOpEUH83DHyzj\nWF6Zc6UYzzUFBgJXAcOBxSIyHmdSmh+AN4A/4pwtTHK3EVN3ee2MQFUPA8OKFE11la8Abi6x7mPA\nY97ad6BYu38tU3+dyrWdrqVfy36eP/CXKXBkD3NiLyMnv4Drz6hdM0Td0+Mevsj4gvGLx/PM2UVm\nzkrsDqPS4cPbYfYDsGWBU28QUXHdRv3IMJ68oivD3lzAq+nruW9gR5/FHwR2qWp5p2TbKfEZNoHF\nmo/WIq8seYWY8BjuPvVuzx+UexS+fw5t1Yvn1ibSIzmBTs09roqpEc1imnFTl5v4bONnrNxTolNZ\nvYZw/XuQ9idY9h68cR7s2+DRdvt3asrlpybx8jfrWLvT46uOxpgSrMlFLbF2/1rmbJ7Dnd3vLN5B\nrCLfPg0HtrG6z7Os/+gIzwxp77sgq2F41+FMXzOdcYvGMen8ScUveYWEQNrD0OI0+GCEc6no5lnQ\ntHOF2/3rpV34bs1uHv5gGf8d3dcv/SZM3ZPyyOxqPT5j7CVeiqR2sDOCWmLab9OIDI3khk7XwooP\nYNpQmDTQ+X/ey2X/Sv5tNsz9F/S4kde3tKR+ZBiXdk+s+eA9UD+iPqO7j+bn33/mh20/lL1Sh4Ew\n/AtA4N8Xw+8VdzVpVD+Sv17WhcWbM5ky34afMKYqLBHUAgdzDvLxho+5uGV/4t8dCtOHw46lEBHj\n9Mb94k/wQg94pY8zkufqz+HzP8F7N0FSD/b2+wefLNvO5acmUS+i9p7kXd3xapJjkxm3aBz5BW4q\neJt2hmGfQlg0vDUY9q6vcLuDT23B2R2b8Mznv7Et06a2NKayLBHUArPWzyI7L5vrVsxxRg+9/GUY\nswxu/gju/cW5feFYqNcIvn8O3r0WfnoVTrkGbvqQKYv3cSyvgGFntvH3UylXeGg4Y3qOYV3mOmat\nn+V+xUbtnOcO8PZgZ8a1cogITw3uSoHCnz9c7gyBbYzxmCUCPyvQAqb9+i7dC8LokrkDbprpTP4e\nUuTQNGgNve+AWz+BB9fB8C/hgTVwxQSOhtbn7fkZDOjclPZN6/vviXjovNbncUrjU3hpyUtk55Xz\n671xe7hxOhzZB1OudHokl6NVw3o8eEEnvlm9m1lLt3s5amMCmyUCP5u/fT4ZBzdx3Z7fYcjrkNyr\n/AfENHLWqd8EgA9/2caeQzncdlbtPhsoJCL84bQ/sOvILqb+OrX8lZN6wHVTYe86ePd6yC3/ss+t\nfVPo3jKev3+8in2Hc7wYtTGBzRKBn01bMoGG+fmc334wnHRZpR57LC+fl/63ju4t4+nTrpGPIvS+\n1OappLVKY9LySew7uq/8ldumwZUTYfN8+O8wyHc/d3FoiDB2yCkcyM7lydk2QqkxnrJE4Ec79m/g\n292/MCQnhIiLnq7046f9vIVtmdk8eEEnn89J7G1/6PkHjuUdY9zCcRWvfPIVcPGzsOYz+GRMuT2Q\nT0qM4460dsxYvI3PV5Rft2CMcVgi8KP/zrkfUK465wmIiqvUYzOP5PDC12s5o03DGp98xhvaJrTl\n1q638tH6j5i3fV7FDzhjpDNY3S9T4Ou/l7vq3QPa071VAg+8v5R1uw55KeKAFiEibxSOCCwiN7iG\npX7LNbx8mUPMm8BhicBPctfN4YODazg7sjlJJ11R6cc//flqMrNz+dtlJ9e5s4FCt59yO63jWvOP\nef/gSO6Rih+Q9iicNswZxvqHf7ldLTIslFeH9iQqPJRRby8kKzvXi1EHpBxVHVHk/hWqOhJ4H2d4\n+cIh5kcCg/wRoPEtSwT+kJ3J55+PYV9oKNf2/VOlH/7Vqp28+/Nmhp+ZQpekyp1J1CZRYVE83udx\nth3axlM/PVVxs08RuOQ56DoE5jwOXz/h9jJRUkI0Lw/tyZZ9Rxjx7wVk59jAdJVQ+KIWG4baVVbm\nC2nDUNdtlgj8oGD2g7wRmU/7mBb0TU6r1GPX7TrEA+8voWuLOB44v5NvAqxBpzc/ndu7386s9bP4\ncN2HFT8gJBSufB163gzf/z/4+F5n9NUy9G7biH9d24PFm/cz6u2FlgwqLxlnCOrCIebBzXeGqk5U\n1VRVTW3SpElNxWe8xBJBTVs+nW82fML6iHBu63kPIeL5IVi36yBDJ80nIiyUV244rdYMNV1do08Z\nTa/EXjwx/wl+3PZjxQ8ICYXLXoB+D8Lit5yxifaXPbzEJackMnbIKcxdt4frX5/P3kNlJ40gFyoi\nE3CGhn8UmOkaTv5qnOHliw0x78c4jY/U3vEIAtHu1eR+8gdeSEykdWwSF6Rc4NHDCgqU6Yu38vdZ\nK4kKD+Wdkb1JbuS/aSi9LTQklHFp4xj++XDuS7+Pf/X/F32T+pb/IBE49y9OX4OZdzjDb/T/E/Qa\nDaHF39bXpLYiPjqce9/9hUEvzeVf153K6SkNffiM6px8VR1douydEveHYbyqNg18Z2cENeXwXnjn\nWt6NjWGD5PHg6Q8RFuI+D6sqm/ceYfIPG7lw/Hf8cfoyTkqM45N7z6p1w0x7Q1xEHBPOm0Cr2Fbc\nOedOJi2fRG6BB5W8J10Kd8yFlLPgy8fg5dNhybul+htccHJz3r+9D6EhwrWvzeOJT1ZZJbIxLnZG\nUBMO7Ya3Lmf10V28kNScfom9OaflOaVW25aZzY/r9jBv/V7mb9jL9qyjAHRvGc/4607lslOSCAng\nYZYbRzfmrYve4i9z/8L4xeOZuW4m13a6ljNbnElKXIr7y2gJyXDDe7D6M0j/P5g52mlieupQ6HkT\nNEgBoHurBD4d04+nZv/K5LkbmbF4K7f0TWFor9Y0ibWJ8kzwskTga9sWwX+H8Xv2Hu5r24k4EZ44\n8wlEhD2HjjFv/V5+XL+XH9fvYdNepwllo5gIerdtxB1tG9KnXeM6MYaQt8SExzAubRzfbP6G15e/\nzjMLnoEFECZhNIxuSFxEHPXD6xMTEeP8Hx5DTHgMcRFxNI9pTtLlz5G4ay3Nln9IxA/jnArlpJ7O\nmcNJg6jfuAP/vLIbN/ZO5rkv1/CvOWt5+Zt1nNm+MRee3Jwz2zemZYPoOtsk15iqsETgK/sz4McX\nYdG/+bFBIn9JacfhvGyGtfsnL371O/PWr2S1a1at2MgwerVtxC19UujbvhEdm8YG9C9/T/RP7k//\n5P5kZGWweNdithzcwp7sPRzKOcSh3EMcOHaA7Ye2czjnMIdyD3Ekr3Q/hMadupFIKE2zD5Cw5CUa\nLB5Pg/B4GjTuSELzHjx4Tm/uHdidT5Zk8cWqnTwyw5n/oGlspGumtzg6NK1P+6b1adM4JmAq540p\nyWuJQERigFeAHCBdVae6yrsCj7pW+6eqrhCRsUA94IiqPlLdfefmF5Cd6zQNPJp3jNz8HApU0dyj\naH4uUEBBQT4FKJpfgJKPFigFFCBaQIGCagEFWoCqogX5KIpqAahSUKDOY9Qpc9q7F1BQoBSoIgVH\nCcneR0j2XkL3r6dg1zKyDm9gTUQkn7XswMbQw4QcieDg5pv55/IjRIZt5vSUhlzeI4m+7RrTNSmO\nsFCrrilLSnwKKfEpFa53LP8YOw/vZMfhHSf+Djn/ZxzZTWZ0DJnHMslHIXsNbFwDG98DIFqFxMRo\nOraMI1ITyD2WwP59MczOqM+xvFiOFUQDYTSsF0PT2Ho0iatHs9goGsVFUz8qnNioCGKjwqgfGUZo\naAgRoSGECoSFhhAWIoSECCElquPcnnEITmV3WFSp4kJRoVGEhoR6dNZSLzw06H9UmIp584ygsPfh\nxyLyHq7J64ExwF04nVSeEZGngHBVvVdEnhWRVqq6xc02PfLp8h2MmbYEgMimnxLR6LvqbK766nN8\ndNCCowkkHL2YrnHncdq5TUlNaUjXpHgiwuyL35siQyNJjksmOS7Z7ToFWsDBnINkZu9j/47F7Nm+\nkO371rDj0DZ2HMlkh+xnbdg29oeGQgLOXxHbXX8ocMD15weHN95NwdGWFa8I/Pync2kaF1Xxiiao\neTMRtAQK5xYs2nMnXlUzAUQkFmjBiV6Kmynea/E4ERkFjAJITnb/4QY4OSmeP19yEgDbsgvYldMe\nQYg/spl6x3a5fjkJIuL6ZRVSuA+nnBAQ5xYiFP4rfIzzICGkyPITywQknLzwOPLC61MQ1YDY6Fha\nJyTRqVEKbRs2tXl0a4kQCSE+Mp74yHhaJ7SBk4YUXyE3Gw7u4EjWVn4/uJUdh3dw8FgWuXlHydU8\ncgvyyNV8UEVRcvPyyc3PJy9fyc0vIL+ggHx1WnwVuP5XhYIScSjl96A+HNGEg/Xcv+c7JadSL7SB\nR885JtKu/pqKefNdUtj7cAnFm6VmiUg8zu+og8A2TvRSbAXMLGtjqjoRmAiQmppa7ienves6rqNt\nVeM3wS48Ghq2pV7DtrTF3kkmeHgzEcwAXhKRS4CPReRtVb0JGA+86FrnGVXdLCK5IjIOOFbdy0LG\nGN9zVwdoAoPXEoGqHqZ478OprvIVwM0l1n0UY0xd4q4O0AQAqQsTfYvIbpyREN1pDOzxcRi+3kcg\nPAfbR93cR2tVLXekONcYRJ+p6hIReUdVbyix/HidHtAJWF3VgKmZ1yUYYqjwuBaqEzVJHrxJF6pq\nqi9j8PU+AuE52D4Ceh/u6gCB4nV61VUTr4vFUFydSATGGL8rVgfo72CMd1kiMMZUqIw6QBNAAqVX\nk1dOSf28j0B4DraP4NyHt9WGmIMqhjpRWWyMMcZ3AuWMwBhjTBVZIjDGmCBXZyuLRWQ4cIuqniMi\nScCzOGMcvamq33hh+2/i9KKMAG4Dmnl7H0X25ZNemyIyGLgEiAPeALoBbYBwYLR66bqgK/5vgb/h\ntCH36j5EJAR4Aud5LARygf5AJHCHqyKzuvtIBl4A9gFrcMbB8so+RKQt8BjOuFtXicgNRbftWq1a\nx7+MfdTY+zcQiMjZwI+qmici4UBvVf3eT7GEqGrJIap8yxkYq2794QwD80ecno4Af8H5kgsB3vHy\nvsbjjInky33cBFzmuv2eD16vBsCbwFTX/buBfl7c/j9cx2OQL/YBXAH8BxgHnAv811V+KXCTl/Zx\nCXBj4THw0T4K36/Ftu3N41+4jyL3ff7+9dJr86Dr/1HAJ8DTNbz/CSXuv+yH16AH8D7OfNHfAEOB\nU2pi33Xu0pDr1+EDwL+KFLcEtqiXs6iIdAYi1RkPySf7cCk6Amt+eStW0Z+BScBu1/1NnBj4r1pE\n5DxgFbALiPfFPnDOMn5U1ftxfkEXnmV4cx/zgREi8j/gcx/to1DJbfvk+Nfg+9cbCl/j01X1UiC6\nhvefU+K+Lz6HFbkLuF6dXtvn4bzXb6qJHdepS0MicjNwDc4p9TNAdxG5GFevRxGp9gjxrn30BD7E\nOQh3uhZ5bR9lKLfXZlWJM872WOAzYAEnLkMkA8u8tJs0IAbogvPh2e+DfWzlxAc1nxPztCS7lnnD\nMOBxVf1ORKZzYvRob+6jpKLb9urxd00IdR818/71hiYicgGw03W/xr+bXPtfAKRSfC6gmnJQVfMB\n1LlEtUhVH6qJHdfp5qMiMl2d66FJOF94ecAUVf1fNbcbgjNc9mc4X0BP4nwxeG0fJfYXA7wEHAV+\nUO/VEdwL3ILz5l6CMytca05c9/bawReRW3HGReno7X2ISD2cEWyPAL/hJJt+OL8a71Lv1BF0xanj\n2AMcAhZ7ax8i0gh4CudX3iScM4Hj23atVq3jX2Ifk3EuzdXI+9cbRKQLcD4wSVUPicjlqvpRDe4/\nHBgJnAyscMWRW1P7d8XwAjCbE8noMlW9p0b2XZcTgTHGBApXMroN6AqsxElGJS9Z+WbflgiMMSa4\n1bnKYmOMMd5licAYY4KcJQJjjAlydar5qKmdRORZnE5rAEOAD1y3k4A7VTXDH3EZYzxjicB4w2pV\nnQQgIvOL3B7o37CMMZ6wS0PGGBPkLBEYY0yQs0RgjDFBzhKBMcYEOassNt6gIjLJdTtFRHoXWfaw\nPwIyxnjOhpgwxpggZ5eGjDEmyFkiMMaYIGeJwBhjgpwlAmOMCXKWCIwxJsj9fxg9IHcF94DnAAAA\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110b5b7d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "fig = plt.figure()\n",
    "fig.set(alpha=0.2)  # 设定图表颜色alpha参数\n",
    "\n",
    "plt.rc('font', family='SimHei', size=7)  ##显示中文\n",
    "\n",
    "plt.subplot2grid((2,3),(0,0))             # 在一张大图里分列几个小图\n",
    "data_train.Survived.value_counts().plot(kind='bar')# plots a bar graph of those who surived vs those who did not. \n",
    "plt.title(u\"获救情况 (1为获救)\") # puts a title on our graph\n",
    "plt.ylabel(u\"人数\")  \n",
    "\n",
    "plt.subplot2grid((2,3),(0,1))\n",
    "data_train.Pclass.value_counts().plot(kind=\"bar\")\n",
    "plt.ylabel(u\"人数\")\n",
    "plt.title(u\"乘客等级分布\")\n",
    "\n",
    "plt.subplot2grid((2,3),(0,2))\n",
    "plt.scatter(data_train.Survived, data_train.Age)\n",
    "plt.ylabel(u\"年龄\")                         # sets the y axis lable\n",
    "plt.grid(b=True, which='major', axis='y') # formats the grid line style of our graphs\n",
    "plt.title(u\"按年龄看获救分布 (1为获救)\")\n",
    "\n",
    "\n",
    "plt.subplot2grid((2,3),(1,0), colspan=2)\n",
    "data_train.Age[data_train.Pclass == 1].plot(kind='kde')   # plots a kernel desnsity estimate of the subset of the 1st class passanges's age\n",
    "data_train.Age[data_train.Pclass == 2].plot(kind='kde')\n",
    "data_train.Age[data_train.Pclass == 3].plot(kind='kde')\n",
    "plt.xlabel(u\"年龄\")# plots an axis lable\n",
    "plt.ylabel(u\"密度\") \n",
    "plt.title(u\"各等级的乘客年龄分布\")\n",
    "plt.legend((u'头等舱', u'2等舱',u'3等舱'),loc='best') # sets our legend for our graph.\n",
    "\n",
    "\n",
    "plt.subplot2grid((2,3),(1,2))\n",
    "data_train.Embarked.value_counts().plot(kind='bar')\n",
    "plt.title(u\"各登船口岸上船人数\")\n",
    "plt.ylabel(u\"人数\")  \n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10cd38e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c06d050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#看看各乘客等级的获救情况\n",
    "fig = plt.figure()\n",
    "fig.set(alpha=0.2)  # 设定图表颜色alpha参数\n",
    "\n",
    "Survived_0 = data_train.Pclass[data_train.Survived == 0].value_counts()\n",
    "Survived_1 = data_train.Pclass[data_train.Survived == 1].value_counts()\n",
    "df=pd.DataFrame({u'获救':Survived_1, u'未获救':Survived_0})\n",
    "df.plot(kind='bar', stacked=True)\n",
    "plt.title(u\"各乘客等级的获救情况\")\n",
    "plt.xlabel(u\"乘客等级\") \n",
    "plt.ylabel(u\"人数\") \n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110e6a5d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAELCAYAAAAx94awAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112038a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#看看各登录港口的获救情况\n",
    "fig = plt.figure()\n",
    "fig.set(alpha=0.2)  # 设定图表颜色alpha参数\n",
    "\n",
    "Survived_0 = data_train.Embarked[data_train.Survived == 0].value_counts()\n",
    "Survived_1 = data_train.Embarked[data_train.Survived == 1].value_counts()\n",
    "df=pd.DataFrame({u'获救':Survived_1, u'未获救':Survived_0})\n",
    "df.plot(kind='bar', stacked=True)\n",
    "plt.title(u\"各登录港口乘客的获救情况\")\n",
    "plt.xlabel(u\"登录港口\") \n",
    "plt.ylabel(u\"人数\") \n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110f66cd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAEKCAYAAAD6q1UVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nH6Ah8Qdo6P8AhLlZgMRdQ0AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11093dbd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#看看各性别的获救情况\n",
    "fig = plt.figure()\n",
    "fig.set(alpha=0.2)  # 设定图表颜色alpha参数\n",
    "\n",
    "Survived_m = data_train.Survived[data_train.Sex == 'male'].value_counts()\n",
    "Survived_f = data_train.Survived[data_train.Sex == 'female'].value_counts()\n",
    "df=pd.DataFrame({u'男性':Survived_m, u'女性':Survived_f})\n",
    "df.plot(kind='bar', stacked=True)\n",
    "plt.title(u\"按性别看获救情况\")\n",
    "plt.xlabel(u\"性别\") \n",
    "plt.ylabel(u\"人数\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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epaSubH3s6VrWs37tcC3rUe90suc+RSvgX7RcRLwcuAv47cz83lwLZuZEZq7LTE9ElqQe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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10cf76710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#然后我们再来看看各种舱级别情况下各性别的获救情况\n",
    "fig=plt.figure()\n",
    "fig.set(alpha=0.65) # 设置图像透明度，无所谓\n",
    "plt.title(u\"根据舱等级和性别的获救情况\")\n",
    "\n",
    "ax1=fig.add_subplot(141)\n",
    "data_train.Survived[data_train.Sex == 'female'][data_train.Pclass != 3].value_counts().plot(kind='bar', label=\"female highclass\", color='#FA2479')\n",
    "ax1.set_xticklabels([u\"获救\", u\"未获救\"], rotation=0)\n",
    "ax1.legend([u\"女性/高级舱\"], loc='best')\n",
    "\n",
    "ax2=fig.add_subplot(142, sharey=ax1)\n",
    "data_train.Survived[data_train.Sex == 'female'][data_train.Pclass == 3].value_counts().plot(kind='bar', label='female, low class', color='pink')\n",
    "ax2.set_xticklabels([u\"未获救\", u\"获救\"], rotation=0)\n",
    "plt.legend([u\"女性/低级舱\"], loc='best')\n",
    "\n",
    "ax3=fig.add_subplot(143, sharey=ax1)\n",
    "data_train.Survived[data_train.Sex == 'male'][data_train.Pclass != 3].value_counts().plot(kind='bar', label='male, high class',color='lightblue')\n",
    "ax3.set_xticklabels([u\"未获救\", u\"获救\"], rotation=0)\n",
    "plt.legend([u\"男性/高级舱\"], loc='best')\n",
    "\n",
    "ax4=fig.add_subplot(144, sharey=ax1)\n",
    "data_train.Survived[data_train.Sex == 'male'][data_train.Pclass == 3].value_counts().plot(kind='bar', label='male low class', color='steelblue')\n",
    "ax4.set_xticklabels([u\"未获救\", u\"获救\"], rotation=0)\n",
    "plt.legend([u\"男性/低级舱\"], loc='best')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">0</th>\n",
       "      <th>0</th>\n",
       "      <td>398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1</th>\n",
       "      <th>0</th>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2</th>\n",
       "      <th>0</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">3</th>\n",
       "      <th>0</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">4</th>\n",
       "      <th>0</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                PassengerId\n",
       "SibSp Survived             \n",
       "0     0                 398\n",
       "      1                 210\n",
       "1     0                  97\n",
       "      1                 112\n",
       "2     0                  15\n",
       "      1                  13\n",
       "3     0                  12\n",
       "      1                   4\n",
       "4     0                  15\n",
       "      1                   3\n",
       "5     0                   5\n",
       "8     0                   7"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g = data_train.groupby(['SibSp','Survived'])\n",
    "df = pd.DataFrame(g.count()['PassengerId'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <th>Survived</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">0</th>\n",
       "      <th>0</th>\n",
       "      <td>445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1</th>\n",
       "      <th>0</th>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2</th>\n",
       "      <th>0</th>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">3</th>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">5</th>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                PassengerId\n",
       "Parch Survived             \n",
       "0     0                 445\n",
       "      1                 233\n",
       "1     0                  53\n",
       "      1                  65\n",
       "2     0                  40\n",
       "      1                  40\n",
       "3     0                   2\n",
       "      1                   3\n",
       "4     0                   4\n",
       "5     0                   4\n",
       "      1                   1\n",
       "6     0                   1"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g = data_train.groupby(['Parch','Survived'])\n",
    "df = pd.DataFrame(g.count()['PassengerId'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "C23 C25 C27        4\n",
       "G6                 4\n",
       "B96 B98            4\n",
       "D                  3\n",
       "C22 C26            3\n",
       "E101               3\n",
       "F2                 3\n",
       "F33                3\n",
       "B57 B59 B63 B66    2\n",
       "C68                2\n",
       "B58 B60            2\n",
       "E121               2\n",
       "D20                2\n",
       "E8                 2\n",
       "E44                2\n",
       "B77                2\n",
       "C65                2\n",
       "D26                2\n",
       "E24                2\n",
       "E25                2\n",
       "B20                2\n",
       "C93                2\n",
       "D33                2\n",
       "E67                2\n",
       "D35                2\n",
       "D36                2\n",
       "C52                2\n",
       "F4                 2\n",
       "C125               2\n",
       "C124               2\n",
       "                  ..\n",
       "F G63              1\n",
       "A6                 1\n",
       "D45                1\n",
       "D6                 1\n",
       "D56                1\n",
       "C101               1\n",
       "C54                1\n",
       "D28                1\n",
       "D37                1\n",
       "B102               1\n",
       "D30                1\n",
       "E17                1\n",
       "E58                1\n",
       "F E69              1\n",
       "D10 D12            1\n",
       "E50                1\n",
       "A14                1\n",
       "C91                1\n",
       "A16                1\n",
       "B38                1\n",
       "B39                1\n",
       "C95                1\n",
       "B78                1\n",
       "B79                1\n",
       "C99                1\n",
       "B37                1\n",
       "A19                1\n",
       "E12                1\n",
       "A7                 1\n",
       "D15                1\n",
       "Name: Cabin, Length: 147, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#ticket是船票编号，应该是unique的，和最后的结果没有太大的关系，不纳入考虑的特征范畴\n",
    "#cabin只有204个乘客有值，我们先看看它的一个分布\n",
    "data_train.Cabin.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1118bfc90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AaYYMf1V3JfD1zPwGQERcHBF/Cfw08AdNn9+OiPOBH2TmTcApTd+dwL3Ah4C1wOOZeVdz\n7HcBmv7/uy8dKVz2UXUfBJ4Yt//fdP5fzAM+0rR9MzP/EPiViBj/f2Y0M2+hc4fK2b0oVmqL4a/q\nnqYzcz/os5n5R8A3geO7+nbfGvdG8/gTYMHslCfNDpd9VN2dwJci4kLgWOA9EXE98H7gH5o+H4uI\nXwJGMvNAhJ9D0tHP8FdpmbkPWD9Ft3u7zlnd9XgHQEScNgslSrPC8Jfaszgi7mqefx94btw+wF0T\nnCP1hZ/wlaSCfMNXkgoy/CWpIMNfkgoy/CWpIMNfkgr6H+TAeqaEr5CjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1118bfb10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#cabin的值计数太分散了，绝大多数Cabin值只出现一次。感觉上作为类目，加入特征未必会有效\n",
    "#那我们一起看看这个值的有无，对于survival的分布状况，影响如何吧\n",
    "fig = plt.figure()\n",
    "fig.set(alpha=0.2)  # 设定图表颜色alpha参数\n",
    "\n",
    "Survived_cabin = data_train.Survived[pd.notnull(data_train.Cabin)].value_counts()\n",
    "Survived_nocabin = data_train.Survived[pd.isnull(data_train.Cabin)].value_counts()\n",
    "df=pd.DataFrame({u'有':Survived_cabin, u'无':Survived_nocabin}).transpose()\n",
    "df.plot(kind='bar', stacked=True)\n",
    "plt.title(u\"按Cabin有无看获救情况\")\n",
    "plt.xlabel(u\"Cabin有无\") \n",
    "plt.ylabel(u\"人数\")\n",
    "plt.show()\n",
    "\n",
    "#似乎有cabin记录的乘客survival比例稍高，那先试试把这个值分为两类，有cabin值/无cabin值，一会儿加到类别特征好了"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>No</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>Yes</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>No</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>Yes</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>No</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>23.838953</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>No</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>Yes</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "      <td>male</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>No</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "      <td>female</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>No</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
       "      <td>female</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>237736</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>No</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Sandstrom, Miss. Marguerite Rut</td>\n",
       "      <td>female</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>PP 9549</td>\n",
       "      <td>16.7000</td>\n",
       "      <td>Yes</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Bonnell, Miss. Elizabeth</td>\n",
       "      <td>female</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113783</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>Yes</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Saundercock, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5. 2151</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>No</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Andersson, Mr. Anders Johan</td>\n",
       "      <td>male</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>347082</td>\n",
       "      <td>31.2750</td>\n",
       "      <td>No</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Vestrom, Miss. Hulda Amanda Adolfina</td>\n",
       "      <td>female</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>350406</td>\n",
       "      <td>7.8542</td>\n",
       "      <td>No</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Hewlett, Mrs. (Mary D Kingcome)</td>\n",
       "      <td>female</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>248706</td>\n",
       "      <td>16.0000</td>\n",
       "      <td>No</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Rice, Master. Eugene</td>\n",
       "      <td>male</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>382652</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>No</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Williams, Mr. Charles Eugene</td>\n",
       "      <td>male</td>\n",
       "      <td>32.066493</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>244373</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>No</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Vander Planke, Mrs. Julius (Emelia Maria Vande...</td>\n",
       "      <td>female</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>345763</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>No</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Masselmani, Mrs. Fatima</td>\n",
       "      <td>female</td>\n",
       "      <td>29.518205</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2649</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>No</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Fynney, Mr. Joseph J</td>\n",
       "      <td>male</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>239865</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>No</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Beesley, Mr. Lawrence</td>\n",
       "      <td>male</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>248698</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>Yes</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>McGowan, Miss. Anna \"Annie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330923</td>\n",
       "      <td>8.0292</td>\n",
       "      <td>No</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sloper, Mr. William Thompson</td>\n",
       "      <td>male</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113788</td>\n",
       "      <td>35.5000</td>\n",
       "      <td>Yes</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Miss. Torborg Danira</td>\n",
       "      <td>female</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>No</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>347077</td>\n",
       "      <td>31.3875</td>\n",
       "      <td>No</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Emir, Mr. Farred Chehab</td>\n",
       "      <td>male</td>\n",
       "      <td>29.518205</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2631</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>No</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Fortune, Mr. Charles Alexander</td>\n",
       "      <td>male</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>19950</td>\n",
       "      <td>263.0000</td>\n",
       "      <td>Yes</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>O'Dwyer, Miss. Ellen \"Nellie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>22.380113</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330959</td>\n",
       "      <td>7.8792</td>\n",
       "      <td>No</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Todoroff, Mr. Lalio</td>\n",
       "      <td>male</td>\n",
       "      <td>27.947206</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349216</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>No</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>861</th>\n",
       "      <td>862</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Giles, Mr. Frederick Edward</td>\n",
       "      <td>male</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>28134</td>\n",
       "      <td>11.5000</td>\n",
       "      <td>No</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>862</th>\n",
       "      <td>863</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Swift, Mrs. Frederick Joel (Margaret Welles Ba...</td>\n",
       "      <td>female</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17466</td>\n",
       "      <td>25.9292</td>\n",
       "      <td>Yes</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>863</th>\n",
       "      <td>864</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Sage, Miss. Dorothy Edith \"Dolly\"</td>\n",
       "      <td>female</td>\n",
       "      <td>10.869867</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>69.5500</td>\n",
       "      <td>No</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>864</th>\n",
       "      <td>865</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Gill, Mr. John William</td>\n",
       "      <td>male</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>233866</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>No</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>865</th>\n",
       "      <td>866</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Bystrom, Mrs. (Karolina)</td>\n",
       "      <td>female</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>236852</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>No</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>866</th>\n",
       "      <td>867</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Duran y More, Miss. Asuncion</td>\n",
       "      <td>female</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>SC/PARIS 2149</td>\n",
       "      <td>13.8583</td>\n",
       "      <td>No</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>867</th>\n",
       "      <td>868</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Roebling, Mr. Washington Augustus II</td>\n",
       "      <td>male</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17590</td>\n",
       "      <td>50.4958</td>\n",
       "      <td>Yes</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868</th>\n",
       "      <td>869</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>van Melkebeke, Mr. Philemon</td>\n",
       "      <td>male</td>\n",
       "      <td>25.977889</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>345777</td>\n",
       "      <td>9.5000</td>\n",
       "      <td>No</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>869</th>\n",
       "      <td>870</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Master. Harold Theodor</td>\n",
       "      <td>male</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>No</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>870</th>\n",
       "      <td>871</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Balkic, Mr. Cerin</td>\n",
       "      <td>male</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349248</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>No</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>871</th>\n",
       "      <td>872</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Beckwith, Mrs. Richard Leonard (Sallie Monypeny)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11751</td>\n",
       "      <td>52.5542</td>\n",
       "      <td>Yes</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>872</th>\n",
       "      <td>873</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Carlsson, Mr. Frans Olof</td>\n",
       "      <td>male</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>695</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>Yes</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>873</th>\n",
       "      <td>874</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Vander Cruyssen, Mr. Victor</td>\n",
       "      <td>male</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>345765</td>\n",
       "      <td>9.0000</td>\n",
       "      <td>No</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>874</th>\n",
       "      <td>875</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Abelson, Mrs. Samuel (Hannah Wizosky)</td>\n",
       "      <td>female</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>P/PP 3381</td>\n",
       "      <td>24.0000</td>\n",
       "      <td>No</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>875</th>\n",
       "      <td>876</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Najib, Miss. Adele Kiamie \"Jane\"</td>\n",
       "      <td>female</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2667</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>No</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>876</th>\n",
       "      <td>877</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Gustafsson, Mr. Alfred Ossian</td>\n",
       "      <td>male</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7534</td>\n",
       "      <td>9.8458</td>\n",
       "      <td>No</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>877</th>\n",
       "      <td>878</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Petroff, Mr. Nedelio</td>\n",
       "      <td>male</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349212</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>No</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>878</th>\n",
       "      <td>879</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Laleff, Mr. Kristo</td>\n",
       "      <td>male</td>\n",
       "      <td>27.947206</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349217</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>No</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>879</th>\n",
       "      <td>880</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)</td>\n",
       "      <td>female</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11767</td>\n",
       "      <td>83.1583</td>\n",
       "      <td>Yes</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>880</th>\n",
       "      <td>881</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Shelley, Mrs. William (Imanita Parrish Hall)</td>\n",
       "      <td>female</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>230433</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>No</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>881</th>\n",
       "      <td>882</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Markun, Mr. Johann</td>\n",
       "      <td>male</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349257</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>No</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>883</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dahlberg, Miss. Gerda Ulrika</td>\n",
       "      <td>female</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7552</td>\n",
       "      <td>10.5167</td>\n",
       "      <td>No</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>883</th>\n",
       "      <td>884</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Banfield, Mr. Frederick James</td>\n",
       "      <td>male</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C.A./SOTON 34068</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>No</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>884</th>\n",
       "      <td>885</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Sutehall, Mr. Henry Jr</td>\n",
       "      <td>male</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/OQ 392076</td>\n",
       "      <td>7.0500</td>\n",
       "      <td>No</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>885</th>\n",
       "      <td>886</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Rice, Mrs. William (Margaret Norton)</td>\n",
       "      <td>female</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>382652</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>No</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>No</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>Yes</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>16.193950</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>No</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>Yes</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>No</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "0              1         0       3   \n",
       "1              2         1       1   \n",
       "2              3         1       3   \n",
       "3              4         1       1   \n",
       "4              5         0       3   \n",
       "5              6         0       3   \n",
       "6              7         0       1   \n",
       "7              8         0       3   \n",
       "8              9         1       3   \n",
       "9             10         1       2   \n",
       "10            11         1       3   \n",
       "11            12         1       1   \n",
       "12            13         0       3   \n",
       "13            14         0       3   \n",
       "14            15         0       3   \n",
       "15            16         1       2   \n",
       "16            17         0       3   \n",
       "17            18         1       2   \n",
       "18            19         0       3   \n",
       "19            20         1       3   \n",
       "20            21         0       2   \n",
       "21            22         1       2   \n",
       "22            23         1       3   \n",
       "23            24         1       1   \n",
       "24            25         0       3   \n",
       "25            26         1       3   \n",
       "26            27         0       3   \n",
       "27            28         0       1   \n",
       "28            29         1       3   \n",
       "29            30         0       3   \n",
       "..           ...       ...     ...   \n",
       "861          862         0       2   \n",
       "862          863         1       1   \n",
       "863          864         0       3   \n",
       "864          865         0       2   \n",
       "865          866         1       2   \n",
       "866          867         1       2   \n",
       "867          868         0       1   \n",
       "868          869         0       3   \n",
       "869          870         1       3   \n",
       "870          871         0       3   \n",
       "871          872         1       1   \n",
       "872          873         0       1   \n",
       "873          874         0       3   \n",
       "874          875         1       2   \n",
       "875          876         1       3   \n",
       "876          877         0       3   \n",
       "877          878         0       3   \n",
       "878          879         0       3   \n",
       "879          880         1       1   \n",
       "880          881         1       2   \n",
       "881          882         0       3   \n",
       "882          883         0       3   \n",
       "883          884         0       2   \n",
       "884          885         0       3   \n",
       "885          886         0       3   \n",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         0       3   \n",
       "\n",
       "                                                  Name     Sex        Age  \\\n",
       "0                              Braund, Mr. Owen Harris    male  22.000000   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.000000   \n",
       "2                               Heikkinen, Miss. Laina  female  26.000000   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.000000   \n",
       "4                             Allen, Mr. William Henry    male  35.000000   \n",
       "5                                     Moran, Mr. James    male  23.838953   \n",
       "6                              McCarthy, Mr. Timothy J    male  54.000000   \n",
       "7                       Palsson, Master. Gosta Leonard    male   2.000000   \n",
       "8    Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.000000   \n",
       "9                  Nasser, Mrs. Nicholas (Adele Achem)  female  14.000000   \n",
       "10                     Sandstrom, Miss. Marguerite Rut  female   4.000000   \n",
       "11                            Bonnell, Miss. Elizabeth  female  58.000000   \n",
       "12                      Saundercock, Mr. William Henry    male  20.000000   \n",
       "13                         Andersson, Mr. Anders Johan    male  39.000000   \n",
       "14                Vestrom, Miss. Hulda Amanda Adolfina  female  14.000000   \n",
       "15                    Hewlett, Mrs. (Mary D Kingcome)   female  55.000000   \n",
       "16                                Rice, Master. Eugene    male   2.000000   \n",
       "17                        Williams, Mr. Charles Eugene    male  32.066493   \n",
       "18   Vander Planke, Mrs. Julius (Emelia Maria Vande...  female  31.000000   \n",
       "19                             Masselmani, Mrs. Fatima  female  29.518205   \n",
       "20                                Fynney, Mr. Joseph J    male  35.000000   \n",
       "21                               Beesley, Mr. Lawrence    male  34.000000   \n",
       "22                         McGowan, Miss. Anna \"Annie\"  female  15.000000   \n",
       "23                        Sloper, Mr. William Thompson    male  28.000000   \n",
       "24                       Palsson, Miss. Torborg Danira  female   8.000000   \n",
       "25   Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...  female  38.000000   \n",
       "26                             Emir, Mr. Farred Chehab    male  29.518205   \n",
       "27                      Fortune, Mr. Charles Alexander    male  19.000000   \n",
       "28                       O'Dwyer, Miss. Ellen \"Nellie\"  female  22.380113   \n",
       "29                                 Todoroff, Mr. Lalio    male  27.947206   \n",
       "..                                                 ...     ...        ...   \n",
       "861                        Giles, Mr. Frederick Edward    male  21.000000   \n",
       "862  Swift, Mrs. Frederick Joel (Margaret Welles Ba...  female  48.000000   \n",
       "863                  Sage, Miss. Dorothy Edith \"Dolly\"  female  10.869867   \n",
       "864                             Gill, Mr. John William    male  24.000000   \n",
       "865                           Bystrom, Mrs. (Karolina)  female  42.000000   \n",
       "866                       Duran y More, Miss. Asuncion  female  27.000000   \n",
       "867               Roebling, Mr. Washington Augustus II    male  31.000000   \n",
       "868                        van Melkebeke, Mr. Philemon    male  25.977889   \n",
       "869                    Johnson, Master. Harold Theodor    male   4.000000   \n",
       "870                                  Balkic, Mr. Cerin    male  26.000000   \n",
       "871   Beckwith, Mrs. Richard Leonard (Sallie Monypeny)  female  47.000000   \n",
       "872                           Carlsson, Mr. Frans Olof    male  33.000000   \n",
       "873                        Vander Cruyssen, Mr. Victor    male  47.000000   \n",
       "874              Abelson, Mrs. Samuel (Hannah Wizosky)  female  28.000000   \n",
       "875                   Najib, Miss. Adele Kiamie \"Jane\"  female  15.000000   \n",
       "876                      Gustafsson, Mr. Alfred Ossian    male  20.000000   \n",
       "877                               Petroff, Mr. Nedelio    male  19.000000   \n",
       "878                                 Laleff, Mr. Kristo    male  27.947206   \n",
       "879      Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)  female  56.000000   \n",
       "880       Shelley, Mrs. William (Imanita Parrish Hall)  female  25.000000   \n",
       "881                                 Markun, Mr. Johann    male  33.000000   \n",
       "882                       Dahlberg, Miss. Gerda Ulrika  female  22.000000   \n",
       "883                      Banfield, Mr. Frederick James    male  28.000000   \n",
       "884                             Sutehall, Mr. Henry Jr    male  25.000000   \n",
       "885               Rice, Mrs. William (Margaret Norton)  female  39.000000   \n",
       "886                              Montvila, Rev. Juozas    male  27.000000   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.000000   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female  16.193950   \n",
       "889                              Behr, Mr. Karl Howell    male  26.000000   \n",
       "890                                Dooley, Mr. Patrick    male  32.000000   \n",
       "\n",
       "     SibSp  Parch            Ticket      Fare Cabin Embarked  \n",
       "0        1      0         A/5 21171    7.2500    No        S  \n",
       "1        1      0          PC 17599   71.2833   Yes        C  \n",
       "2        0      0  STON/O2. 3101282    7.9250    No        S  \n",
       "3        1      0            113803   53.1000   Yes        S  \n",
       "4        0      0            373450    8.0500    No        S  \n",
       "5        0      0            330877    8.4583    No        Q  \n",
       "6        0      0             17463   51.8625   Yes        S  \n",
       "7        3      1            349909   21.0750    No        S  \n",
       "8        0      2            347742   11.1333    No        S  \n",
       "9        1      0            237736   30.0708    No        C  \n",
       "10       1      1           PP 9549   16.7000   Yes        S  \n",
       "11       0      0            113783   26.5500   Yes        S  \n",
       "12       0      0         A/5. 2151    8.0500    No        S  \n",
       "13       1      5            347082   31.2750    No        S  \n",
       "14       0      0            350406    7.8542    No        S  \n",
       "15       0      0            248706   16.0000    No        S  \n",
       "16       4      1            382652   29.1250    No        Q  \n",
       "17       0      0            244373   13.0000    No        S  \n",
       "18       1      0            345763   18.0000    No        S  \n",
       "19       0      0              2649    7.2250    No        C  \n",
       "20       0      0            239865   26.0000    No        S  \n",
       "21       0      0            248698   13.0000   Yes        S  \n",
       "22       0      0            330923    8.0292    No        Q  \n",
       "23       0      0            113788   35.5000   Yes        S  \n",
       "24       3      1            349909   21.0750    No        S  \n",
       "25       1      5            347077   31.3875    No        S  \n",
       "26       0      0              2631    7.2250    No        C  \n",
       "27       3      2             19950  263.0000   Yes        S  \n",
       "28       0      0            330959    7.8792    No        Q  \n",
       "29       0      0            349216    7.8958    No        S  \n",
       "..     ...    ...               ...       ...   ...      ...  \n",
       "861      1      0             28134   11.5000    No        S  \n",
       "862      0      0             17466   25.9292   Yes        S  \n",
       "863      8      2          CA. 2343   69.5500    No        S  \n",
       "864      0      0            233866   13.0000    No        S  \n",
       "865      0      0            236852   13.0000    No        S  \n",
       "866      1      0     SC/PARIS 2149   13.8583    No        C  \n",
       "867      0      0          PC 17590   50.4958   Yes        S  \n",
       "868      0      0            345777    9.5000    No        S  \n",
       "869      1      1            347742   11.1333    No        S  \n",
       "870      0      0            349248    7.8958    No        S  \n",
       "871      1      1             11751   52.5542   Yes        S  \n",
       "872      0      0               695    5.0000   Yes        S  \n",
       "873      0      0            345765    9.0000    No        S  \n",
       "874      1      0         P/PP 3381   24.0000    No        C  \n",
       "875      0      0              2667    7.2250    No        C  \n",
       "876      0      0              7534    9.8458    No        S  \n",
       "877      0      0            349212    7.8958    No        S  \n",
       "878      0      0            349217    7.8958    No        S  \n",
       "879      0      1             11767   83.1583   Yes        C  \n",
       "880      0      1            230433   26.0000    No        S  \n",
       "881      0      0            349257    7.8958    No        S  \n",
       "882      0      0              7552   10.5167    No        S  \n",
       "883      0      0  C.A./SOTON 34068   10.5000    No        S  \n",
       "884      0      0   SOTON/OQ 392076    7.0500    No        S  \n",
       "885      0      5            382652   29.1250    No        Q  \n",
       "886      0      0            211536   13.0000    No        S  \n",
       "887      0      0            112053   30.0000   Yes        S  \n",
       "888      1      2        W./C. 6607   23.4500    No        S  \n",
       "889      0      0            111369   30.0000   Yes        C  \n",
       "890      0      0            370376    7.7500    No        Q  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#我们发现Age这个属性也有177个乘客没有记录。\n",
    "#通常遇到缺值的情况，我们会有几种常见的处理方式\n",
    "#1. 如果缺值的样本占总数比例极高，我们可能就直接舍弃了，作为特征加入的话，可能反倒带入noise，影响最后的结果了\n",
    "#2. 如果缺值的样本适中，而该属性非连续值特征属性，那就把NaN作为一个类别，加到类别特征中\n",
    "#3. 如果缺值的样本适中，而该属性为连续值特征属性，有时候我们会考虑给定一个step(比如这里的age，我们可以考虑每隔2/3岁为一个步长)，然后把它离散化，之后把NaN作为一个type加到属性类目中\n",
    "#4. 有些情况下，缺失的值个数并不是特别多，那我们也可以试着根据已有的值，拟合一下数据，补充上\n",
    "#本例中，3和4应该都是可行的，我们试试4\n",
    "#我们这里用scikit-learn中的RandomForest来拟合一下缺失的年龄数据\n",
    "#注：RandomForest是一个用在原始数据中做不同采样，建立多颗DecisionTree，再进行vote取结果的机器学习算法，我们之后会介绍到\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    " \n",
    "### 使用 RandomForestClassifier 填补缺失的年龄属性\n",
    "def set_missing_ages(df):\n",
    "    \n",
    "    # 把已有的数值型特征取出来丢进Random Forest Regressor中\n",
    "    age_df = df[['Age','Fare', 'Parch', 'SibSp', 'Pclass']]\n",
    "\n",
    "    # 乘客分成已知年龄和未知年龄两部分\n",
    "    known_age = age_df[age_df.Age.notnull()].as_matrix()\n",
    "    unknown_age = age_df[age_df.Age.isnull()].as_matrix()\n",
    "\n",
    "    # y即目标年龄\n",
    "    y = known_age[:, 0]\n",
    "\n",
    "    # X即特征属性值\n",
    "    X = known_age[:, 1:]\n",
    "\n",
    "    # fit到RandomForestRegressor之中\n",
    "    rfr = RandomForestRegressor(random_state=0, n_estimators=2000, n_jobs=-1)\n",
    "    rfr.fit(X, y)\n",
    "    \n",
    "    # 用得到的模型进行未知年龄结果预测\n",
    "    predictedAges = rfr.predict(unknown_age[:, 1::])\n",
    "    \n",
    "    # 用得到的预测结果填补原缺失数据\n",
    "    df.loc[ (df.Age.isnull()), 'Age' ] = predictedAges \n",
    "    \n",
    "    return df, rfr\n",
    "\n",
    "def set_Cabin_type(df):\n",
    "    df.loc[ (df.Cabin.notnull()), 'Cabin' ] = \"Yes\"\n",
    "    df.loc[ (df.Cabin.isnull()), 'Cabin' ] = \"No\"\n",
    "    return df\n",
    "\n",
    "data_train, rfr = set_missing_ages(data_train)\n",
    "data_train = set_Cabin_type(data_train)\n",
    "data_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>23.838953</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
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       "      <td>54.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>16.7000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>31.2750</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8542</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>32.066493</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>29.518205</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0292</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35.5000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>31.3875</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>29.518205</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>263.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>22.380113</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8792</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>27.947206</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>861</th>\n",
       "      <td>862</td>\n",
       "      <td>0</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>11.5000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>862</th>\n",
       "      <td>863</td>\n",
       "      <td>1</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25.9292</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>863</th>\n",
       "      <td>864</td>\n",
       "      <td>0</td>\n",
       "      <td>10.869867</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>69.5500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>864</th>\n",
       "      <td>865</td>\n",
       "      <td>0</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>865</th>\n",
       "      <td>866</td>\n",
       "      <td>1</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>866</th>\n",
       "      <td>867</td>\n",
       "      <td>1</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>13.8583</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>867</th>\n",
       "      <td>868</td>\n",
       "      <td>0</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50.4958</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868</th>\n",
       "      <td>869</td>\n",
       "      <td>0</td>\n",
       "      <td>25.977889</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.5000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>869</th>\n",
       "      <td>870</td>\n",
       "      <td>1</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>870</th>\n",
       "      <td>871</td>\n",
       "      <td>0</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>871</th>\n",
       "      <td>872</td>\n",
       "      <td>1</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>52.5542</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>872</th>\n",
       "      <td>873</td>\n",
       "      <td>0</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>873</th>\n",
       "      <td>874</td>\n",
       "      <td>0</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>874</th>\n",
       "      <td>875</td>\n",
       "      <td>1</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>24.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>875</th>\n",
       "      <td>876</td>\n",
       "      <td>1</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>876</th>\n",
       "      <td>877</td>\n",
       "      <td>0</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.8458</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>877</th>\n",
       "      <td>878</td>\n",
       "      <td>0</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>878</th>\n",
       "      <td>879</td>\n",
       "      <td>0</td>\n",
       "      <td>27.947206</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>879</th>\n",
       "      <td>880</td>\n",
       "      <td>1</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>83.1583</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>880</th>\n",
       "      <td>881</td>\n",
       "      <td>1</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>881</th>\n",
       "      <td>882</td>\n",
       "      <td>0</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>883</td>\n",
       "      <td>0</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.5167</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>883</th>\n",
       "      <td>884</td>\n",
       "      <td>0</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>884</th>\n",
       "      <td>885</td>\n",
       "      <td>0</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>885</th>\n",
       "      <td>886</td>\n",
       "      <td>0</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>16.193950</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived        Age  SibSp  Parch      Fare  Cabin_No  \\\n",
       "0              1         0  22.000000      1      0    7.2500         1   \n",
       "1              2         1  38.000000      1      0   71.2833         0   \n",
       "2              3         1  26.000000      0      0    7.9250         1   \n",
       "3              4         1  35.000000      1      0   53.1000         0   \n",
       "4              5         0  35.000000      0      0    8.0500         1   \n",
       "5              6         0  23.838953      0      0    8.4583         1   \n",
       "6              7         0  54.000000      0      0   51.8625         0   \n",
       "7              8         0   2.000000      3      1   21.0750         1   \n",
       "8              9         1  27.000000      0      2   11.1333         1   \n",
       "9             10         1  14.000000      1      0   30.0708         1   \n",
       "10            11         1   4.000000      1      1   16.7000         0   \n",
       "11            12         1  58.000000      0      0   26.5500         0   \n",
       "12            13         0  20.000000      0      0    8.0500         1   \n",
       "13            14         0  39.000000      1      5   31.2750         1   \n",
       "14            15         0  14.000000      0      0    7.8542         1   \n",
       "15            16         1  55.000000      0      0   16.0000         1   \n",
       "16            17         0   2.000000      4      1   29.1250         1   \n",
       "17            18         1  32.066493      0      0   13.0000         1   \n",
       "18            19         0  31.000000      1      0   18.0000         1   \n",
       "19            20         1  29.518205      0      0    7.2250         1   \n",
       "20            21         0  35.000000      0      0   26.0000         1   \n",
       "21            22         1  34.000000      0      0   13.0000         0   \n",
       "22            23         1  15.000000      0      0    8.0292         1   \n",
       "23            24         1  28.000000      0      0   35.5000         0   \n",
       "24            25         0   8.000000      3      1   21.0750         1   \n",
       "25            26         1  38.000000      1      5   31.3875         1   \n",
       "26            27         0  29.518205      0      0    7.2250         1   \n",
       "27            28         0  19.000000      3      2  263.0000         0   \n",
       "28            29         1  22.380113      0      0    7.8792         1   \n",
       "29            30         0  27.947206      0      0    7.8958         1   \n",
       "..           ...       ...        ...    ...    ...       ...       ...   \n",
       "861          862         0  21.000000      1      0   11.5000         1   \n",
       "862          863         1  48.000000      0      0   25.9292         0   \n",
       "863          864         0  10.869867      8      2   69.5500         1   \n",
       "864          865         0  24.000000      0      0   13.0000         1   \n",
       "865          866         1  42.000000      0      0   13.0000         1   \n",
       "866          867         1  27.000000      1      0   13.8583         1   \n",
       "867          868         0  31.000000      0      0   50.4958         0   \n",
       "868          869         0  25.977889      0      0    9.5000         1   \n",
       "869          870         1   4.000000      1      1   11.1333         1   \n",
       "870          871         0  26.000000      0      0    7.8958         1   \n",
       "871          872         1  47.000000      1      1   52.5542         0   \n",
       "872          873         0  33.000000      0      0    5.0000         0   \n",
       "873          874         0  47.000000      0      0    9.0000         1   \n",
       "874          875         1  28.000000      1      0   24.0000         1   \n",
       "875          876         1  15.000000      0      0    7.2250         1   \n",
       "876          877         0  20.000000      0      0    9.8458         1   \n",
       "877          878         0  19.000000      0      0    7.8958         1   \n",
       "878          879         0  27.947206      0      0    7.8958         1   \n",
       "879          880         1  56.000000      0      1   83.1583         0   \n",
       "880          881         1  25.000000      0      1   26.0000         1   \n",
       "881          882         0  33.000000      0      0    7.8958         1   \n",
       "882          883         0  22.000000      0      0   10.5167         1   \n",
       "883          884         0  28.000000      0      0   10.5000         1   \n",
       "884          885         0  25.000000      0      0    7.0500         1   \n",
       "885          886         0  39.000000      0      5   29.1250         1   \n",
       "886          887         0  27.000000      0      0   13.0000         1   \n",
       "887          888         1  19.000000      0      0   30.0000         0   \n",
       "888          889         0  16.193950      1      2   23.4500         1   \n",
       "889          890         1  26.000000      0      0   30.0000         0   \n",
       "890          891         0  32.000000      0      0    7.7500         1   \n",
       "\n",
       "     Cabin_Yes  Embarked_C  Embarked_Q  Embarked_S  Sex_female  Sex_male  \\\n",
       "0            0           0           0           1           0         1   \n",
       "1            1           1           0           0           1         0   \n",
       "2            0           0           0           1           1         0   \n",
       "3            1           0           0           1           1         0   \n",
       "4            0           0           0           1           0         1   \n",
       "5            0           0           1           0           0         1   \n",
       "6            1           0           0           1           0         1   \n",
       "7            0           0           0           1           0         1   \n",
       "8            0           0           0           1           1         0   \n",
       "9            0           1           0           0           1         0   \n",
       "10           1           0           0           1           1         0   \n",
       "11           1           0           0           1           1         0   \n",
       "12           0           0           0           1           0         1   \n",
       "13           0           0           0           1           0         1   \n",
       "14           0           0           0           1           1         0   \n",
       "15           0           0           0           1           1         0   \n",
       "16           0           0           1           0           0         1   \n",
       "17           0           0           0           1           0         1   \n",
       "18           0           0           0           1           1         0   \n",
       "19           0           1           0           0           1         0   \n",
       "20           0           0           0           1           0         1   \n",
       "21           1           0           0           1           0         1   \n",
       "22           0           0           1           0           1         0   \n",
       "23           1           0           0           1           0         1   \n",
       "24           0           0           0           1           1         0   \n",
       "25           0           0           0           1           1         0   \n",
       "26           0           1           0           0           0         1   \n",
       "27           1           0           0           1           0         1   \n",
       "28           0           0           1           0           1         0   \n",
       "29           0           0           0           1           0         1   \n",
       "..         ...         ...         ...         ...         ...       ...   \n",
       "861          0           0           0           1           0         1   \n",
       "862          1           0           0           1           1         0   \n",
       "863          0           0           0           1           1         0   \n",
       "864          0           0           0           1           0         1   \n",
       "865          0           0           0           1           1         0   \n",
       "866          0           1           0           0           1         0   \n",
       "867          1           0           0           1           0         1   \n",
       "868          0           0           0           1           0         1   \n",
       "869          0           0           0           1           0         1   \n",
       "870          0           0           0           1           0         1   \n",
       "871          1           0           0           1           1         0   \n",
       "872          1           0           0           1           0         1   \n",
       "873          0           0           0           1           0         1   \n",
       "874          0           1           0           0           1         0   \n",
       "875          0           1           0           0           1         0   \n",
       "876          0           0           0           1           0         1   \n",
       "877          0           0           0           1           0         1   \n",
       "878          0           0           0           1           0         1   \n",
       "879          1           1           0           0           1         0   \n",
       "880          0           0           0           1           1         0   \n",
       "881          0           0           0           1           0         1   \n",
       "882          0           0           0           1           1         0   \n",
       "883          0           0           0           1           0         1   \n",
       "884          0           0           0           1           0         1   \n",
       "885          0           0           1           0           1         0   \n",
       "886          0           0           0           1           0         1   \n",
       "887          1           0           0           1           1         0   \n",
       "888          0           0           0           1           1         0   \n",
       "889          1           1           0           0           0         1   \n",
       "890          0           0           1           0           0         1   \n",
       "\n",
       "     Pclass_1  Pclass_2  Pclass_3  \n",
       "0           0         0         1  \n",
       "1           1         0         0  \n",
       "2           0         0         1  \n",
       "3           1         0         0  \n",
       "4           0         0         1  \n",
       "5           0         0         1  \n",
       "6           1         0         0  \n",
       "7           0         0         1  \n",
       "8           0         0         1  \n",
       "9           0         1         0  \n",
       "10          0         0         1  \n",
       "11          1         0         0  \n",
       "12          0         0         1  \n",
       "13          0         0         1  \n",
       "14          0         0         1  \n",
       "15          0         1         0  \n",
       "16          0         0         1  \n",
       "17          0         1         0  \n",
       "18          0         0         1  \n",
       "19          0         0         1  \n",
       "20          0         1         0  \n",
       "21          0         1         0  \n",
       "22          0         0         1  \n",
       "23          1         0         0  \n",
       "24          0         0         1  \n",
       "25          0         0         1  \n",
       "26          0         0         1  \n",
       "27          1         0         0  \n",
       "28          0         0         1  \n",
       "29          0         0         1  \n",
       "..        ...       ...       ...  \n",
       "861         0         1         0  \n",
       "862         1         0         0  \n",
       "863         0         0         1  \n",
       "864         0         1         0  \n",
       "865         0         1         0  \n",
       "866         0         1         0  \n",
       "867         1         0         0  \n",
       "868         0         0         1  \n",
       "869         0         0         1  \n",
       "870         0         0         1  \n",
       "871         1         0         0  \n",
       "872         1         0         0  \n",
       "873         0         0         1  \n",
       "874         0         1         0  \n",
       "875         0         0         1  \n",
       "876         0         0         1  \n",
       "877         0         0         1  \n",
       "878         0         0         1  \n",
       "879         1         0         0  \n",
       "880         0         1         0  \n",
       "881         0         0         1  \n",
       "882         0         0         1  \n",
       "883         0         1         0  \n",
       "884         0         0         1  \n",
       "885         0         0         1  \n",
       "886         0         1         0  \n",
       "887         1         0         0  \n",
       "888         0         0         1  \n",
       "889         1         0         0  \n",
       "890         0         0         1  \n",
       "\n",
       "[891 rows x 16 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 因为逻辑回归建模时，需要输入的特征都是数值型特征\n",
    "# 我们先对类目型的特征离散/因子化\n",
    "# 以Cabin为例，原本一个属性维度，因为其取值可以是['yes','no']，而将其平展开为'Cabin_yes','Cabin_no'两个属性\n",
    "# 原本Cabin取值为yes的，在此处的'Cabin_yes'下取值为1，在'Cabin_no'下取值为0\n",
    "# 原本Cabin取值为no的，在此处的'Cabin_yes'下取值为0，在'Cabin_no'下取值为1\n",
    "# 我们使用pandas的get_dummies来完成这个工作，并拼接在原来的data_train之上，如下所示\n",
    "dummies_Cabin = pd.get_dummies(data_train['Cabin'], prefix= 'Cabin')\n",
    "\n",
    "dummies_Embarked = pd.get_dummies(data_train['Embarked'], prefix= 'Embarked')\n",
    "\n",
    "dummies_Sex = pd.get_dummies(data_train['Sex'], prefix= 'Sex')\n",
    "\n",
    "dummies_Pclass = pd.get_dummies(data_train['Pclass'], prefix= 'Pclass')\n",
    "\n",
    "df = pd.concat([data_train, dummies_Cabin, dummies_Embarked, dummies_Sex, dummies_Pclass], axis=1)\n",
    "df.drop(['Pclass', 'Name', 'Sex', 'Ticket', 'Cabin', 'Embarked'], axis=1, inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Expected 2D array, got 1D array instead:\narray=[22.         38.         26.         35.         35.         23.83895259\n 54.          2.         27.         14.          4.         58.\n 20.         39.         14.         55.          2.         32.06649305\n 31.         29.51820514 35.         34.         15.         28.\n  8.         38.         29.51820514 19.         22.38011324 27.94720616\n 40.         36.10804822 35.2958243  66.         28.         42.\n 22.87630686 21.         18.         14.         40.         27.\n 27.94720616  3.         19.         30.70572678 33.12898535 35.2958243\n 23.45968333 18.          7.         21.         49.         29.\n 65.         44.06483036 21.         28.5         5.         11.\n 22.         38.         45.          4.         41.20008848 17.09991595\n 29.         19.         17.         26.         32.         16.\n 21.         26.         32.         25.         27.94720616 30.70572678\n  0.83       30.         22.         29.         23.32262739 28.\n 17.         33.         16.         30.70572678 23.         24.\n 29.         20.         46.         26.         59.         30.70572678\n 71.         23.         34.         34.         28.         27.94720616\n 21.         33.         37.         28.         21.         27.51545426\n 38.         33.55117591 47.         14.5        22.         20.\n 17.         21.         70.5        29.         24.          2.\n 21.         30.70572678 32.5        32.5        54.         12.\n 35.2958243  24.         25.78337698 45.         33.         20.\n 47.         29.         25.         23.         19.         37.\n 16.         24.         25.34409583 22.         24.         19.\n 18.         19.         27.          9.         36.5        42.\n 51.         22.         55.5        40.5        29.78279613 51.\n 16.         30.         25.52340334 10.86986696 44.         40.\n 26.         17.          1.          9.         26.03188214 45.\n 49.5542756  28.         61.          4.          1.         21.\n 56.         18.          7.30954704 50.         30.         36.\n 10.86986696 31.71894048  9.          1.          4.         46.24976824\n 33.12898535 45.         40.         36.         32.         19.\n 19.          3.         44.         58.         35.2958243  42.\n 35.2958243  24.         28.         10.86986696 34.         45.5\n 18.          2.         32.         26.         16.         40.\n 24.         35.         22.         30.         31.09342985 31.\n 27.         42.         32.         30.         16.         27.\n 51.         27.94720616 38.         22.         19.         20.5\n 18.          7.30954704 35.         29.         59.          5.\n 24.         31.10838452 44.          8.         19.         33.\n 20.80015413 33.12898535 29.         22.         30.         44.\n 25.         24.         37.         54.         29.78279613 29.\n 62.         30.         41.         29.         34.62028571 30.\n 35.         50.         35.2958243   3.         52.         40.\n 35.2958243  36.         16.         25.         58.         35.\n 36.87489821 25.         41.         37.         35.2958243  63.\n 45.         35.05181757  7.         35.         65.         28.\n 16.         19.         57.74249226 33.         30.         22.\n 42.         22.         26.         19.         36.         24.\n 24.         41.20008848 23.5         2.         41.57487718 50.\n 35.2958243  23.31368333 19.         42.57451554 30.70572678  0.92\n 28.57888393 17.         30.         30.         24.         18.\n 26.         28.         43.         26.         24.         54.\n 31.         40.         22.         27.         30.         22.\n 10.86986696 36.         61.         36.         31.         16.\n 23.31368333 45.5        38.         16.         31.42587794 27.94720616\n 29.         41.         45.         45.          2.         24.\n 28.         25.         36.         24.         40.         26.68916849\n  3.         42.         23.         59.96916448 15.         25.\n 29.51820514 28.         22.         38.         22.38011324 22.38011324\n 40.         29.         45.         35.         33.12898535 30.\n 60.         22.87630686 35.2958243  24.         25.         18.\n 19.         22.          3.         31.94479345 22.         27.\n 20.         19.         42.          1.         32.         35.\n 27.94720616 18.          1.         36.         19.89558113 17.\n 36.         21.         28.         23.         24.         22.\n 31.         46.         23.         28.         39.         26.\n 21.         28.         20.         34.         51.          3.\n 21.          7.30954704 27.94720616 20.68131488 33.         35.05181757\n 44.         30.70572678 34.         18.         30.         10.\n 27.94720616 21.         29.         28.         18.         29.78279613\n 28.         19.         35.2958243  32.         28.         26.68916849\n 42.         17.         50.         14.         21.         24.\n 64.         31.         45.         20.         25.         28.\n 23.47643239  4.         13.         34.          5.         52.\n 36.         24.10899881 30.         49.         30.70572678 29.\n 65.         44.06001827 50.         35.2958243  48.         34.\n 47.         48.         30.70572678 38.         35.05181757 56.\n 19.89558113  0.75       29.78279613 38.         33.         23.\n 22.         44.65953056 34.         29.         22.          2.\n  9.         35.05181757 50.         63.         25.          7.30954704\n 35.         58.         30.          9.         24.10899881 21.\n 55.         71.         21.         18.19479484 54.         25.7554753\n 25.         24.         17.         21.         26.82073281 37.\n 16.         18.         33.         49.8994093  28.         26.\n 29.         30.70572678 36.         54.         24.         47.\n 34.         34.17374861 36.         32.         30.         22.\n 29.51820514 44.         22.87630686 40.5        50.         38.5155\n 39.         23.          2.         22.87630686 17.          8.01370298\n 30.          7.         45.         30.         24.33021696 22.\n 36.          9.         11.         32.         50.         64.\n 19.         32.79502428 33.          8.         17.         27.\n 26.3814249  22.         22.         62.         48.         38.5155\n 39.         36.         35.2958243  40.         28.         30.70572678\n 30.70572678 24.         19.         29.         22.87630686 32.\n 62.         53.         36.         35.2958243  16.         19.\n 34.         39.         20.07016773 32.         25.         39.\n 54.         36.         29.04293865 18.         47.         60.\n 22.         30.70572678 35.         52.         47.         27.45639428\n 37.         36.         24.40235514 49.         29.51820514 49.\n 24.         27.94720616 37.51615833 44.         35.         36.\n 30.         27.         22.         40.         39.         27.86873699\n 33.12898535 35.2958243  35.         24.         34.         26.\n  4.         26.         27.         42.         20.         21.\n 21.         61.         57.         21.         26.         19.89558113\n 80.         51.         32.         38.83477484  9.         28.\n 32.         31.         41.         26.68916849 20.         24.\n  2.         29.56889809  0.75       48.         19.         56.\n 31.10838452 23.         27.94720616 18.         21.         26.3814249\n 18.         24.         27.94720616 32.         23.         58.\n 50.         40.         47.         36.         20.         32.\n 25.         27.51545426 43.         39.08817814 40.         31.\n 70.         31.         35.05181757 18.         24.5        18.\n 43.         36.         23.47643239 27.         20.         14.\n 60.         25.         14.         19.         18.         15.\n 31.          4.         29.56889809 25.         60.         52.\n 44.         19.89558113 49.         42.         18.         35.\n 18.         25.         26.         39.         45.         42.\n 22.         17.09991595 24.         49.8994093  48.         29.\n 52.         19.         38.         27.         27.30887391 33.\n  6.         17.         34.         50.         27.         20.\n 30.         19.89558113 25.         25.         29.         11.\n 35.05181757 23.         23.         28.5        48.         35.\n 27.94720616 27.94720616 38.42663175 36.         21.         24.\n 31.         70.         16.         30.         19.         31.\n  4.          6.         33.         23.         48.          0.67\n 28.         18.         34.         33.         24.33021696 41.\n 20.         36.         16.         51.         39.17490238 30.5\n 33.55117591 32.         24.         48.         57.         29.51820514\n 54.         18.         35.2958243   5.         19.89558113 43.\n 13.         17.         29.         16.1939502  25.         25.\n 18.          8.          1.         46.         35.2958243  16.\n 10.86986696 50.38328427 25.         39.         49.         31.\n 30.         30.         34.         31.         11.          0.42\n 27.         31.         39.         18.         39.         33.\n 26.         39.         35.          6.         30.5        38.83477484\n 23.         31.         43.         10.         52.         27.\n 38.         27.          2.         35.09787898 29.56889809  1.\n 35.2958243  62.         15.          0.83       22.87630686 23.\n 18.         39.         21.         30.70572678 32.         50.91095013\n 20.         16.         30.         34.5        17.         42.\n 10.86986696 35.         28.         43.96476448  4.         74.\n  9.         16.         44.         18.         45.         51.\n 24.         22.87630686 41.         21.         48.         10.86986696\n 24.         42.         27.         31.         25.97788916  4.\n 26.         47.         33.         47.         28.         15.\n 20.         19.         27.94720616 56.         25.         33.\n 22.         28.         25.         39.         27.         19.\n 16.1939502  26.         32.        ].\nReshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-58-24b6b3e8bee5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpreprocessing\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpreprocessing\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mscaler\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpreprocessing\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mStandardScaler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mage_scale_param\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscaler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Age'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Age_scaled'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscaler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Age'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mage_scale_param\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mfare_scale_param\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscaler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Fare'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sklearn/preprocessing/data.pyc\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m    588\u001b[0m         \u001b[0;31m# Reset internal state before fitting\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    589\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 590\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpartial_fit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    591\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    592\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mpartial_fit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sklearn/preprocessing/data.pyc\u001b[0m in \u001b[0;36mpartial_fit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m    610\u001b[0m         \"\"\"\n\u001b[1;32m    611\u001b[0m         X = check_array(X, accept_sparse=('csr', 'csc'), copy=self.copy,\n\u001b[0;32m--> 612\u001b[0;31m                         warn_on_dtype=True, estimator=self, dtype=FLOAT_DTYPES)\n\u001b[0m\u001b[1;32m    613\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    614\u001b[0m         \u001b[0;31m# Even in the case of `with_mean=False`, we update the mean anyway\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sklearn/utils/validation.pyc\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    439\u001b[0m                     \u001b[0;34m\"Reshape your data either using array.reshape(-1, 1) if \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    440\u001b[0m                     \u001b[0;34m\"your data has a single feature or array.reshape(1, -1) \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 441\u001b[0;31m                     \"if it contains a single sample.\".format(array))\n\u001b[0m\u001b[1;32m    442\u001b[0m             \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0matleast_2d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    443\u001b[0m             \u001b[0;31m# To ensure that array flags are maintained\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Expected 2D array, got 1D array instead:\narray=[22.         38.         26.         35.         35.         23.83895259\n 54.          2.         27.         14.          4.         58.\n 20.         39.         14.         55.          2.         32.06649305\n 31.         29.51820514 35.         34.         15.         28.\n  8.         38.         29.51820514 19.         22.38011324 27.94720616\n 40.         36.10804822 35.2958243  66.         28.         42.\n 22.87630686 21.         18.         14.         40.         27.\n 27.94720616  3.         19.         30.70572678 33.12898535 35.2958243\n 23.45968333 18.          7.         21.         49.         29.\n 65.         44.06483036 21.         28.5         5.         11.\n 22.         38.         45.          4.         41.20008848 17.09991595\n 29.         19.         17.         26.         32.         16.\n 21.         26.         32.         25.         27.94720616 30.70572678\n  0.83       30.         22.         29.         23.32262739 28.\n 17.         33.         16.         30.70572678 23.         24.\n 29.         20.         46.         26.         59.         30.70572678\n 71.         23.         34.         34.         28.         27.94720616\n 21.         33.         37.         28.         21.         27.51545426\n 38.         33.55117591 47.         14.5        22.         20.\n 17.         21.         70.5        29.         24.          2.\n 21.         30.70572678 32.5        32.5        54.         12.\n 35.2958243  24.         25.78337698 45.         33.         20.\n 47.         29.         25.         23.         19.         37.\n 16.         24.         25.34409583 22.         24.         19.\n 18.         19.         27.          9.         36.5        42.\n 51.         22.         55.5        40.5        29.78279613 51.\n 16.         30.         25.52340334 10.86986696 44.         40.\n 26.         17.          1.          9.         26.03188214 45.\n 49.5542756  28.         61.          4.          1.         21.\n 56.         18.          7.30954704 50.         30.         36.\n 10.86986696 31.71894048  9.          1.          4.         46.24976824\n 33.12898535 45.         40.         36.         32.         19.\n 19.          3.         44.         58.         35.2958243  42.\n 35.2958243  24.         28.         10.86986696 34.         45.5\n 18.          2.         32.         26.         16.         40.\n 24.         35.         22.         30.         31.09342985 31.\n 27.         42.         32.         30.         16.         27.\n 51.         27.94720616 38.         22.         19.         20.5\n 18.          7.30954704 35.         29.         59.          5.\n 24.         31.10838452 44.          8.         19.         33.\n 20.80015413 33.12898535 29.         22.         30.         44.\n 25.         24.         37.         54.         29.78279613 29.\n 62.         30.         41.         29.         34.62028571 30.\n 35.         50.         35.2958243   3.         52.         40.\n 35.2958243  36.         16.         25.         58.         35.\n 36.87489821 25.         41.         37.         35.2958243  63.\n 45.         35.05181757  7.         35.         65.         28.\n 16.         19.         57.74249226 33.         30.         22.\n 42.         22.         26.         19.         36.         24.\n 24.         41.20008848 23.5         2.         41.57487718 50.\n 35.2958243  23.31368333 19.         42.57451554 30.70572678  0.92\n 28.57888393 17.         30.         30.         24.         18.\n 26.         28.         43.         26.         24.         54.\n 31.         40.         22.         27.         30.         22.\n 10.86986696 36.         61.         36.         31.         16.\n 23.31368333 45.5        38.         16.         31.42587794 27.94720616\n 29.         41.         45.         45.          2.         24.\n 28.         25.         36.         24.         40.         26.68916849\n  3.         42.         23.         59.96916448 15.         25.\n 29.51820514 28.         22.         38.         22.38011324 22.38011324\n 40.         29.         45.         35.         33.12898535 30.\n 60.         22.87630686 35.2958243  24.         25.         18.\n 19.         22.          3.         31.94479345 22.         27.\n 20.         19.         42.          1.         32.         35.\n 27.94720616 18.          1.         36.         19.89558113 17.\n 36.         21.         28.         23.         24.         22.\n 31.         46.         23.         28.         39.         26.\n 21.         28.         20.         34.         51.          3.\n 21.          7.30954704 27.94720616 20.68131488 33.         35.05181757\n 44.         30.70572678 34.         18.         30.         10.\n 27.94720616 21.         29.         28.         18.         29.78279613\n 28.         19.         35.2958243  32.         28.         26.68916849\n 42.         17.         50.         14.         21.         24.\n 64.         31.         45.         20.         25.         28.\n 23.47643239  4.         13.         34.          5.         52.\n 36.         24.10899881 30.         49.         30.70572678 29.\n 65.         44.06001827 50.         35.2958243  48.         34.\n 47.         48.         30.70572678 38.         35.05181757 56.\n 19.89558113  0.75       29.78279613 38.         33.         23.\n 22.         44.65953056 34.         29.         22.          2.\n  9.         35.05181757 50.         63.         25.          7.30954704\n 35.         58.         30.          9.         24.10899881 21.\n 55.         71.         21.         18.19479484 54.         25.7554753\n 25.         24.         17.         21.         26.82073281 37.\n 16.         18.         33.         49.8994093  28.         26.\n 29.         30.70572678 36.         54.         24.         47.\n 34.         34.17374861 36.         32.         30.         22.\n 29.51820514 44.         22.87630686 40.5        50.         38.5155\n 39.         23.          2.         22.87630686 17.          8.01370298\n 30.          7.         45.         30.         24.33021696 22.\n 36.          9.         11.         32.         50.         64.\n 19.         32.79502428 33.          8.         17.         27.\n 26.3814249  22.         22.         62.         48.         38.5155\n 39.         36.         35.2958243  40.         28.         30.70572678\n 30.70572678 24.         19.         29.         22.87630686 32.\n 62.         53.         36.         35.2958243  16.         19.\n 34.         39.         20.07016773 32.         25.         39.\n 54.         36.         29.04293865 18.         47.         60.\n 22.         30.70572678 35.         52.         47.         27.45639428\n 37.         36.         24.40235514 49.         29.51820514 49.\n 24.         27.94720616 37.51615833 44.         35.         36.\n 30.         27.         22.         40.         39.         27.86873699\n 33.12898535 35.2958243  35.         24.         34.         26.\n  4.         26.         27.         42.         20.         21.\n 21.         61.         57.         21.         26.         19.89558113\n 80.         51.         32.         38.83477484  9.         28.\n 32.         31.         41.         26.68916849 20.         24.\n  2.         29.56889809  0.75       48.         19.         56.\n 31.10838452 23.         27.94720616 18.         21.         26.3814249\n 18.         24.         27.94720616 32.         23.         58.\n 50.         40.         47.         36.         20.         32.\n 25.         27.51545426 43.         39.08817814 40.         31.\n 70.         31.         35.05181757 18.         24.5        18.\n 43.         36.         23.47643239 27.         20.         14.\n 60.         25.         14.         19.         18.         15.\n 31.          4.         29.56889809 25.         60.         52.\n 44.         19.89558113 49.         42.         18.         35.\n 18.         25.         26.         39.         45.         42.\n 22.         17.09991595 24.         49.8994093  48.         29.\n 52.         19.         38.         27.         27.30887391 33.\n  6.         17.         34.         50.         27.         20.\n 30.         19.89558113 25.         25.         29.         11.\n 35.05181757 23.         23.         28.5        48.         35.\n 27.94720616 27.94720616 38.42663175 36.         21.         24.\n 31.         70.         16.         30.         19.         31.\n  4.          6.         33.         23.         48.          0.67\n 28.         18.         34.         33.         24.33021696 41.\n 20.         36.         16.         51.         39.17490238 30.5\n 33.55117591 32.         24.         48.         57.         29.51820514\n 54.         18.         35.2958243   5.         19.89558113 43.\n 13.         17.         29.         16.1939502  25.         25.\n 18.          8.          1.         46.         35.2958243  16.\n 10.86986696 50.38328427 25.         39.         49.         31.\n 30.         30.         34.         31.         11.          0.42\n 27.         31.         39.         18.         39.         33.\n 26.         39.         35.          6.         30.5        38.83477484\n 23.         31.         43.         10.         52.         27.\n 38.         27.          2.         35.09787898 29.56889809  1.\n 35.2958243  62.         15.          0.83       22.87630686 23.\n 18.         39.         21.         30.70572678 32.         50.91095013\n 20.         16.         30.         34.5        17.         42.\n 10.86986696 35.         28.         43.96476448  4.         74.\n  9.         16.         44.         18.         45.         51.\n 24.         22.87630686 41.         21.         48.         10.86986696\n 24.         42.         27.         31.         25.97788916  4.\n 26.         47.         33.         47.         28.         15.\n 20.         19.         27.94720616 56.         25.         33.\n 22.         28.         25.         39.         27.         19.\n 16.1939502  26.         32.        ].\nReshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample."
     ]
    }
   ],
   "source": [
    "# 接下来我们要接着做一些数据预处理的工作，比如scaling，将一些变化幅度较大的特征化到[-1,1]之内\n",
    "# 这样可以加速logistic regression的收敛\n",
    "import sklearn.preprocessing as preprocessing\n",
    "scaler = preprocessing.StandardScaler()\n",
    "age_scale_param = scaler.fit(df['Age'])\n",
    "df['Age_scaled'] = scaler.fit_transform(df['Age'], age_scale_param)\n",
    "fare_scale_param = scaler.fit(df['Fare'])\n",
    "df['Fare_scaled'] = scaler.fit_transform(df['Fare'], fare_scale_param)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l1', random_state=None, solver='liblinear', tol=1e-06,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 我们把需要的feature字段取出来，转成numpy格式，使用scikit-learn中的LogisticRegression建模\n",
    "from sklearn import linear_model\n",
    "\n",
    "train_df = df.filter(regex='Survived|Age_.*|SibSp|Parch|Fare_.*|Cabin_.*|Embarked_.*|Sex_.*|Pclass_.*')\n",
    "train_np = train_df.as_matrix()\n",
    "\n",
    "# y即Survival结果\n",
    "y = train_np[:, 0]\n",
    "\n",
    "# X即特征属性值\n",
    "X = train_np[:, 1:]\n",
    "\n",
    "# fit到RandomForestRegressor之中\n",
    "clf = linear_model.LogisticRegression(C=1.0, penalty='l1', tol=1e-6)\n",
    "clf.fit(X, y)\n",
    "    \n",
    "clf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 12)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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DZpylQHNzM4sXLy7oZ/j9oj7z+UZOfZbN/RopHNVSJBIXQm63vHdWGNl+L5n6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\nQFEUpcxRQaAoilLm/H8zQKLfvX7HgQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c2b06d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.8015093034017282, 0.03376943718866787)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.learning_curve import learning_curve\n",
    "\n",
    "# 用sklearn的learning_curve得到training_score和cv_score，使用matplotlib画出learning curve\n",
    "def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None, n_jobs=1, \n",
    "                        train_sizes=np.linspace(.05, 1., 20), verbose=0, plot=True):\n",
    "    \"\"\"\n",
    "    画出data在某模型上的learning curve.\n",
    "    参数解释\n",
    "    ----------\n",
    "    estimator : 你用的分类器。\n",
    "    title : 表格的标题。\n",
    "    X : 输入的feature，numpy类型\n",
    "    y : 输入的target vector\n",
    "    ylim : tuple格式的(ymin, ymax), 设定图像中纵坐标的最低点和最高点\n",
    "    cv : 做cross-validation的时候，数据分成的份数，其中一份作为cv集，其余n-1份作为training(默认为3份)\n",
    "    n_jobs : 并行的的任务数(默认1)\n",
    "    \"\"\"\n",
    "    train_sizes, train_scores, test_scores = learning_curve(\n",
    "        estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes, verbose=verbose)\n",
    "    \n",
    "    train_scores_mean = np.mean(train_scores, axis=1)\n",
    "    train_scores_std = np.std(train_scores, axis=1)\n",
    "    test_scores_mean = np.mean(test_scores, axis=1)\n",
    "    test_scores_std = np.std(test_scores, axis=1)\n",
    "    \n",
    "    if plot:\n",
    "        plt.figure()\n",
    "        plt.title(title)\n",
    "        if ylim is not None:\n",
    "            plt.ylim(*ylim)\n",
    "        plt.xlabel(u\"训练样本数\")\n",
    "        plt.ylabel(u\"得分\")\n",
    "        plt.gca().invert_yaxis()\n",
    "        plt.grid()\n",
    "    \n",
    "        plt.fill_between(train_sizes, train_scores_mean - train_scores_std, train_scores_mean + train_scores_std, \n",
    "                         alpha=0.1, color=\"b\")\n",
    "        plt.fill_between(train_sizes, test_scores_mean - test_scores_std, test_scores_mean + test_scores_std, \n",
    "                         alpha=0.1, color=\"r\")\n",
    "        plt.plot(train_sizes, train_scores_mean, 'o-', color=\"b\", label=u\"训练集上得分\")\n",
    "        plt.plot(train_sizes, test_scores_mean, 'o-', color=\"r\", label=u\"交叉验证集上得分\")\n",
    "    \n",
    "        plt.legend(loc=\"best\")\n",
    "        \n",
    "        plt.draw()\n",
    "        plt.gca().invert_yaxis()\n",
    "        plt.show()\n",
    "    \n",
    "    midpoint = ((train_scores_mean[-1] + train_scores_std[-1]) + (test_scores_mean[-1] - test_scores_std[-1])) / 2\n",
    "    diff = (train_scores_mean[-1] + train_scores_std[-1]) - (test_scores_mean[-1] - test_scores_std[-1])\n",
    "    return midpoint, diff\n",
    "\n",
    "plot_learning_curve(clf, u\"学习曲线\", X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'age_scale_param' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-59-f9b8d3215f69>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0mdf_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdata_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdummies_Cabin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdummies_Embarked\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdummies_Sex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdummies_Pclass\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0mdf_test\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Pclass'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Name'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Sex'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Ticket'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Cabin'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Embarked'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m \u001b[0mdf_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Age_scaled'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscaler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Age'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mage_scale_param\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     22\u001b[0m \u001b[0mdf_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Fare_scaled'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscaler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Fare'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfare_scale_param\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0mdf_test\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'age_scale_param' is not defined"
     ]
    }
   ],
   "source": [
    "data_test = pd.read_csv(\"/Users/yumh/project/kaggle/kaggle_titanic/test.csv\")\n",
    "data_test.loc[ (data_test.Fare.isnull()), 'Fare' ] = 0\n",
    "# 接着我们对test_data做和train_data中一致的特征变换\n",
    "# 首先用同样的RandomForestRegressor模型填上丢失的年龄\n",
    "tmp_df = data_test[['Age','Fare', 'Parch', 'SibSp', 'Pclass']]\n",
    "null_age = tmp_df[data_test.Age.isnull()].as_matrix()\n",
    "# 根据特征属性X预测年龄并补上\n",
    "X = null_age[:, 1:]\n",
    "predictedAges = rfr.predict(X)\n",
    "data_test.loc[ (data_test.Age.isnull()), 'Age' ] = predictedAges\n",
    "\n",
    "data_test = set_Cabin_type(data_test)\n",
    "dummies_Cabin = pd.get_dummies(data_test['Cabin'], prefix= 'Cabin')\n",
    "dummies_Embarked = pd.get_dummies(data_test['Embarked'], prefix= 'Embarked')\n",
    "dummies_Sex = pd.get_dummies(data_test['Sex'], prefix= 'Sex')\n",
    "dummies_Pclass = pd.get_dummies(data_test['Pclass'], prefix= 'Pclass')\n",
    "\n",
    "\n",
    "df_test = pd.concat([data_test, dummies_Cabin, dummies_Embarked, dummies_Sex, dummies_Pclass], axis=1)\n",
    "df_test.drop(['Pclass', 'Name', 'Sex', 'Ticket', 'Cabin', 'Embarked'], axis=1, inplace=True)\n",
    "df_test['Age_scaled'] = scaler.fit_transform(df_test['Age'], age_scale_param)\n",
    "df_test['Fare_scaled'] = scaler.fit_transform(df_test['Fare'], fare_scale_param)\n",
    "df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = df_test.filter(regex='Age_.*|SibSp|Parch|Fare_.*|Cabin_.*|Embarked_.*|Sex_.*|Pclass_.*')\n",
    "predictions = clf.predict(test)\n",
    "result = pd.DataFrame({'PassengerId':data_test['PassengerId'].as_matrix(), 'Survived':predictions.astype(np.int32)})\n",
    "result.to_csv(\"/Users/MLS/Downloads/logistic_regression_predictions.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 515,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>907</td>\n",
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       "      <td>914</td>\n",
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       "      <th>23</th>\n",
       "      <td>915</td>\n",
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       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>916</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>917</td>\n",
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       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>918</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>919</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>920</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>921</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>388</th>\n",
       "      <td>1280</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>389</th>\n",
       "      <td>1281</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>1282</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>1283</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>392</th>\n",
       "      <td>1284</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>1285</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>1286</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>1287</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>1288</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>1289</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>1290</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>1291</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>1292</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>401</th>\n",
       "      <td>1293</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>1294</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>1295</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>1296</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405</th>\n",
       "      <td>1297</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>1298</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>1299</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>1300</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>409</th>\n",
       "      <td>1301</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>1302</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>411</th>\n",
       "      <td>1303</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>1304</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>1305</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>1306</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>1307</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>1308</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>1309</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived\n",
       "0            892         0\n",
       "1            893         0\n",
       "2            894         0\n",
       "3            895         0\n",
       "4            896         1\n",
       "5            897         0\n",
       "6            898         1\n",
       "7            899         0\n",
       "8            900         1\n",
       "9            901         0\n",
       "10           902         0\n",
       "11           903         0\n",
       "12           904         1\n",
       "13           905         0\n",
       "14           906         1\n",
       "15           907         1\n",
       "16           908         0\n",
       "17           909         0\n",
       "18           910         1\n",
       "19           911         1\n",
       "20           912         0\n",
       "21           913         0\n",
       "22           914         1\n",
       "23           915         0\n",
       "24           916         1\n",
       "25           917         0\n",
       "26           918         1\n",
       "27           919         0\n",
       "28           920         0\n",
       "29           921         0\n",
       "..           ...       ...\n",
       "388         1280         0\n",
       "389         1281         0\n",
       "390         1282         1\n",
       "391         1283         1\n",
       "392         1284         0\n",
       "393         1285         0\n",
       "394         1286         0\n",
       "395         1287         1\n",
       "396         1288         0\n",
       "397         1289         1\n",
       "398         1290         0\n",
       "399         1291         0\n",
       "400         1292         1\n",
       "401         1293         0\n",
       "402         1294         1\n",
       "403         1295         0\n",
       "404         1296         0\n",
       "405         1297         1\n",
       "406         1298         0\n",
       "407         1299         0\n",
       "408         1300         1\n",
       "409         1301         1\n",
       "410         1302         1\n",
       "411         1303         1\n",
       "412         1304         1\n",
       "413         1305         0\n",
       "414         1306         1\n",
       "415         1307         0\n",
       "416         1308         0\n",
       "417         1309         0\n",
       "\n",
       "[418 rows x 2 columns]"
      ]
     },
     "execution_count": 515,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv(\"/Users/MLS/Downloads/logistic_regression_predictions.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 0.76555，恩，结果还不错。毕竟，这只是我们简单分析过后出的一个baseline系统嘛\n",
    "# Andrew Ng老师说的第一步到此我们就做完了，你以为故事到这里就结束了吗？图样图森破啊，这只是第一步好咩！！接下来，我们就该看看如何优化baseline系统了\n",
    "# 看过Andrew Ng老师的machine Learning 课程的同学们，知道，我们应该分析分析模型现在的状态了，是 过/欠拟合？，对，就是下面这个很神奇的图，以便了解下一步做什么样的操作可能是有效的\n",
    "# 不过在现在的场景下，先不着急做这个事情，我们这个baseline系统有些粗糙，先再挖掘挖掘\n",
    "# 1. 比如说Name和Ticket两个属性被我们完整舍弃了(好吧，其实是一开始我们对于这种，每一条记录都是一个完全不同的值的属性，并没有很直接的处理方式)\n",
    "# 2. 比如说，我们想想，年龄的拟合本身也未必是一件非常靠谱的事情\n",
    "# 3. 另外，以我们的日常经验，小盆友和老人可能得到的照顾会多一些，这样看的话，年龄作为一个连续值，给一个固定的系数，似乎体现不出两头受照顾的实际情况，所以，说不定我们把年龄离散化，按区段分作类别属性会更合适一些\n",
    "# 那怎么样才知道，哪些地方可以优化，哪些优化的方法是promising的呢？\n",
    "# 是的，要做交叉验证(cross validation)!要做交叉验证(cross validation)!要做交叉验证(cross validation)!重要的事情说3编！！！\n",
    "# 因为test.csv里面并没有Survived这个字段(好吧，这是废话，这明明就是我们要预测的结果)，我们无法在这份数据上评定我们算法在该场景下的效果。。。\n",
    "# 别，哥们儿，你没那个想法吧『每做一次调整就make a submission，然后根据结果来判定这次调整的好坏』，这。。。其实是行不通的。。。\n",
    "# 我们通常情况下，这么做cross validation：把train.csv分成两部分，一部分用于训练我们需要的模型，另外一部分数据上看我们预测算法的效果。\n",
    "# 我们用scikit-learn的cross_validation来完成这个工作\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 493,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coef</th>\n",
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       "      <th>0</th>\n",
       "      <td>[-0.344189431858]</td>\n",
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       "      <th>1</th>\n",
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       "      <td>[0.902071479485]</td>\n",
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       "      <th>4</th>\n",
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       "      <th>6</th>\n",
       "      <td>[-0.417226462259]</td>\n",
       "      <td>Embarked_S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>[1.95649520339]</td>\n",
       "      <td>Sex_female</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>[-0.677484871046]</td>\n",
       "      <td>Sex_male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>[0.341226064445]</td>\n",
       "      <td>Pclass_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>[0.0]</td>\n",
       "      <td>Pclass_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>[-1.19410912948]</td>\n",
       "      <td>Pclass_3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>[-0.523774279397]</td>\n",
       "      <td>Age_scaled</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>[0.0844279740271]</td>\n",
       "      <td>Fare_scaled</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 coef      columns\n",
       "0   [-0.344189431858]        SibSp\n",
       "1   [-0.104924350555]        Parch\n",
       "2               [0.0]     Cabin_No\n",
       "3    [0.902071479485]    Cabin_Yes\n",
       "4               [0.0]   Embarked_C\n",
       "5               [0.0]   Embarked_Q\n",
       "6   [-0.417226462259]   Embarked_S\n",
       "7     [1.95649520339]   Sex_female\n",
       "8   [-0.677484871046]     Sex_male\n",
       "9    [0.341226064445]     Pclass_1\n",
       "10              [0.0]     Pclass_2\n",
       "11   [-1.19410912948]     Pclass_3\n",
       "12  [-0.523774279397]   Age_scaled\n",
       "13  [0.0844279740271]  Fare_scaled"
      ]
     },
     "execution_count": 493,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({\"columns\":list(train_df.columns)[1:], \"coef\":list(clf.coef_.T)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 上面的系数和最后的结果是一个正相关的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 560,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.81564246  0.81005587  0.78651685  0.78651685  0.81355932]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin_No</th>\n",
       "      <th>Cabin_Yes</th>\n",
       "      <th>Embarked_C</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "      <th>Sex_female</th>\n",
       "      <th>Sex_male</th>\n",
       "      <th>Pclass_1</th>\n",
       "      <th>Pclass_2</th>\n",
       "      <th>Pclass_3</th>\n",
       "      <th>Age_scaled</th>\n",
       "      <th>Fare_scaled</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>299</td>\n",
       "      <td>1</td>\n",
       "      <td>41.574877</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.5000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.875617</td>\n",
       "      <td>-0.034314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8542</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.148714</td>\n",
       "      <td>-0.490280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>803</th>\n",
       "      <td>804</td>\n",
       "      <td>1</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8.5167</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-2.145650</td>\n",
       "      <td>-0.476941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>475</td>\n",
       "      <td>0</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.8375</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.561417</td>\n",
       "      <td>-0.450347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>642</th>\n",
       "      <td>643</td>\n",
       "      <td>0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>27.9000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-2.029659</td>\n",
       "      <td>-0.086664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>44.064830</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35.5000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.058409</td>\n",
       "      <td>0.066360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>587</th>\n",
       "      <td>588</td>\n",
       "      <td>1</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>79.2000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.228244</td>\n",
       "      <td>0.946246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>740</th>\n",
       "      <td>741</td>\n",
       "      <td>1</td>\n",
       "      <td>38.426632</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.644497</td>\n",
       "      <td>-0.044381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>839</th>\n",
       "      <td>840</td>\n",
       "      <td>1</td>\n",
       "      <td>50.903700</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>29.7000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.560465</td>\n",
       "      <td>-0.050421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>302</td>\n",
       "      <td>1</td>\n",
       "      <td>23.332602</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>23.2500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.463588</td>\n",
       "      <td>-0.180290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>568</td>\n",
       "      <td>0</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.047532</td>\n",
       "      <td>-0.224083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>712</th>\n",
       "      <td>713</td>\n",
       "      <td>1</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>52.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.347299</td>\n",
       "      <td>0.398582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489</th>\n",
       "      <td>490</td>\n",
       "      <td>1</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>15.9000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.515774</td>\n",
       "      <td>-0.328280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>312</th>\n",
       "      <td>313</td>\n",
       "      <td>0</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.267768</td>\n",
       "      <td>-0.124920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>483</th>\n",
       "      <td>484</td>\n",
       "      <td>1</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.5875</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.448480</td>\n",
       "      <td>-0.455380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>506</td>\n",
       "      <td>0</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>108.9000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.855065</td>\n",
       "      <td>1.544246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>252</td>\n",
       "      <td>0</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.4625</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.047532</td>\n",
       "      <td>-0.437762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>280</td>\n",
       "      <td>1</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>20.2500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.392941</td>\n",
       "      <td>-0.240694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>838</th>\n",
       "      <td>839</td>\n",
       "      <td>1</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>56.4958</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.172705</td>\n",
       "      <td>0.489104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>565</td>\n",
       "      <td>0</td>\n",
       "      <td>30.705727</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.077689</td>\n",
       "      <td>-0.486337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>391</td>\n",
       "      <td>1</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>120.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.466353</td>\n",
       "      <td>1.767741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>643</th>\n",
       "      <td>644</td>\n",
       "      <td>1</td>\n",
       "      <td>29.568898</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>56.4958</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.005768</td>\n",
       "      <td>0.489104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>268</td>\n",
       "      <td>1</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7750</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.341180</td>\n",
       "      <td>-0.491874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>502</td>\n",
       "      <td>0</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.634829</td>\n",
       "      <td>-0.492378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>114</td>\n",
       "      <td>0</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>9.8250</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.708241</td>\n",
       "      <td>-0.450598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>654</th>\n",
       "      <td>655</td>\n",
       "      <td>0</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.7500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.855065</td>\n",
       "      <td>-0.512513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>294</td>\n",
       "      <td>0</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.8500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.414592</td>\n",
       "      <td>-0.470230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>504</td>\n",
       "      <td>0</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.5875</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.539765</td>\n",
       "      <td>-0.455380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>362</th>\n",
       "      <td>363</td>\n",
       "      <td>0</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>14.4542</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.127062</td>\n",
       "      <td>-0.357391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>570</th>\n",
       "      <td>571</td>\n",
       "      <td>1</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2.375068</td>\n",
       "      <td>-0.437007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>265</td>\n",
       "      <td>0</td>\n",
       "      <td>35.295824</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.414658</td>\n",
       "      <td>-0.492378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>762</th>\n",
       "      <td>763</td>\n",
       "      <td>1</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2292</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.708241</td>\n",
       "      <td>-0.502864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>86</td>\n",
       "      <td>1</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>15.8500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.246117</td>\n",
       "      <td>-0.329287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>25.352908</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>15.2458</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.315273</td>\n",
       "      <td>-0.341452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>403</td>\n",
       "      <td>0</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>9.8250</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.634829</td>\n",
       "      <td>-0.450598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>447</th>\n",
       "      <td>448</td>\n",
       "      <td>1</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.319529</td>\n",
       "      <td>-0.113846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>205</td>\n",
       "      <td>1</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.855065</td>\n",
       "      <td>-0.486337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>241</td>\n",
       "      <td>0</td>\n",
       "      <td>20.800154</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>14.4542</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.649500</td>\n",
       "      <td>-0.357391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>284</td>\n",
       "      <td>1</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.781653</td>\n",
       "      <td>-0.486337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>647</th>\n",
       "      <td>648</td>\n",
       "      <td>1</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35.5000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.934596</td>\n",
       "      <td>0.066360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>852</th>\n",
       "      <td>853</td>\n",
       "      <td>0</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>15.2458</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.515774</td>\n",
       "      <td>-0.341452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>788</th>\n",
       "      <td>789</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>20.5750</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-2.103071</td>\n",
       "      <td>-0.234150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>69</td>\n",
       "      <td>1</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.928477</td>\n",
       "      <td>-0.488854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>272</td>\n",
       "      <td>1</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.341180</td>\n",
       "      <td>-0.648422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>883</td>\n",
       "      <td>0</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.5167</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.561417</td>\n",
       "      <td>-0.436671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "      <td>17.107664</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>15.2458</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.920573</td>\n",
       "      <td>-0.341452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>680</th>\n",
       "      <td>681</td>\n",
       "      <td>0</td>\n",
       "      <td>23.476432</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.1375</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.453029</td>\n",
       "      <td>-0.484576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>17.8000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.855065</td>\n",
       "      <td>-0.290024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>339</td>\n",
       "      <td>1</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.127062</td>\n",
       "      <td>-0.486337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>262</td>\n",
       "      <td>1</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>31.3875</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.956247</td>\n",
       "      <td>-0.016444</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived        Age  SibSp  Parch      Fare  Cabin_No  \\\n",
       "298          299         1  41.574877      0      0   30.5000         0   \n",
       "14            15         0  14.000000      0      0    7.8542         1   \n",
       "803          804         1   0.420000      0      1    8.5167         1   \n",
       "474          475         0  22.000000      0      0    9.8375         1   \n",
       "642          643         0   2.000000      3      2   27.9000         1   \n",
       "55            56         1  44.064830      0      0   35.5000         0   \n",
       "587          588         1  60.000000      1      1   79.2000         0   \n",
       "740          741         1  38.426632      0      0   30.0000         0   \n",
       "839          840         1  50.903700      0      0   29.7000         0   \n",
       "301          302         1  23.332602      2      0   23.2500         1   \n",
       "567          568         0  29.000000      0      4   21.0750         1   \n",
       "712          713         1  48.000000      1      0   52.0000         0   \n",
       "489          490         1   9.000000      1      1   15.9000         1   \n",
       "312          313         0  26.000000      1      1   26.0000         1   \n",
       "483          484         1  63.000000      0      0    9.5875         1   \n",
       "505          506         0  18.000000      1      0  108.9000         0   \n",
       "251          252         0  29.000000      1      1   10.4625         0   \n",
       "279          280         1  35.000000      1      1   20.2500         1   \n",
       "838          839         1  32.000000      0      0   56.4958         1   \n",
       "564          565         0  30.705727      0      0    8.0500         1   \n",
       "390          391         1  36.000000      1      2  120.0000         0   \n",
       "643          644         1  29.568898      0      0   56.4958         1   \n",
       "267          268         1  25.000000      1      0    7.7750         1   \n",
       "501          502         0  21.000000      0      0    7.7500         1   \n",
       "113          114         0  20.000000      1      0    9.8250         1   \n",
       "654          655         0  18.000000      0      0    6.7500         1   \n",
       "293          294         0  24.000000      0      0    8.8500         1   \n",
       "503          504         0  37.000000      0      0    9.5875         1   \n",
       "362          363         0  45.000000      0      1   14.4542         1   \n",
       "570          571         1  62.000000      0      0   10.5000         1   \n",
       "264          265         0  35.295824      0      0    7.7500         1   \n",
       "762          763         1  20.000000      0      0    7.2292         1   \n",
       "85            86         1  33.000000      3      0   15.8500         1   \n",
       "140          141         0  25.352908      0      2   15.2458         1   \n",
       "402          403         0  21.000000      1      0    9.8250         1   \n",
       "447          448         1  34.000000      0      0   26.5500         1   \n",
       "204          205         1  18.000000      0      0    8.0500         1   \n",
       "240          241         0  20.800154      1      0   14.4542         1   \n",
       "283          284         1  19.000000      0      0    8.0500         1   \n",
       "647          648         1  56.000000      0      0   35.5000         0   \n",
       "852          853         0   9.000000      1      1   15.2458         1   \n",
       "788          789         1   1.000000      1      2   20.5750         1   \n",
       "68            69         1  17.000000      4      2    7.9250         1   \n",
       "271          272         1  25.000000      0      0    0.0000         1   \n",
       "882          883         0  22.000000      0      0   10.5167         1   \n",
       "65            66         1  17.107664      1      1   15.2458         1   \n",
       "680          681         0  23.476432      0      0    8.1375         1   \n",
       "49            50         0  18.000000      1      0   17.8000         1   \n",
       "338          339         1  45.000000      0      0    8.0500         1   \n",
       "261          262         1   3.000000      4      2   31.3875         1   \n",
       "\n",
       "     Cabin_Yes  Embarked_C  Embarked_Q  Embarked_S  Sex_female  Sex_male  \\\n",
       "298          1           0           0           1           0         1   \n",
       "14           0           0           0           1           1         0   \n",
       "803          0           1           0           0           0         1   \n",
       "474          0           0           0           1           1         0   \n",
       "642          0           0           0           1           1         0   \n",
       "55           1           0           0           1           0         1   \n",
       "587          1           1           0           0           0         1   \n",
       "740          1           0           0           1           0         1   \n",
       "839          1           1           0           0           0         1   \n",
       "301          0           0           1           0           0         1   \n",
       "567          0           0           0           1           1         0   \n",
       "712          1           0           0           1           0         1   \n",
       "489          0           0           0           1           0         1   \n",
       "312          0           0           0           1           1         0   \n",
       "483          0           0           0           1           1         0   \n",
       "505          1           1           0           0           0         1   \n",
       "251          1           0           0           1           1         0   \n",
       "279          0           0           0           1           1         0   \n",
       "838          0           0           0           1           0         1   \n",
       "564          0           0           0           1           1         0   \n",
       "390          1           0           0           1           0         1   \n",
       "643          0           0           0           1           0         1   \n",
       "267          0           0           0           1           0         1   \n",
       "501          0           0           1           0           1         0   \n",
       "113          0           0           0           1           1         0   \n",
       "654          0           0           1           0           1         0   \n",
       "293          0           0           0           1           1         0   \n",
       "503          0           0           0           1           1         0   \n",
       "362          0           1           0           0           1         0   \n",
       "570          0           0           0           1           0         1   \n",
       "264          0           0           1           0           1         0   \n",
       "762          0           1           0           0           0         1   \n",
       "85           0           0           0           1           1         0   \n",
       "140          0           1           0           0           1         0   \n",
       "402          0           0           0           1           1         0   \n",
       "447          0           0           0           1           0         1   \n",
       "204          0           0           0           1           0         1   \n",
       "240          0           1           0           0           1         0   \n",
       "283          0           0           0           1           0         1   \n",
       "647          1           1           0           0           0         1   \n",
       "852          0           1           0           0           1         0   \n",
       "788          0           0           0           1           0         1   \n",
       "68           0           0           0           1           1         0   \n",
       "271          0           0           0           1           0         1   \n",
       "882          0           0           0           1           1         0   \n",
       "65           0           1           0           0           0         1   \n",
       "680          0           0           1           0           1         0   \n",
       "49           0           0           0           1           1         0   \n",
       "338          0           0           0           1           0         1   \n",
       "261          0           0           0           1           0         1   \n",
       "\n",
       "     Pclass_1  Pclass_2  Pclass_3  Age_scaled  Fare_scaled  \n",
       "298         1         0         0    0.875617    -0.034314  \n",
       "14          0         0         1   -1.148714    -0.490280  \n",
       "803         0         0         1   -2.145650    -0.476941  \n",
       "474         0         0         1   -0.561417    -0.450347  \n",
       "642         0         0         1   -2.029659    -0.086664  \n",
       "55          1         0         0    1.058409     0.066360  \n",
       "587         1         0         0    2.228244     0.946246  \n",
       "740         1         0         0    0.644497    -0.044381  \n",
       "839         1         0         0    1.560465    -0.050421  \n",
       "301         0         0         1   -0.463588    -0.180290  \n",
       "567         0         0         1   -0.047532    -0.224083  \n",
       "712         1         0         0    1.347299     0.398582  \n",
       "489         0         0         1   -1.515774    -0.328280  \n",
       "312         0         1         0   -0.267768    -0.124920  \n",
       "483         0         0         1    2.448480    -0.455380  \n",
       "505         1         0         0   -0.855065     1.544246  \n",
       "251         0         0         1   -0.047532    -0.437762  \n",
       "279         0         0         1    0.392941    -0.240694  \n",
       "838         0         0         1    0.172705     0.489104  \n",
       "564         0         0         1    0.077689    -0.486337  \n",
       "390         1         0         0    0.466353     1.767741  \n",
       "643         0         0         1   -0.005768     0.489104  \n",
       "267         0         0         1   -0.341180    -0.491874  \n",
       "501         0         0         1   -0.634829    -0.492378  \n",
       "113         0         0         1   -0.708241    -0.450598  \n",
       "654         0         0         1   -0.855065    -0.512513  \n",
       "293         0         0         1   -0.414592    -0.470230  \n",
       "503         0         0         1    0.539765    -0.455380  \n",
       "362         0         0         1    1.127062    -0.357391  \n",
       "570         0         1         0    2.375068    -0.437007  \n",
       "264         0         0         1    0.414658    -0.492378  \n",
       "762         0         0         1   -0.708241    -0.502864  \n",
       "85          0         0         1    0.246117    -0.329287  \n",
       "140         0         0         1   -0.315273    -0.341452  \n",
       "402         0         0         1   -0.634829    -0.450598  \n",
       "447         1         0         0    0.319529    -0.113846  \n",
       "204         0         0         1   -0.855065    -0.486337  \n",
       "240         0         0         1   -0.649500    -0.357391  \n",
       "283         0         0         1   -0.781653    -0.486337  \n",
       "647         1         0         0    1.934596     0.066360  \n",
       "852         0         0         1   -1.515774    -0.341452  \n",
       "788         0         0         1   -2.103071    -0.234150  \n",
       "68          0         0         1   -0.928477    -0.488854  \n",
       "271         0         0         1   -0.341180    -0.648422  \n",
       "882         0         0         1   -0.561417    -0.436671  \n",
       "65          0         0         1   -0.920573    -0.341452  \n",
       "680         0         0         1   -0.453029    -0.484576  \n",
       "49          0         0         1   -0.855065    -0.290024  \n",
       "338         0         0         1    1.127062    -0.486337  \n",
       "261         0         0         1   -1.956247    -0.016444  "
      ]
     },
     "execution_count": 560,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import cross_validation\n",
    "\n",
    "# 简单看看打分情况\n",
    "clf = linear_model.LogisticRegression(C=1.0, penalty='l1', tol=1e-6)\n",
    "all_data = df.filter(regex='Survived|Age_.*|SibSp|Parch|Fare_.*|Cabin_.*|Embarked_.*|Sex_.*|Pclass_.*')\n",
    "X = all_data.as_matrix()[:,1:]\n",
    "y = all_data.as_matrix()[:,0]\n",
    "print cross_validation.cross_val_score(clf, X, y, cv=5)\n",
    "\n",
    "\n",
    "# 分割数据\n",
    "split_train, split_cv = cross_validation.train_test_split(\u001d",
    "df, test_size=0.3, random_state=0)\n",
    "train_df = split_train.filter(regex='Survived|Age_.*|SibSp|Parch|Fare_.*|Cabin_.*|Embarked_.*|Sex_.*|Pclass_.*')\n",
    "# 生成模型\n",
    "clf = linear_model.LogisticRegression(C=1.0, penalty='l1', tol=1e-6)\n",
    "clf.fit(train_df.as_matrix()[:,1:], train_df.as_matrix()[:,0])\n",
    "\n",
    "\n",
    "\n",
    "# 对cross validation数据进行预测\n",
    "\n",
    "cv_df = split_cv.filter(regex='Survived|Age_.*|SibSp|Parch|Fare_.*|Cabin_.*|Embarked_.*|Sex_.*|Pclass_.*')\n",
    "predictions = clf.predict(cv_df.as_matrix()[:,1:])\n",
    "split_cv[ predictions != cv_df.as_matrix()[:,0] ].drop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 562,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Vestrom, Miss. Hulda Amanda Adolfina</td>\n",
       "      <td>female</td>\n",
       "      <td>14.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>350406</td>\n",
       "      <td>7.8542</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Arnold-Franchi, Mrs. Josef (Josefine Franchi)</td>\n",
       "      <td>female</td>\n",
       "      <td>18.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>349237</td>\n",
       "      <td>17.8000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Woolner, Mr. Hugh</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>19947</td>\n",
       "      <td>35.5000</td>\n",
       "      <td>C52</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Moubarek, Master. Gerios</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2661</td>\n",
       "      <td>15.2458</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>69</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Andersson, Miss. Erna Alexandra</td>\n",
       "      <td>female</td>\n",
       "      <td>17.00</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3101281</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>86</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Backstrom, Mrs. Karl Alfred (Maria Mathilda Gu...</td>\n",
       "      <td>female</td>\n",
       "      <td>33.00</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3101278</td>\n",
       "      <td>15.8500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>114</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Jussila, Miss. Katriina</td>\n",
       "      <td>female</td>\n",
       "      <td>20.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4136</td>\n",
       "      <td>9.8250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Boulos, Mrs. Joseph (Sultana)</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2678</td>\n",
       "      <td>15.2458</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>205</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Cohen, Mr. Gurshon \"Gus\"</td>\n",
       "      <td>male</td>\n",
       "      <td>18.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 3540</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>241</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Zabour, Miss. Thamine</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2665</td>\n",
       "      <td>14.4542</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>252</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Strom, Mrs. Wilhelm (Elna Matilda Persson)</td>\n",
       "      <td>female</td>\n",
       "      <td>29.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>347054</td>\n",
       "      <td>10.4625</td>\n",
       "      <td>G6</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>262</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Asplund, Master. Edvin Rojj Felix</td>\n",
       "      <td>male</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>347077</td>\n",
       "      <td>31.3875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>265</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Henry, Miss. Delia</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>382649</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>268</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Persson, Mr. Ernst Ulrik</td>\n",
       "      <td>male</td>\n",
       "      <td>25.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>347083</td>\n",
       "      <td>7.7750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>272</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Tornquist, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>25.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>LINE</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>280</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Abbott, Mrs. Stanton (Rosa Hunt)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>C.A. 2673</td>\n",
       "      <td>20.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>284</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Dorking, Mr. Edward Arthur</td>\n",
       "      <td>male</td>\n",
       "      <td>19.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5. 10482</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>294</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Haas, Miss. Aloisia</td>\n",
       "      <td>female</td>\n",
       "      <td>24.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349236</td>\n",
       "      <td>8.8500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>299</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Saalfeld, Mr. Adolphe</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>19988</td>\n",
       "      <td>30.5000</td>\n",
       "      <td>C106</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>McCoy, Mr. Bernard</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>367226</td>\n",
       "      <td>23.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>312</th>\n",
       "      <td>313</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Lahtinen, Mrs. William (Anna Sylfven)</td>\n",
       "      <td>female</td>\n",
       "      <td>26.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>250651</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>339</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Dahl, Mr. Karl Edwart</td>\n",
       "      <td>male</td>\n",
       "      <td>45.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7598</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>362</th>\n",
       "      <td>363</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Barbara, Mrs. (Catherine David)</td>\n",
       "      <td>female</td>\n",
       "      <td>45.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2691</td>\n",
       "      <td>14.4542</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>391</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Carter, Mr. William Ernest</td>\n",
       "      <td>male</td>\n",
       "      <td>36.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113760</td>\n",
       "      <td>120.0000</td>\n",
       "      <td>B96 B98</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>403</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Jussila, Miss. Mari Aina</td>\n",
       "      <td>female</td>\n",
       "      <td>21.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4137</td>\n",
       "      <td>9.8250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>447</th>\n",
       "      <td>448</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Seward, Mr. Frederic Kimber</td>\n",
       "      <td>male</td>\n",
       "      <td>34.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113794</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>475</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Strandberg, Miss. Ida Sofia</td>\n",
       "      <td>female</td>\n",
       "      <td>22.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7553</td>\n",
       "      <td>9.8375</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>483</th>\n",
       "      <td>484</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Turkula, Mrs. (Hedwig)</td>\n",
       "      <td>female</td>\n",
       "      <td>63.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4134</td>\n",
       "      <td>9.5875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489</th>\n",
       "      <td>490</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Coutts, Master. Eden Leslie \"Neville\"</td>\n",
       "      <td>male</td>\n",
       "      <td>9.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>C.A. 37671</td>\n",
       "      <td>15.9000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>502</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Canavan, Miss. Mary</td>\n",
       "      <td>female</td>\n",
       "      <td>21.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>364846</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>504</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Laitinen, Miss. Kristina Sofia</td>\n",
       "      <td>female</td>\n",
       "      <td>37.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4135</td>\n",
       "      <td>9.5875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>506</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Penasco y Castellana, Mr. Victor de Satode</td>\n",
       "      <td>male</td>\n",
       "      <td>18.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17758</td>\n",
       "      <td>108.9000</td>\n",
       "      <td>C65</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>565</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Meanwell, Miss. (Marion Ogden)</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/O.Q. 392087</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>568</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Mrs. Nils (Alma Cornelia Berglund)</td>\n",
       "      <td>female</td>\n",
       "      <td>29.00</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>570</th>\n",
       "      <td>571</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Harris, Mr. George</td>\n",
       "      <td>male</td>\n",
       "      <td>62.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>S.W./PP 752</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>587</th>\n",
       "      <td>588</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Frolicher-Stehli, Mr. Maxmillian</td>\n",
       "      <td>male</td>\n",
       "      <td>60.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>13567</td>\n",
       "      <td>79.2000</td>\n",
       "      <td>B41</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>642</th>\n",
       "      <td>643</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Skoog, Miss. Margit Elizabeth</td>\n",
       "      <td>female</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>347088</td>\n",
       "      <td>27.9000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>643</th>\n",
       "      <td>644</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Foo, Mr. Choong</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1601</td>\n",
       "      <td>56.4958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>647</th>\n",
       "      <td>648</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Simonius-Blumer, Col. Oberst Alfons</td>\n",
       "      <td>male</td>\n",
       "      <td>56.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13213</td>\n",
       "      <td>35.5000</td>\n",
       "      <td>A26</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>654</th>\n",
       "      <td>655</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Hegarty, Miss. Hanora \"Nora\"</td>\n",
       "      <td>female</td>\n",
       "      <td>18.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>365226</td>\n",
       "      <td>6.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>680</th>\n",
       "      <td>681</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Peters, Miss. Katie</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330935</td>\n",
       "      <td>8.1375</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>712</th>\n",
       "      <td>713</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Taylor, Mr. Elmer Zebley</td>\n",
       "      <td>male</td>\n",
       "      <td>48.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>19996</td>\n",
       "      <td>52.0000</td>\n",
       "      <td>C126</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>740</th>\n",
       "      <td>741</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Hawksford, Mr. Walter James</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16988</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>D45</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>762</th>\n",
       "      <td>763</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Barah, Mr. Hanna Assi</td>\n",
       "      <td>male</td>\n",
       "      <td>20.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2663</td>\n",
       "      <td>7.2292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>788</th>\n",
       "      <td>789</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Dean, Master. Bertram Vere</td>\n",
       "      <td>male</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>C.A. 2315</td>\n",
       "      <td>20.5750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>803</th>\n",
       "      <td>804</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Thomas, Master. Assad Alexander</td>\n",
       "      <td>male</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2625</td>\n",
       "      <td>8.5167</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>838</th>\n",
       "      <td>839</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Chip, Mr. Chang</td>\n",
       "      <td>male</td>\n",
       "      <td>32.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1601</td>\n",
       "      <td>56.4958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>839</th>\n",
       "      <td>840</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Marechal, Mr. Pierre</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11774</td>\n",
       "      <td>29.7000</td>\n",
       "      <td>C47</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>852</th>\n",
       "      <td>853</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Boulos, Miss. Nourelain</td>\n",
       "      <td>female</td>\n",
       "      <td>9.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2678</td>\n",
       "      <td>15.2458</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>883</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dahlberg, Miss. Gerda Ulrika</td>\n",
       "      <td>female</td>\n",
       "      <td>22.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7552</td>\n",
       "      <td>10.5167</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "14            15         0       3   \n",
       "49            50         0       3   \n",
       "55            56         1       1   \n",
       "65            66         1       3   \n",
       "68            69         1       3   \n",
       "85            86         1       3   \n",
       "113          114         0       3   \n",
       "140          141         0       3   \n",
       "204          205         1       3   \n",
       "240          241         0       3   \n",
       "251          252         0       3   \n",
       "261          262         1       3   \n",
       "264          265         0       3   \n",
       "267          268         1       3   \n",
       "271          272         1       3   \n",
       "279          280         1       3   \n",
       "283          284         1       3   \n",
       "293          294         0       3   \n",
       "298          299         1       1   \n",
       "301          302         1       3   \n",
       "312          313         0       2   \n",
       "338          339         1       3   \n",
       "362          363         0       3   \n",
       "390          391         1       1   \n",
       "402          403         0       3   \n",
       "447          448         1       1   \n",
       "474          475         0       3   \n",
       "483          484         1       3   \n",
       "489          490         1       3   \n",
       "501          502         0       3   \n",
       "503          504         0       3   \n",
       "505          506         0       1   \n",
       "564          565         0       3   \n",
       "567          568         0       3   \n",
       "570          571         1       2   \n",
       "587          588         1       1   \n",
       "642          643         0       3   \n",
       "643          644         1       3   \n",
       "647          648         1       1   \n",
       "654          655         0       3   \n",
       "680          681         0       3   \n",
       "712          713         1       1   \n",
       "740          741         1       1   \n",
       "762          763         1       3   \n",
       "788          789         1       3   \n",
       "803          804         1       3   \n",
       "838          839         1       3   \n",
       "839          840         1       1   \n",
       "852          853         0       3   \n",
       "882          883         0       3   \n",
       "\n",
       "                                                  Name     Sex    Age  SibSp  \\\n",
       "14                Vestrom, Miss. Hulda Amanda Adolfina  female  14.00      0   \n",
       "49       Arnold-Franchi, Mrs. Josef (Josefine Franchi)  female  18.00      1   \n",
       "55                                   Woolner, Mr. Hugh    male    NaN      0   \n",
       "65                            Moubarek, Master. Gerios    male    NaN      1   \n",
       "68                     Andersson, Miss. Erna Alexandra  female  17.00      4   \n",
       "85   Backstrom, Mrs. Karl Alfred (Maria Mathilda Gu...  female  33.00      3   \n",
       "113                            Jussila, Miss. Katriina  female  20.00      1   \n",
       "140                      Boulos, Mrs. Joseph (Sultana)  female    NaN      0   \n",
       "204                           Cohen, Mr. Gurshon \"Gus\"    male  18.00      0   \n",
       "240                              Zabour, Miss. Thamine  female    NaN      1   \n",
       "251         Strom, Mrs. Wilhelm (Elna Matilda Persson)  female  29.00      1   \n",
       "261                  Asplund, Master. Edvin Rojj Felix    male   3.00      4   \n",
       "264                                 Henry, Miss. Delia  female    NaN      0   \n",
       "267                           Persson, Mr. Ernst Ulrik    male  25.00      1   \n",
       "271                       Tornquist, Mr. William Henry    male  25.00      0   \n",
       "279                   Abbott, Mrs. Stanton (Rosa Hunt)  female  35.00      1   \n",
       "283                         Dorking, Mr. Edward Arthur    male  19.00      0   \n",
       "293                                Haas, Miss. Aloisia  female  24.00      0   \n",
       "298                              Saalfeld, Mr. Adolphe    male    NaN      0   \n",
       "301                                 McCoy, Mr. Bernard    male    NaN      2   \n",
       "312              Lahtinen, Mrs. William (Anna Sylfven)  female  26.00      1   \n",
       "338                              Dahl, Mr. Karl Edwart    male  45.00      0   \n",
       "362                    Barbara, Mrs. (Catherine David)  female  45.00      0   \n",
       "390                         Carter, Mr. William Ernest    male  36.00      1   \n",
       "402                           Jussila, Miss. Mari Aina  female  21.00      1   \n",
       "447                        Seward, Mr. Frederic Kimber    male  34.00      0   \n",
       "474                        Strandberg, Miss. Ida Sofia  female  22.00      0   \n",
       "483                             Turkula, Mrs. (Hedwig)  female  63.00      0   \n",
       "489              Coutts, Master. Eden Leslie \"Neville\"    male   9.00      1   \n",
       "501                                Canavan, Miss. Mary  female  21.00      0   \n",
       "503                     Laitinen, Miss. Kristina Sofia  female  37.00      0   \n",
       "505         Penasco y Castellana, Mr. Victor de Satode    male  18.00      1   \n",
       "564                     Meanwell, Miss. (Marion Ogden)  female    NaN      0   \n",
       "567        Palsson, Mrs. Nils (Alma Cornelia Berglund)  female  29.00      0   \n",
       "570                                 Harris, Mr. George    male  62.00      0   \n",
       "587                   Frolicher-Stehli, Mr. Maxmillian    male  60.00      1   \n",
       "642                      Skoog, Miss. Margit Elizabeth  female   2.00      3   \n",
       "643                                    Foo, Mr. Choong    male    NaN      0   \n",
       "647                Simonius-Blumer, Col. Oberst Alfons    male  56.00      0   \n",
       "654                       Hegarty, Miss. Hanora \"Nora\"  female  18.00      0   \n",
       "680                                Peters, Miss. Katie  female    NaN      0   \n",
       "712                           Taylor, Mr. Elmer Zebley    male  48.00      1   \n",
       "740                        Hawksford, Mr. Walter James    male    NaN      0   \n",
       "762                              Barah, Mr. Hanna Assi    male  20.00      0   \n",
       "788                         Dean, Master. Bertram Vere    male   1.00      1   \n",
       "803                    Thomas, Master. Assad Alexander    male   0.42      0   \n",
       "838                                    Chip, Mr. Chang    male  32.00      0   \n",
       "839                               Marechal, Mr. Pierre    male    NaN      0   \n",
       "852                            Boulos, Miss. Nourelain  female   9.00      1   \n",
       "882                       Dahlberg, Miss. Gerda Ulrika  female  22.00      0   \n",
       "\n",
       "     Parch             Ticket      Fare    Cabin Embarked  \n",
       "14       0             350406    7.8542      NaN        S  \n",
       "49       0             349237   17.8000      NaN        S  \n",
       "55       0              19947   35.5000      C52        S  \n",
       "65       1               2661   15.2458      NaN        C  \n",
       "68       2            3101281    7.9250      NaN        S  \n",
       "85       0            3101278   15.8500      NaN        S  \n",
       "113      0               4136    9.8250      NaN        S  \n",
       "140      2               2678   15.2458      NaN        C  \n",
       "204      0           A/5 3540    8.0500      NaN        S  \n",
       "240      0               2665   14.4542      NaN        C  \n",
       "251      1             347054   10.4625       G6        S  \n",
       "261      2             347077   31.3875      NaN        S  \n",
       "264      0             382649    7.7500      NaN        Q  \n",
       "267      0             347083    7.7750      NaN        S  \n",
       "271      0               LINE    0.0000      NaN        S  \n",
       "279      1          C.A. 2673   20.2500      NaN        S  \n",
       "283      0         A/5. 10482    8.0500      NaN        S  \n",
       "293      0             349236    8.8500      NaN        S  \n",
       "298      0              19988   30.5000     C106        S  \n",
       "301      0             367226   23.2500      NaN        Q  \n",
       "312      1             250651   26.0000      NaN        S  \n",
       "338      0               7598    8.0500      NaN        S  \n",
       "362      1               2691   14.4542      NaN        C  \n",
       "390      2             113760  120.0000  B96 B98        S  \n",
       "402      0               4137    9.8250      NaN        S  \n",
       "447      0             113794   26.5500      NaN        S  \n",
       "474      0               7553    9.8375      NaN        S  \n",
       "483      0               4134    9.5875      NaN        S  \n",
       "489      1         C.A. 37671   15.9000      NaN        S  \n",
       "501      0             364846    7.7500      NaN        Q  \n",
       "503      0               4135    9.5875      NaN        S  \n",
       "505      0           PC 17758  108.9000      C65        C  \n",
       "564      0  SOTON/O.Q. 392087    8.0500      NaN        S  \n",
       "567      4             349909   21.0750      NaN        S  \n",
       "570      0        S.W./PP 752   10.5000      NaN        S  \n",
       "587      1              13567   79.2000      B41        C  \n",
       "642      2             347088   27.9000      NaN        S  \n",
       "643      0               1601   56.4958      NaN        S  \n",
       "647      0              13213   35.5000      A26        C  \n",
       "654      0             365226    6.7500      NaN        Q  \n",
       "680      0             330935    8.1375      NaN        Q  \n",
       "712      0              19996   52.0000     C126        S  \n",
       "740      0              16988   30.0000      D45        S  \n",
       "762      0               2663    7.2292      NaN        C  \n",
       "788      2          C.A. 2315   20.5750      NaN        S  \n",
       "803      1               2625    8.5167      NaN        C  \n",
       "838      0               1601   56.4958      NaN        S  \n",
       "839      0              11774   29.7000      C47        C  \n",
       "852      1               2678   15.2458      NaN        C  \n",
       "882      0               7552   10.5167      NaN        S  "
      ]
     },
     "execution_count": 562,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 去除预测错误的case看原始dataframe数据\n",
    "#split_cv['PredictResult'] = predictions\n",
    "origin_data_train = pd.read_csv(\"/Users/MLS/Downloads/Train.csv\")\n",
    "bad_cases = origin_data_train.loc[origin_data_train['PassengerId'].isin(split_cv[predictions != cv_df.as_matrix()[:,0]]['PassengerId'].values)]\n",
    "bad_cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 440,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>450</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Peuchen, Major. Arthur Godfrey</td>\n",
       "      <td>male</td>\n",
       "      <td>52</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113786</td>\n",
       "      <td>30.50</td>\n",
       "      <td>Yes</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>536</th>\n",
       "      <td>537</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Butt, Major. Archibald Willingham</td>\n",
       "      <td>male</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113050</td>\n",
       "      <td>26.55</td>\n",
       "      <td>Yes</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                               Name   Sex  \\\n",
       "449          450         1       1     Peuchen, Major. Arthur Godfrey  male   \n",
       "536          537         0       1  Butt, Major. Archibald Willingham  male   \n",
       "\n",
       "     Age  SibSp  Parch  Ticket   Fare Cabin Embarked  \n",
       "449   52      0      0  113786  30.50   Yes        S  \n",
       "536   45      0      0  113050  26.55   Yes        S  "
      ]
     },
     "execution_count": 440,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train[data_train['Name'].str.contains(\"Major\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 431,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0             A/5 21171\n",
       "1              PC 17599\n",
       "2      STON/O2. 3101282\n",
       "3                113803\n",
       "4                373450\n",
       "5                330877\n",
       "6                 17463\n",
       "7                349909\n",
       "8                347742\n",
       "9                237736\n",
       "10              PP 9549\n",
       "11               113783\n",
       "12            A/5. 2151\n",
       "13               347082\n",
       "14               350406\n",
       "15               248706\n",
       "16               382652\n",
       "17               244373\n",
       "18               345763\n",
       "19                 2649\n",
       "20               239865\n",
       "21               248698\n",
       "22               330923\n",
       "23               113788\n",
       "24               349909\n",
       "25               347077\n",
       "26                 2631\n",
       "27                19950\n",
       "28               330959\n",
       "29               349216\n",
       "             ...       \n",
       "861               28134\n",
       "862               17466\n",
       "863            CA. 2343\n",
       "864              233866\n",
       "865              236852\n",
       "866       SC/PARIS 2149\n",
       "867            PC 17590\n",
       "868              345777\n",
       "869              347742\n",
       "870              349248\n",
       "871               11751\n",
       "872                 695\n",
       "873              345765\n",
       "874           P/PP 3381\n",
       "875                2667\n",
       "876                7534\n",
       "877              349212\n",
       "878              349217\n",
       "879               11767\n",
       "880              230433\n",
       "881              349257\n",
       "882                7552\n",
       "883    C.A./SOTON 34068\n",
       "884     SOTON/OQ 392076\n",
       "885              382652\n",
       "886              211536\n",
       "887              112053\n",
       "888          W./C. 6607\n",
       "889              111369\n",
       "890              370376\n",
       "Name: Ticket, dtype: object"
      ]
     },
     "execution_count": 431,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 546,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr',\n",
       "          penalty='l1', random_state=None, solver='liblinear', tol=1e-06,\n",
       "          verbose=0)"
      ]
     },
     "execution_count": 546,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train = pd.read_csv(\"/Users/MLS/Downloads/Train.csv\")\n",
    "data_train['Sex_Pclass'] = data_train.Sex + \"_\" + data_train.Pclass.map(str)\n",
    "\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    " \n",
    "### 使用 RandomForestClassifier 填补缺失的年龄属性\n",
    "def set_missing_ages(df):\n",
    "    \n",
    "    # 把已有的数值型特征取出来丢进Random Forest Regressor中\n",
    "    age_df = df[['Age','Fare', 'Parch', 'SibSp', 'Pclass']]\n",
    "\n",
    "    # 乘客分成已知年龄和未知年龄两部分\n",
    "    known_age = age_df[age_df.Age.notnull()].as_matrix()\n",
    "    unknown_age = age_df[age_df.Age.isnull()].as_matrix()\n",
    "\n",
    "    # y即目标年龄\n",
    "    y = known_age[:, 0]\n",
    "\n",
    "    # X即特征属性值\n",
    "    X = known_age[:, 1:]\n",
    "\n",
    "    # fit到RandomForestRegressor之中\n",
    "    rfr = RandomForestRegressor(random_state=0, n_estimators=2000, n_jobs=-1)\n",
    "    rfr.fit(X, y)\n",
    "    \n",
    "    # 用得到的模型进行未知年龄结果预测\n",
    "    predictedAges = rfr.predict(unknown_age[:, 1::])\n",
    "    \n",
    "    # 用得到的预测结果填补原缺失数据\n",
    "    df.loc[ (df.Age.isnull()), 'Age' ] = predictedAges \n",
    "    \n",
    "    return df, rfr\n",
    "\n",
    "def set_Cabin_type(df):\n",
    "    df.loc[ (df.Cabin.notnull()), 'Cabin' ] = \"Yes\"\n",
    "    df.loc[ (df.Cabin.isnull()), 'Cabin' ] = \"No\"\n",
    "    return df\n",
    "\n",
    "data_train, rfr = set_missing_ages(data_train)\n",
    "data_train = set_Cabin_type(data_train)\n",
    "\n",
    "dummies_Cabin = pd.get_dummies(data_train['Cabin'], prefix= 'Cabin')\n",
    "dummies_Embarked = pd.get_dummies(data_train['Embarked'], prefix= 'Embarked')\n",
    "dummies_Sex = pd.get_dummies(data_train['Sex'], prefix= 'Sex')\n",
    "dummies_Pclass = pd.get_dummies(data_train['Pclass'], prefix= 'Pclass')\n",
    "dummies_Sex_Pclass = pd.get_dummies(data_train['Sex_Pclass'], prefix= 'Sex_Pclass')\n",
    "\n",
    "\n",
    "df = pd.concat([data_train, dummies_Cabin, dummies_Embarked, dummies_Sex, dummies_Pclass, dummies_Sex_Pclass], axis=1)\n",
    "df.drop(['Pclass', 'Name', 'Sex', 'Ticket', 'Cabin', 'Embarked', 'Sex_Pclass'], axis=1, inplace=True)\n",
    "import sklearn.preprocessing as preprocessing\n",
    "scaler = preprocessing.StandardScaler()\n",
    "age_scale_param = scaler.fit(df['Age'])\n",
    "df['Age_scaled'] = scaler.fit_transform(df['Age'], age_scale_param)\n",
    "fare_scale_param = scaler.fit(df['Fare'])\n",
    "df['Fare_scaled'] = scaler.fit_transform(df['Fare'], fare_scale_param)\n",
    "\n",
    "from sklearn import linear_model\n",
    "\n",
    "train_df = df.filter(regex='Survived|Age_.*|SibSp|Parch|Fare_.*|Cabin_.*|Embarked_.*|Sex_.*|Pclass.*')\n",
    "train_np = train_df.as_matrix()\n",
    "\n",
    "# y即Survival结果\n",
    "y = train_np[:, 0]\n",
    "\n",
    "# X即特征属性值\n",
    "X = train_np[:, 1:]\n",
    "\n",
    "# fit到RandomForestRegressor之中\n",
    "clf = linear_model.LogisticRegression(C=1.0, penalty='l1', tol=1e-6)\n",
    "clf.fit(X, y)\n",
    "clf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 543,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin_No</th>\n",
       "      <th>Cabin_Yes</th>\n",
       "      <th>Embarked_C</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "      <th>...</th>\n",
       "      <th>Pclass_2</th>\n",
       "      <th>Pclass_3</th>\n",
       "      <th>Sex_Pclass_female_1</th>\n",
       "      <th>Sex_Pclass_female_2</th>\n",
       "      <th>Sex_Pclass_female_3</th>\n",
       "      <th>Sex_Pclass_male_1</th>\n",
       "      <th>Sex_Pclass_male_2</th>\n",
       "      <th>Sex_Pclass_male_3</th>\n",
       "      <th>Age_scaled</th>\n",
       "      <th>Fare_scaled</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>34.500000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.307495</td>\n",
       "      <td>-0.496637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.256225</td>\n",
       "      <td>-0.511497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2.394702</td>\n",
       "      <td>-0.463335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.261743</td>\n",
       "      <td>-0.481704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.641235</td>\n",
       "      <td>-0.416740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>897</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.2250</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.248423</td>\n",
       "      <td>-0.471623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>898</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.6292</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.034048</td>\n",
       "      <td>-0.500221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>899</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.337642</td>\n",
       "      <td>-0.117238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>900</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2292</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.944829</td>\n",
       "      <td>-0.507390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>901</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>24.1500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.717134</td>\n",
       "      <td>-0.204154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>902</td>\n",
       "      <td>27.947206</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.189852</td>\n",
       "      <td>-0.495444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>903</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.180327</td>\n",
       "      <td>-0.171000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>904</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>82.2667</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.565337</td>\n",
       "      <td>0.837349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>905</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2.470600</td>\n",
       "      <td>-0.171000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>906</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>61.1750</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.256225</td>\n",
       "      <td>0.459367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>907</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>27.7208</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.489439</td>\n",
       "      <td>-0.140162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>908</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12.3500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.345444</td>\n",
       "      <td>-0.415620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>909</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.717134</td>\n",
       "      <td>-0.507465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>910</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.261743</td>\n",
       "      <td>-0.494920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>911</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.104428</td>\n",
       "      <td>-0.507465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>912</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>59.4000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.863413</td>\n",
       "      <td>0.427557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>913</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.1708</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.627915</td>\n",
       "      <td>-0.580120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>914</td>\n",
       "      <td>52.314311</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31.6833</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.659573</td>\n",
       "      <td>-0.069151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>915</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>61.3792</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.717134</td>\n",
       "      <td>0.463026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>916</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>262.3750</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.332124</td>\n",
       "      <td>4.065049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>917</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>14.5000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.483921</td>\n",
       "      <td>-0.377090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>918</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>61.9792</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.641235</td>\n",
       "      <td>0.473779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>919</td>\n",
       "      <td>22.500000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.603286</td>\n",
       "      <td>-0.507465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>920</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.5000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.800835</td>\n",
       "      <td>-0.090356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>921</td>\n",
       "      <td>23.481602</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>21.6792</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.528784</td>\n",
       "      <td>-0.248433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>388</th>\n",
       "      <td>1280</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.717134</td>\n",
       "      <td>-0.498056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389</th>\n",
       "      <td>1281</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.855610</td>\n",
       "      <td>-0.259261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>1282</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93.5000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.565337</td>\n",
       "      <td>1.038659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>1283</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>39.4000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.559819</td>\n",
       "      <td>0.069140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392</th>\n",
       "      <td>1284</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>20.2500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.324321</td>\n",
       "      <td>-0.274045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>1285</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.256225</td>\n",
       "      <td>-0.448774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>1286</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>22.0250</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.109946</td>\n",
       "      <td>-0.242236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>1287</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>60.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.944829</td>\n",
       "      <td>0.438310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>1288</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.489439</td>\n",
       "      <td>-0.507017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>1289</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>79.2000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.332124</td>\n",
       "      <td>0.782391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>1290</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7750</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.641235</td>\n",
       "      <td>-0.497608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>1291</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7333</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.041850</td>\n",
       "      <td>-0.498356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>1292</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>164.8667</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.034048</td>\n",
       "      <td>2.317614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>401</th>\n",
       "      <td>1293</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>21.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.573139</td>\n",
       "      <td>-0.260605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>1294</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>59.4000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.641235</td>\n",
       "      <td>0.427557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>1295</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>47.1000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.020728</td>\n",
       "      <td>0.207130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>1296</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>27.7208</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.952632</td>\n",
       "      <td>-0.140162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405</th>\n",
       "      <td>1297</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.8625</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.793032</td>\n",
       "      <td>-0.388515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>1298</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.565337</td>\n",
       "      <td>-0.448774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>1299</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>211.5000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.483921</td>\n",
       "      <td>3.153324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>1300</td>\n",
       "      <td>19.895581</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7208</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.800957</td>\n",
       "      <td>-0.498580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>409</th>\n",
       "      <td>1301</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>13.7750</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-2.083305</td>\n",
       "      <td>-0.390083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>1302</td>\n",
       "      <td>35.295824</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.367897</td>\n",
       "      <td>-0.498056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>411</th>\n",
       "      <td>1303</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>90.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.497241</td>\n",
       "      <td>0.975936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>1304</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7750</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.185845</td>\n",
       "      <td>-0.497608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>1305</td>\n",
       "      <td>30.705727</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.019516</td>\n",
       "      <td>-0.492680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>1306</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>108.9000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.649038</td>\n",
       "      <td>1.314641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>1307</td>\n",
       "      <td>38.500000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.611089</td>\n",
       "      <td>-0.507017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>1308</td>\n",
       "      <td>30.705727</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.019516</td>\n",
       "      <td>-0.492680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>1309</td>\n",
       "      <td>25.729701</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>22.3583</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.358157</td>\n",
       "      <td>-0.236263</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId        Age  SibSp  Parch      Fare  Cabin_No  Cabin_Yes  \\\n",
       "0            892  34.500000      0      0    7.8292         1          0   \n",
       "1            893  47.000000      1      0    7.0000         1          0   \n",
       "2            894  62.000000      0      0    9.6875         1          0   \n",
       "3            895  27.000000      0      0    8.6625         1          0   \n",
       "4            896  22.000000      1      1   12.2875         1          0   \n",
       "5            897  14.000000      0      0    9.2250         1          0   \n",
       "6            898  30.000000      0      0    7.6292         1          0   \n",
       "7            899  26.000000      1      1   29.0000         1          0   \n",
       "8            900  18.000000      0      0    7.2292         1          0   \n",
       "9            901  21.000000      2      0   24.1500         1          0   \n",
       "10           902  27.947206      0      0    7.8958         1          0   \n",
       "11           903  46.000000      0      0   26.0000         1          0   \n",
       "12           904  23.000000      1      0   82.2667         0          1   \n",
       "13           905  63.000000      1      0   26.0000         1          0   \n",
       "14           906  47.000000      1      0   61.1750         0          1   \n",
       "15           907  24.000000      1      0   27.7208         1          0   \n",
       "16           908  35.000000      0      0   12.3500         1          0   \n",
       "17           909  21.000000      0      0    7.2250         1          0   \n",
       "18           910  27.000000      1      0    7.9250         1          0   \n",
       "19           911  45.000000      0      0    7.2250         1          0   \n",
       "20           912  55.000000      1      0   59.4000         1          0   \n",
       "21           913   9.000000      0      1    3.1708         1          0   \n",
       "22           914  52.314311      0      0   31.6833         1          0   \n",
       "23           915  21.000000      0      1   61.3792         1          0   \n",
       "24           916  48.000000      1      3  262.3750         0          1   \n",
       "25           917  50.000000      1      0   14.5000         1          0   \n",
       "26           918  22.000000      0      1   61.9792         0          1   \n",
       "27           919  22.500000      0      0    7.2250         1          0   \n",
       "28           920  41.000000      0      0   30.5000         0          1   \n",
       "29           921  23.481602      2      0   21.6792         1          0   \n",
       "..           ...        ...    ...    ...       ...       ...        ...   \n",
       "388         1280  21.000000      0      0    7.7500         1          0   \n",
       "389         1281   6.000000      3      1   21.0750         1          0   \n",
       "390         1282  23.000000      0      0   93.5000         0          1   \n",
       "391         1283  51.000000      0      1   39.4000         0          1   \n",
       "392         1284  13.000000      0      2   20.2500         1          0   \n",
       "393         1285  47.000000      0      0   10.5000         1          0   \n",
       "394         1286  29.000000      3      1   22.0250         1          0   \n",
       "395         1287  18.000000      1      0   60.0000         0          1   \n",
       "396         1288  24.000000      0      0    7.2500         1          0   \n",
       "397         1289  48.000000      1      1   79.2000         0          1   \n",
       "398         1290  22.000000      0      0    7.7750         1          0   \n",
       "399         1291  31.000000      0      0    7.7333         1          0   \n",
       "400         1292  30.000000      0      0  164.8667         0          1   \n",
       "401         1293  38.000000      1      0   21.0000         1          0   \n",
       "402         1294  22.000000      0      1   59.4000         1          0   \n",
       "403         1295  17.000000      0      0   47.1000         1          0   \n",
       "404         1296  43.000000      1      0   27.7208         0          1   \n",
       "405         1297  20.000000      0      0   13.8625         0          1   \n",
       "406         1298  23.000000      1      0   10.5000         1          0   \n",
       "407         1299  50.000000      1      1  211.5000         0          1   \n",
       "408         1300  19.895581      0      0    7.7208         1          0   \n",
       "409         1301   3.000000      1      1   13.7750         1          0   \n",
       "410         1302  35.295824      0      0    7.7500         1          0   \n",
       "411         1303  37.000000      1      0   90.0000         0          1   \n",
       "412         1304  28.000000      0      0    7.7750         1          0   \n",
       "413         1305  30.705727      0      0    8.0500         1          0   \n",
       "414         1306  39.000000      0      0  108.9000         0          1   \n",
       "415         1307  38.500000      0      0    7.2500         1          0   \n",
       "416         1308  30.705727      0      0    8.0500         1          0   \n",
       "417         1309  25.729701      1      1   22.3583         1          0   \n",
       "\n",
       "     Embarked_C  Embarked_Q  Embarked_S     ...       Pclass_2  Pclass_3  \\\n",
       "0             0           1           0     ...              0         1   \n",
       "1             0           0           1     ...              0         1   \n",
       "2             0           1           0     ...              1         0   \n",
       "3             0           0           1     ...              0         1   \n",
       "4             0           0           1     ...              0         1   \n",
       "5             0           0           1     ...              0         1   \n",
       "6             0           1           0     ...              0         1   \n",
       "7             0           0           1     ...              1         0   \n",
       "8             1           0           0     ...              0         1   \n",
       "9             0           0           1     ...              0         1   \n",
       "10            0           0           1     ...              0         1   \n",
       "11            0           0           1     ...              0         0   \n",
       "12            0           0           1     ...              0         0   \n",
       "13            0           0           1     ...              1         0   \n",
       "14            0           0           1     ...              0         0   \n",
       "15            1           0           0     ...              1         0   \n",
       "16            0           1           0     ...              1         0   \n",
       "17            1           0           0     ...              0         1   \n",
       "18            0           0           1     ...              0         1   \n",
       "19            1           0           0     ...              0         1   \n",
       "20            1           0           0     ...              0         0   \n",
       "21            0           0           1     ...              0         1   \n",
       "22            0           0           1     ...              0         0   \n",
       "23            1           0           0     ...              0         0   \n",
       "24            1           0           0     ...              0         0   \n",
       "25            0           0           1     ...              0         1   \n",
       "26            1           0           0     ...              0         0   \n",
       "27            1           0           0     ...              0         1   \n",
       "28            0           0           1     ...              0         0   \n",
       "29            1           0           0     ...              0         1   \n",
       "..          ...         ...         ...     ...            ...       ...   \n",
       "388           0           1           0     ...              0         1   \n",
       "389           0           0           1     ...              0         1   \n",
       "390           0           0           1     ...              0         0   \n",
       "391           0           0           1     ...              0         0   \n",
       "392           0           0           1     ...              0         1   \n",
       "393           0           0           1     ...              1         0   \n",
       "394           0           0           1     ...              0         1   \n",
       "395           0           0           1     ...              0         0   \n",
       "396           0           1           0     ...              0         1   \n",
       "397           1           0           0     ...              0         0   \n",
       "398           0           0           1     ...              0         1   \n",
       "399           0           1           0     ...              0         1   \n",
       "400           0           0           1     ...              0         0   \n",
       "401           0           0           1     ...              1         0   \n",
       "402           1           0           0     ...              0         0   \n",
       "403           0           0           1     ...              0         0   \n",
       "404           1           0           0     ...              0         0   \n",
       "405           1           0           0     ...              1         0   \n",
       "406           0           0           1     ...              1         0   \n",
       "407           1           0           0     ...              0         0   \n",
       "408           0           1           0     ...              0         1   \n",
       "409           0           0           1     ...              0         1   \n",
       "410           0           1           0     ...              0         1   \n",
       "411           0           1           0     ...              0         0   \n",
       "412           0           0           1     ...              0         1   \n",
       "413           0           0           1     ...              0         1   \n",
       "414           1           0           0     ...              0         0   \n",
       "415           0           0           1     ...              0         1   \n",
       "416           0           0           1     ...              0         1   \n",
       "417           1           0           0     ...              0         1   \n",
       "\n",
       "     Sex_Pclass_female_1  Sex_Pclass_female_2  Sex_Pclass_female_3  \\\n",
       "0                      0                    0                    0   \n",
       "1                      0                    0                    1   \n",
       "2                      0                    0                    0   \n",
       "3                      0                    0                    0   \n",
       "4                      0                    0                    1   \n",
       "5                      0                    0                    0   \n",
       "6                      0                    0                    1   \n",
       "7                      0                    0                    0   \n",
       "8                      0                    0                    1   \n",
       "9                      0                    0                    0   \n",
       "10                     0                    0                    0   \n",
       "11                     0                    0                    0   \n",
       "12                     1                    0                    0   \n",
       "13                     0                    0                    0   \n",
       "14                     1                    0                    0   \n",
       "15                     0                    1                    0   \n",
       "16                     0                    0                    0   \n",
       "17                     0                    0                    0   \n",
       "18                     0                    0                    1   \n",
       "19                     0                    0                    1   \n",
       "20                     0                    0                    0   \n",
       "21                     0                    0                    0   \n",
       "22                     1                    0                    0   \n",
       "23                     0                    0                    0   \n",
       "24                     1                    0                    0   \n",
       "25                     0                    0                    0   \n",
       "26                     1                    0                    0   \n",
       "27                     0                    0                    0   \n",
       "28                     0                    0                    0   \n",
       "29                     0                    0                    0   \n",
       "..                   ...                  ...                  ...   \n",
       "388                    0                    0                    0   \n",
       "389                    0                    0                    0   \n",
       "390                    0                    0                    0   \n",
       "391                    1                    0                    0   \n",
       "392                    0                    0                    0   \n",
       "393                    0                    0                    0   \n",
       "394                    0                    0                    0   \n",
       "395                    1                    0                    0   \n",
       "396                    0                    0                    0   \n",
       "397                    1                    0                    0   \n",
       "398                    0                    0                    0   \n",
       "399                    0                    0                    0   \n",
       "400                    1                    0                    0   \n",
       "401                    0                    0                    0   \n",
       "402                    1                    0                    0   \n",
       "403                    0                    0                    0   \n",
       "404                    0                    0                    0   \n",
       "405                    0                    0                    0   \n",
       "406                    0                    0                    0   \n",
       "407                    0                    0                    0   \n",
       "408                    0                    0                    1   \n",
       "409                    0                    0                    1   \n",
       "410                    0                    0                    1   \n",
       "411                    1                    0                    0   \n",
       "412                    0                    0                    1   \n",
       "413                    0                    0                    0   \n",
       "414                    1                    0                    0   \n",
       "415                    0                    0                    0   \n",
       "416                    0                    0                    0   \n",
       "417                    0                    0                    0   \n",
       "\n",
       "     Sex_Pclass_male_1  Sex_Pclass_male_2  Sex_Pclass_male_3  Age_scaled  \\\n",
       "0                    0                  0                  1    0.307495   \n",
       "1                    0                  0                  0    1.256225   \n",
       "2                    0                  1                  0    2.394702   \n",
       "3                    0                  0                  1   -0.261743   \n",
       "4                    0                  0                  0   -0.641235   \n",
       "5                    0                  0                  1   -1.248423   \n",
       "6                    0                  0                  0   -0.034048   \n",
       "7                    0                  1                  0   -0.337642   \n",
       "8                    0                  0                  0   -0.944829   \n",
       "9                    0                  0                  1   -0.717134   \n",
       "10                   0                  0                  1   -0.189852   \n",
       "11                   1                  0                  0    1.180327   \n",
       "12                   0                  0                  0   -0.565337   \n",
       "13                   0                  1                  0    2.470600   \n",
       "14                   0                  0                  0    1.256225   \n",
       "15                   0                  0                  0   -0.489439   \n",
       "16                   0                  1                  0    0.345444   \n",
       "17                   0                  0                  1   -0.717134   \n",
       "18                   0                  0                  0   -0.261743   \n",
       "19                   0                  0                  0    1.104428   \n",
       "20                   1                  0                  0    1.863413   \n",
       "21                   0                  0                  1   -1.627915   \n",
       "22                   0                  0                  0    1.659573   \n",
       "23                   1                  0                  0   -0.717134   \n",
       "24                   0                  0                  0    1.332124   \n",
       "25                   0                  0                  1    1.483921   \n",
       "26                   0                  0                  0   -0.641235   \n",
       "27                   0                  0                  1   -0.603286   \n",
       "28                   1                  0                  0    0.800835   \n",
       "29                   0                  0                  1   -0.528784   \n",
       "..                 ...                ...                ...         ...   \n",
       "388                  0                  0                  1   -0.717134   \n",
       "389                  0                  0                  1   -1.855610   \n",
       "390                  1                  0                  0   -0.565337   \n",
       "391                  0                  0                  0    1.559819   \n",
       "392                  0                  0                  1   -1.324321   \n",
       "393                  0                  1                  0    1.256225   \n",
       "394                  0                  0                  1   -0.109946   \n",
       "395                  0                  0                  0   -0.944829   \n",
       "396                  0                  0                  1   -0.489439   \n",
       "397                  0                  0                  0    1.332124   \n",
       "398                  0                  0                  1   -0.641235   \n",
       "399                  0                  0                  1    0.041850   \n",
       "400                  0                  0                  0   -0.034048   \n",
       "401                  0                  1                  0    0.573139   \n",
       "402                  0                  0                  0   -0.641235   \n",
       "403                  1                  0                  0   -1.020728   \n",
       "404                  1                  0                  0    0.952632   \n",
       "405                  0                  1                  0   -0.793032   \n",
       "406                  0                  1                  0   -0.565337   \n",
       "407                  1                  0                  0    1.483921   \n",
       "408                  0                  0                  0   -0.800957   \n",
       "409                  0                  0                  0   -2.083305   \n",
       "410                  0                  0                  0    0.367897   \n",
       "411                  0                  0                  0    0.497241   \n",
       "412                  0                  0                  0   -0.185845   \n",
       "413                  0                  0                  1    0.019516   \n",
       "414                  0                  0                  0    0.649038   \n",
       "415                  0                  0                  1    0.611089   \n",
       "416                  0                  0                  1    0.019516   \n",
       "417                  0                  0                  1   -0.358157   \n",
       "\n",
       "     Fare_scaled  \n",
       "0      -0.496637  \n",
       "1      -0.511497  \n",
       "2      -0.463335  \n",
       "3      -0.481704  \n",
       "4      -0.416740  \n",
       "5      -0.471623  \n",
       "6      -0.500221  \n",
       "7      -0.117238  \n",
       "8      -0.507390  \n",
       "9      -0.204154  \n",
       "10     -0.495444  \n",
       "11     -0.171000  \n",
       "12      0.837349  \n",
       "13     -0.171000  \n",
       "14      0.459367  \n",
       "15     -0.140162  \n",
       "16     -0.415620  \n",
       "17     -0.507465  \n",
       "18     -0.494920  \n",
       "19     -0.507465  \n",
       "20      0.427557  \n",
       "21     -0.580120  \n",
       "22     -0.069151  \n",
       "23      0.463026  \n",
       "24      4.065049  \n",
       "25     -0.377090  \n",
       "26      0.473779  \n",
       "27     -0.507465  \n",
       "28     -0.090356  \n",
       "29     -0.248433  \n",
       "..           ...  \n",
       "388    -0.498056  \n",
       "389    -0.259261  \n",
       "390     1.038659  \n",
       "391     0.069140  \n",
       "392    -0.274045  \n",
       "393    -0.448774  \n",
       "394    -0.242236  \n",
       "395     0.438310  \n",
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       "397     0.782391  \n",
       "398    -0.497608  \n",
       "399    -0.498356  \n",
       "400     2.317614  \n",
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       "402     0.427557  \n",
       "403     0.207130  \n",
       "404    -0.140162  \n",
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       "406    -0.448774  \n",
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       "410    -0.498056  \n",
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       "417    -0.236263  \n",
       "\n",
       "[418 rows x 23 columns]"
      ]
     },
     "execution_count": 543,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test = pd.read_csv(\"/Users/MLS/Downloads/test.csv\")\n",
    "data_test.loc[ (data_test.Fare.isnull()), 'Fare' ] = 0\n",
    "data_test['Sex_Pclass'] = data_test.Sex + \"_\" + data_test.Pclass.map(str)\n",
    "# 接着我们对test_data做和train_data中一致的特征变换\n",
    "# 首先用同样的RandomForestRegressor模型填上丢失的年龄\n",
    "tmp_df = data_test[['Age','Fare', 'Parch', 'SibSp', 'Pclass']]\n",
    "null_age = tmp_df[data_test.Age.isnull()].as_matrix()\n",
    "# 根据特征属性X预测年龄并补上\n",
    "X = null_age[:, 1:]\n",
    "predictedAges = rfr.predict(X)\n",
    "data_test.loc[ (data_test.Age.isnull()), 'Age' ] = predictedAges\n",
    "\n",
    "data_test = set_Cabin_type(data_test)\n",
    "dummies_Cabin = pd.get_dummies(data_test['Cabin'], prefix= 'Cabin')\n",
    "dummies_Embarked = pd.get_dummies(data_test['Embarked'], prefix= 'Embarked')\n",
    "dummies_Sex = pd.get_dummies(data_test['Sex'], prefix= 'Sex')\n",
    "dummies_Pclass = pd.get_dummies(data_test['Pclass'], prefix= 'Pclass')\n",
    "dummies_Sex_Pclass = pd.get_dummies(data_test['Sex_Pclass'], prefix= 'Sex_Pclass')\n",
    "\n",
    "\n",
    "df_test = pd.concat([data_test, dummies_Cabin, dummies_Embarked, dummies_Sex, dummies_Pclass, dummies_Sex_Pclass], axis=1)\n",
    "df_test.drop(['Pclass', 'Name', 'Sex', 'Ticket', 'Cabin', 'Embarked', 'Sex_Pclass'], axis=1, inplace=True)\n",
    "df_test['Age_scaled'] = scaler.fit_transform(df_test['Age'], age_scale_param)\n",
    "df_test['Fare_scaled'] = scaler.fit_transform(df_test['Fare'], fare_scale_param)\n",
    "df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 545,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test = df_test.filter(regex='Age_.*|SibSp|Parch|Fare_.*|Cabin_.*|Embarked_.*|Sex_.*|Pclass.*')\n",
    "predictions = clf.predict(test)\n",
    "result = pd.DataFrame({'PassengerId':data_test['PassengerId'].as_matrix(), 'Survived':predictions.astype(np.int32)})\n",
    "result.to_csv(\"/Users/MLS/Downloads/logistic_regression_predictions2.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 550,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.        ,  1.        ,  0.        , ...,  1.        ,\n",
       "        -0.56141667, -0.50244517],\n",
       "       [ 1.        ,  1.        ,  0.        , ...,  0.        ,\n",
       "         0.61317731,  0.78684529],\n",
       "       [ 1.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "        -0.26776817, -0.48885426],\n",
       "       ..., \n",
       "       [ 0.        ,  1.        ,  2.        , ...,  0.        ,\n",
       "        -0.98482995, -0.17626324],\n",
       "       [ 1.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "        -0.26776817, -0.04438104],\n",
       "       [ 0.        ,  0.        ,  0.        , ...,  1.        ,\n",
       "         0.17270457, -0.49237783]])"
      ]
     },
     "execution_count": 550,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import BaggingRegressor\n",
    "\n",
    "train_df = df.filter(regex='Survived|Age_.*|SibSp|Parch|Fare_.*|Cabin_.*|Embarked_.*|Sex_.*|Pclass.*|Mother|Child|Family|Title')\n",
    "train_np = train_df.as_matrix()\n",
    "\n",
    "# y即Survival结果\n",
    "y = train_np[:, 0]\n",
    "\n",
    "# X即特征属性值\n",
    "X = train_np[:, 1:]\n",
    "\n",
    "# fit到BaggingRegressor之中\n",
    "clf = linear_model.LogisticRegression(C=1.0, penalty='l1', tol=1e-6)\n",
    "bagging_clf = BaggingRegressor(clf, n_estimators=10, max_samples=0.8, max_features=1.0, bootstrap=True, bootstrap_features=False, n_jobs=-1)\n",
    "bagging_clf.fit(X, y)\n",
    "\n",
    "test = df_test.filter(regex='Age_.*|SibSp|Parch|Fare_.*|Cabin_.*|Embarked_.*|Sex_.*|Pclass.*|Mother|Child|Family|Title')\n",
    "predictions = bagging_clf.predict(test)\n",
    "result = pd.DataFrame({'PassengerId':data_test['PassengerId'].as_matrix(), 'Survived':predictions.astype(np.int32)})\n",
    "result.to_csv(\"/Users/MLS/Downloads/logistic_regression_predictions2.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 581,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/MLS/Downloads/train.csv 0\n",
      "(891,) (891, 12)\n",
      "Fitting 10 folds for each of 1 candidates, totalling 10 fits\n",
      "[CV]  ................................................................\n",
      "[CV] ....................................... , score=0.860140 -   0.4s\n",
      "[CV]  ................................................................\n",
      "[CV] ....................................... , score=0.832168 -   0.4s\n",
      "[CV]  ................................................................\n",
      "[CV] ....................................... , score=0.818182 -   0.4s\n",
      "[CV]  ................................................................\n",
      "[CV] ....................................... , score=0.839161 -   0.4s\n",
      "[CV]  ................................................................\n",
      "[CV] ....................................... , score=0.811189 -   0.5s\n",
      "[CV]  ................................................................\n",
      "[CV] ....................................... , score=0.874126 -   0.4s\n",
      "[CV]  ................................................................\n",
      "[CV] ....................................... , score=0.811189 -   0.4s\n",
      "[CV]  ................................................................\n",
      "[CV] ....................................... , score=0.783217 -   0.4s\n",
      "[CV]  ................................................................\n",
      "[CV] ....................................... , score=0.825175 -   0.4s\n",
      "[CV]  ................................................................\n",
      "[CV] ....................................... , score=0.839161 -   0.4s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 jobs       | elapsed:    0.4s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    4.1s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Best score: 0.829\n",
      "Pipeline(steps=[('clf', RandomForestClassifier(bootstrap=False, class_weight=None,\n",
      "            criterion='entropy', max_depth=5, max_features='auto',\n",
      "            max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=1,\n",
      "            min_weight_fraction_leaf=0.0, n_estimators=500, n_jobs=1,\n",
      "            oob_score=False, random_state=0, verbose=0, warm_start=False))])\n",
      "Model with rank: 1\n",
      "Mean validation score: 0.829 (std: 0.025)\n",
      "Parameters: {}\n",
      "\n",
      "-----grid search end------------\n",
      "on all train set\n",
      "0.826038159371 [ 0.81144781  0.83501684  0.83164983]\n",
      "on test set\n",
      "0.782203389831 [ 0.76666667  0.78333333  0.79661017]\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       0.86      0.90      0.88       439\n",
      "        1.0       0.83      0.75      0.79       273\n",
      "\n",
      "avg / total       0.85      0.85      0.85       712\n",
      "\n",
      "test data\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       0.86      0.87      0.86       110\n",
      "        1.0       0.79      0.77      0.78        69\n",
      "\n",
      "avg / total       0.83      0.83      0.83       179\n",
      "\n"
     ]
    },
    {
     "data": {
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       " '/Users/MLS/Downloads/model-rf.pkl_989.npy',\n",
       " '/Users/MLS/Downloads/model-rf.pkl_990.npy',\n",
       " '/Users/MLS/Downloads/model-rf.pkl_991.npy',\n",
       " '/Users/MLS/Downloads/model-rf.pkl_992.npy',\n",
       " '/Users/MLS/Downloads/model-rf.pkl_993.npy',\n",
       " '/Users/MLS/Downloads/model-rf.pkl_994.npy',\n",
       " '/Users/MLS/Downloads/model-rf.pkl_995.npy',\n",
       " '/Users/MLS/Downloads/model-rf.pkl_996.npy',\n",
       " '/Users/MLS/Downloads/model-rf.pkl_997.npy',\n",
       " '/Users/MLS/Downloads/model-rf.pkl_998.npy',\n",
       " '/Users/MLS/Downloads/model-rf.pkl_999.npy',\n",
       " ...]"
      ]
     },
     "execution_count": 581,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Titatic competitor usign pandas and scikit library\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas import  DataFrame\n",
    "from patsy import dmatrices\n",
    "import string\n",
    "from operator import itemgetter\n",
    "#json library for settings file\n",
    "import json\n",
    "# import the machine learning library that holds the randomforest\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.cross_validation import cross_val_score\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "from sklearn.cross_validation import train_test_split,StratifiedShuffleSplit,StratifiedKFold\n",
    "from sklearn import preprocessing\n",
    "from sklearn.metrics import classification_report\n",
    "#joblib library for serialization\n",
    "from sklearn.externals import joblib\n",
    "\n",
    "##Read configuration parameters\n",
    "\n",
    "train_file=\"/Users/Hanxiaoyang/Downloads/train.csv\"\n",
    "MODEL_PATH=\"/Users/Hanxiaoyang/Downloads/\"\n",
    "test_file=\"/Users/Hanxiaoyang/Downloads/test.csv\"\n",
    "SUBMISSION_PATH=\"/Users/Hanxiaoyang/Downloads/\"\n",
    "seed= 0\n",
    "\n",
    "print train_file,seed\n",
    "\n",
    "# Utility function to report best scores\n",
    "def report(grid_scores, n_top=3):\n",
    "    top_scores = sorted(grid_scores, key=itemgetter(1), reverse=True)[:n_top]\n",
    "    for i, score in enumerate(top_scores):\n",
    "        print(\"Model with rank: {0}\".format(i + 1))\n",
    "        print(\"Mean validation score: {0:.3f} (std: {1:.3f})\".format(\n",
    "              score.mean_validation_score,\n",
    "              np.std(score.cv_validation_scores)))\n",
    "        print(\"Parameters: {0}\".format(score.parameters))\n",
    "        print(\"\")\n",
    "\n",
    "###utility to clean and munge data\n",
    "def substrings_in_string(big_string, substrings):\n",
    "    for substring in substrings:\n",
    "        if string.find(big_string, substring) != -1:\n",
    "            return substring\n",
    "    print big_string\n",
    "    return np.nan\n",
    "\n",
    "le = preprocessing.LabelEncoder()\n",
    "enc=preprocessing.OneHotEncoder()\n",
    "\n",
    "def clean_and_munge_data(df):\n",
    "    #setting silly values to nan\n",
    "    df.Fare = df.Fare.map(lambda x: np.nan if x==0 else x)\n",
    "    #creating a title column from name\n",
    "    title_list=['Mrs', 'Mr', 'Master', 'Miss', 'Major', 'Rev',\n",
    "                'Dr', 'Ms', 'Mlle','Col', 'Capt', 'Mme', 'Countess',\n",
    "                'Don', 'Jonkheer']\n",
    "    df['Title']=df['Name'].map(lambda x: substrings_in_string(x, title_list))\n",
    "\n",
    "    #replacing all titles with mr, mrs, miss, master\n",
    "    def replace_titles(x):\n",
    "        title=x['Title']\n",
    "        if title in ['Mr','Don', 'Major', 'Capt', 'Jonkheer', 'Rev', 'Col']:\n",
    "            return 'Mr'\n",
    "        elif title in ['Master']:\n",
    "            return 'Master'\n",
    "        elif title in ['Countess', 'Mme','Mrs']:\n",
    "            return 'Mrs'\n",
    "        elif title in ['Mlle', 'Ms','Miss']:\n",
    "            return 'Miss'\n",
    "        elif title =='Dr':\n",
    "            if x['Sex']=='Male':\n",
    "                return 'Mr'\n",
    "            else:\n",
    "                return 'Mrs'\n",
    "        elif title =='':\n",
    "            if x['Sex']=='Male':\n",
    "                return 'Master'\n",
    "            else:\n",
    "                return 'Miss'\n",
    "        else:\n",
    "            return title\n",
    "\n",
    "    df['Title']=df.apply(replace_titles, axis=1)\n",
    "\n",
    "    #Creating new family_size column\n",
    "    df['Family_Size']=df['SibSp']+df['Parch']\n",
    "    df['Family']=df['SibSp']*df['Parch']\n",
    "\n",
    "\n",
    "    #imputing nan values\n",
    "    df.loc[ (df.Fare.isnull())&(df.Pclass==1),'Fare'] =np.median(df[df['Pclass'] == 1]['Fare'].dropna())\n",
    "    df.loc[ (df.Fare.isnull())&(df.Pclass==2),'Fare'] =np.median( df[df['Pclass'] == 2]['Fare'].dropna())\n",
    "    df.loc[ (df.Fare.isnull())&(df.Pclass==3),'Fare'] = np.median(df[df['Pclass'] == 3]['Fare'].dropna())\n",
    "\n",
    "    df['Gender'] = df['Sex'].map( {'female': 0, 'male': 1} ).astype(int)\n",
    "\n",
    "    df['AgeFill']=df['Age']\n",
    "    mean_ages = np.zeros(4)\n",
    "    mean_ages[0]=np.average(df[df['Title'] == 'Miss']['Age'].dropna())\n",
    "    mean_ages[1]=np.average(df[df['Title'] == 'Mrs']['Age'].dropna())\n",
    "    mean_ages[2]=np.average(df[df['Title'] == 'Mr']['Age'].dropna())\n",
    "    mean_ages[3]=np.average(df[df['Title'] == 'Master']['Age'].dropna())\n",
    "    df.loc[ (df.Age.isnull()) & (df.Title == 'Miss') ,'AgeFill'] = mean_ages[0]\n",
    "    df.loc[ (df.Age.isnull()) & (df.Title == 'Mrs') ,'AgeFill'] = mean_ages[1]\n",
    "    df.loc[ (df.Age.isnull()) & (df.Title == 'Mr') ,'AgeFill'] = mean_ages[2]\n",
    "    df.loc[ (df.Age.isnull()) & (df.Title == 'Master') ,'AgeFill'] = mean_ages[3]\n",
    "\n",
    "    df['AgeCat']=df['AgeFill']\n",
    "    df.loc[ (df.AgeFill<=10) ,'AgeCat'] = 'child'\n",
    "    df.loc[ (df.AgeFill>60),'AgeCat'] = 'aged'\n",
    "    df.loc[ (df.AgeFill>10) & (df.AgeFill <=30) ,'AgeCat'] = 'adult'\n",
    "    df.loc[ (df.AgeFill>30) & (df.AgeFill <=60) ,'AgeCat'] = 'senior'\n",
    "\n",
    "    df.Embarked = df.Embarked.fillna('S')\n",
    "\n",
    "\n",
    "    #Special case for cabins as nan may be signal\n",
    "    df.loc[ df.Cabin.isnull()==True,'Cabin'] = 0.5\n",
    "    df.loc[ df.Cabin.isnull()==False,'Cabin'] = 1.5\n",
    "   #Fare per person\n",
    "\n",
    "    df['Fare_Per_Person']=df['Fare']/(df['Family_Size']+1)\n",
    "\n",
    "    #Age times class\n",
    "\n",
    "    df['AgeClass']=df['AgeFill']*df['Pclass']\n",
    "    df['ClassFare']=df['Pclass']*df['Fare_Per_Person']\n",
    "\n",
    "\n",
    "    df['HighLow']=df['Pclass']\n",
    "    df.loc[ (df.Fare_Per_Person<8) ,'HighLow'] = 'Low'\n",
    "    df.loc[ (df.Fare_Per_Person>=8) ,'HighLow'] = 'High'\n",
    "\n",
    "\n",
    "\n",
    "    le.fit(df['Sex'] )\n",
    "    x_sex=le.transform(df['Sex'])\n",
    "    df['Sex']=x_sex.astype(np.float)\n",
    "\n",
    "    le.fit( df['Ticket'])\n",
    "    x_Ticket=le.transform( df['Ticket'])\n",
    "    df['Ticket']=x_Ticket.astype(np.float)\n",
    "\n",
    "    le.fit(df['Title'])\n",
    "    x_title=le.transform(df['Title'])\n",
    "    df['Title'] =x_title.astype(np.float)\n",
    "\n",
    "    le.fit(df['HighLow'])\n",
    "    x_hl=le.transform(df['HighLow'])\n",
    "    df['HighLow']=x_hl.astype(np.float)\n",
    "\n",
    "\n",
    "    le.fit(df['AgeCat'])\n",
    "    x_age=le.transform(df['AgeCat'])\n",
    "    df['AgeCat'] =x_age.astype(np.float)\n",
    "\n",
    "    le.fit(df['Embarked'])\n",
    "    x_emb=le.transform(df['Embarked'])\n",
    "    df['Embarked']=x_emb.astype(np.float)\n",
    "\n",
    "    df = df.drop(['PassengerId','Name','Age','Cabin'], axis=1) #remove Name,Age and PassengerId\n",
    "\n",
    "\n",
    "    return df\n",
    "\n",
    "#read data\n",
    "traindf=pd.read_csv(train_file)\n",
    "##clean data\n",
    "df=clean_and_munge_data(traindf)\n",
    "########################################formula################################\n",
    " \n",
    "formula_ml='Survived~Pclass+C(Title)+Sex+C(AgeCat)+Fare_Per_Person+Fare+Family_Size' \n",
    "\n",
    "y_train, x_train = dmatrices(formula_ml, data=df, return_type='dataframe')\n",
    "y_train = np.asarray(y_train).ravel()\n",
    "print y_train.shape,x_train.shape\n",
    "\n",
    "##select a train and test set\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(x_train, y_train, test_size=0.2,random_state=seed)\n",
    "#instantiate and fit our model\n",
    "\n",
    "clf=RandomForestClassifier(n_estimators=500, criterion='entropy', max_depth=5, min_samples_split=1,\n",
    "  min_samples_leaf=1, max_features='auto',    bootstrap=False, oob_score=False, n_jobs=1, random_state=seed,\n",
    "  verbose=0)\n",
    "\n",
    "###compute grid search to find best paramters for pipeline\n",
    "param_grid = dict( )\n",
    "##classify pipeline\n",
    "pipeline=Pipeline([ ('clf',clf) ])\n",
    "grid_search = GridSearchCV(pipeline, param_grid=param_grid, verbose=3,scoring='accuracy',\\\n",
    "cv=StratifiedShuffleSplit(Y_train, n_iter=10, test_size=0.2, train_size=None, indices=None, \\\n",
    "random_state=seed, n_iterations=None)).fit(X_train, Y_train)\n",
    "# Score the results\n",
    "###print result\n",
    "print(\"Best score: %0.3f\" % grid_search.best_score_)\n",
    "print(grid_search.best_estimator_)\n",
    "report(grid_search.grid_scores_)\n",
    " \n",
    "print('-----grid search end------------')\n",
    "print ('on all train set')\n",
    "scores = cross_val_score(grid_search.best_estimator_, x_train, y_train,cv=3,scoring='accuracy')\n",
    "print scores.mean(),scores\n",
    "print ('on test set')\n",
    "scores = cross_val_score(grid_search.best_estimator_, X_test, Y_test,cv=3,scoring='accuracy')\n",
    "print scores.mean(),scores\n",
    "\n",
    "# Score the results\n",
    "\n",
    "print(classification_report(Y_train, grid_search.best_estimator_.predict(X_train) ))\n",
    "print('test data')\n",
    "print(classification_report(Y_test, grid_search.best_estimator_.predict(X_test) ))\n",
    "\n",
    "#serialize training\n",
    "model_file=MODEL_PATH+'model-rf.pkl'\n",
    "joblib.dump(grid_search.best_estimator_, model_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 583,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/MLS/Downloads/test.csv 0\n",
      "(418, 18)\n",
      "(418,) (418, 12)\n",
      "(418,)\n"
     ]
    }
   ],
   "source": [
    "#Titatic competitor usign pandas and scikit library\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas import  DataFrame\n",
    "from patsy import dmatrices\n",
    "import string\n",
    "from operator import itemgetter\n",
    "#json library for settings file\n",
    "import json\n",
    "# import the machine learning library that holds the randomforest\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.cross_validation import cross_val_score\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "from sklearn.cross_validation import train_test_split,StratifiedShuffleSplit,StratifiedKFold\n",
    "from sklearn import preprocessing\n",
    "#joblib library for serialization\n",
    "from sklearn.externals import joblib\n",
    "\n",
    "#Read data and configuration parameters#\n",
    "\n",
    "train_file=\"/Users/Hanxiaoyang/Downloads/train.csv\"\n",
    "MODEL_PATH=\"/Users/Hanxiaoyang/Downloads/\"\n",
    "test_file=\"/Users/Hanxiaoyang/Downloads/test.csv\"\n",
    "SUBMISSION_PATH=\"/Users/Hanxiaoyang/Downloads/\"\n",
    "seed= 0\n",
    "\n",
    "\n",
    "print test_file, seed\n",
    "# Utility function to report best scores\n",
    "def report(grid_scores, n_top=3):\n",
    "    top_scores = sorted(grid_scores, key=itemgetter(1), reverse=True)[:n_top]\n",
    "    for i, score in enumerate(top_scores):\n",
    "        print(\"Model with rank: {0}\".format(i + 1))\n",
    "        print(\"Mean validation score: {0:.3f} (std: {1:.3f})\".format(\n",
    "              score.mean_validation_score,\n",
    "              np.std(score.cv_validation_scores)))\n",
    "        print(\"Parameters: {0}\".format(score.parameters))\n",
    "        print(\"\")\n",
    "\n",
    "###utility to clean and munge data\n",
    "def substrings_in_string(big_string, substrings):\n",
    "    for substring in substrings:\n",
    "        if string.find(big_string, substring) != -1:\n",
    "            return substring\n",
    "    print big_string\n",
    "    return np.nan\n",
    "\n",
    "le = preprocessing.LabelEncoder()\n",
    "enc=preprocessing.OneHotEncoder()\n",
    "\n",
    "def clean_and_munge_data(df):\n",
    "    #setting silly values to nan\n",
    "    df.Fare = df.Fare.map(lambda x: np.nan if x==0 else x)\n",
    "    #creating a title column from name\n",
    "    title_list=['Mrs', 'Mr', 'Master', 'Miss', 'Major', 'Rev',\n",
    "                'Dr', 'Ms', 'Mlle','Col', 'Capt', 'Mme', 'Countess',\n",
    "                'Don', 'Jonkheer']\n",
    "    df['Title']=df['Name'].map(lambda x: substrings_in_string(x, title_list))\n",
    "\n",
    "    #replacing all titles with mr, mrs, miss, master\n",
    "    def replace_titles(x):\n",
    "        title=x['Title']\n",
    "        if title in ['Mr','Don', 'Major', 'Capt', 'Jonkheer', 'Rev', 'Col']:\n",
    "            return 'Mr'\n",
    "        elif title in ['Master']:\n",
    "            return 'Master'\n",
    "        elif title in ['Countess', 'Mme','Mrs']:\n",
    "            return 'Mrs'\n",
    "        elif title in ['Mlle', 'Ms','Miss']:\n",
    "            return 'Miss'\n",
    "        elif title =='Dr':\n",
    "            if x['Sex']=='Male':\n",
    "                return 'Mr'\n",
    "            else:\n",
    "                return 'Mrs'\n",
    "        elif title =='':\n",
    "            if x['Sex']=='Male':\n",
    "                return 'Master'\n",
    "            else:\n",
    "                return 'Miss'\n",
    "        else:\n",
    "            return title\n",
    "\n",
    "    df['Title']=df.apply(replace_titles, axis=1)\n",
    "\n",
    "    #Creating new family_size column\n",
    "    df['Family_Size']=df['SibSp']+df['Parch']\n",
    "    df['Family']=df['SibSp']*df['Parch']\n",
    "\n",
    "\n",
    "    #imputing nan values\n",
    "    df.loc[ (df.Fare.isnull())&(df.Pclass==1),'Fare'] =np.median(df[df['Pclass'] == 1]['Fare'].dropna())\n",
    "    df.loc[ (df.Fare.isnull())&(df.Pclass==2),'Fare'] =np.median( df[df['Pclass'] == 2]['Fare'].dropna())\n",
    "    df.loc[ (df.Fare.isnull())&(df.Pclass==3),'Fare'] = np.median(df[df['Pclass'] == 3]['Fare'].dropna())\n",
    "\n",
    "    df['Gender'] = df['Sex'].map( {'female': 0, 'male': 1} ).astype(int)\n",
    "\n",
    "    df['AgeFill']=df['Age']\n",
    "    mean_ages = np.zeros(4)\n",
    "    mean_ages[0]=np.average(df[df['Title'] == 'Miss']['Age'].dropna())\n",
    "    mean_ages[1]=np.average(df[df['Title'] == 'Mrs']['Age'].dropna())\n",
    "    mean_ages[2]=np.average(df[df['Title'] == 'Mr']['Age'].dropna())\n",
    "    mean_ages[3]=np.average(df[df['Title'] == 'Master']['Age'].dropna())\n",
    "    df.loc[ (df.Age.isnull()) & (df.Title == 'Miss') ,'AgeFill'] = mean_ages[0]\n",
    "    df.loc[ (df.Age.isnull()) & (df.Title == 'Mrs') ,'AgeFill'] = mean_ages[1]\n",
    "    df.loc[ (df.Age.isnull()) & (df.Title == 'Mr') ,'AgeFill'] = mean_ages[2]\n",
    "    df.loc[ (df.Age.isnull()) & (df.Title == 'Master') ,'AgeFill'] = mean_ages[3]\n",
    "\n",
    "    df['AgeCat']=df['AgeFill']\n",
    "    df.loc[ (df.AgeFill<=10) ,'AgeCat'] = 'child'\n",
    "    df.loc[ (df.AgeFill>60),'AgeCat'] = 'aged'\n",
    "    df.loc[ (df.AgeFill>10) & (df.AgeFill <=30) ,'AgeCat'] = 'adult'\n",
    "    df.loc[ (df.AgeFill>30) & (df.AgeFill <=60) ,'AgeCat'] = 'senior'\n",
    "\n",
    "    df.Embarked = df.Embarked.fillna('S')\n",
    "\n",
    "\n",
    "    #Special case for cabins as nan may be signal\n",
    "    df.loc[ df.Cabin.isnull()==True,'Cabin'] = 0.5\n",
    "    df.loc[ df.Cabin.isnull()==False,'Cabin'] = 1.5\n",
    "   #Fare per person\n",
    "\n",
    "    df['Fare_Per_Person']=df['Fare']/(df['Family_Size']+1)\n",
    "\n",
    "    #Age times class\n",
    "\n",
    "    df['AgeClass']=df['AgeFill']*df['Pclass']\n",
    "    df['ClassFare']=df['Pclass']*df['Fare_Per_Person']\n",
    "\n",
    "\n",
    "    df['HighLow']=df['Pclass']\n",
    "    df.loc[ (df.Fare_Per_Person<8) ,'HighLow'] = 'Low'\n",
    "    df.loc[ (df.Fare_Per_Person>=8) ,'HighLow'] = 'High'\n",
    "\n",
    "\n",
    "\n",
    "    le.fit(df['Sex'] )\n",
    "    x_sex=le.transform(df['Sex'])\n",
    "    df['Sex']=x_sex.astype(np.float)\n",
    "\n",
    "    le.fit( df['Ticket'])\n",
    "    x_Ticket=le.transform( df['Ticket'])\n",
    "    df['Ticket']=x_Ticket.astype(np.float)\n",
    "\n",
    "    le.fit(df['Title'])\n",
    "    x_title=le.transform(df['Title'])\n",
    "    df['Title'] =x_title.astype(np.float)\n",
    "\n",
    "    le.fit(df['HighLow'])\n",
    "    x_hl=le.transform(df['HighLow'])\n",
    "    df['HighLow']=x_hl.astype(np.float)\n",
    "\n",
    "\n",
    "    le.fit(df['AgeCat'])\n",
    "    x_age=le.transform(df['AgeCat'])\n",
    "    df['AgeCat'] =x_age.astype(np.float)\n",
    "\n",
    "    le.fit(df['Embarked'])\n",
    "    x_emb=le.transform(df['Embarked'])\n",
    "    df['Embarked']=x_emb.astype(np.float)\n",
    "\n",
    "    df = df.drop(['PassengerId','Name','Age','Cabin'], axis=1) #remove Name,Age and PassengerId\n",
    "\n",
    "\n",
    "    return df\n",
    "\n",
    "#read data\n",
    "\n",
    "testdf=pd.read_csv(test_file)\n",
    "\n",
    "ID=testdf['PassengerId']\n",
    "##clean data\n",
    "df_test=clean_and_munge_data(testdf)\n",
    "df_test['Survived'] =  [0 for x in range(len(df_test))]\n",
    "\n",
    "print df_test.shape\n",
    "########################################formula################################\n",
    " \n",
    "formula_ml='Survived~Pclass+C(Title)+Sex+C(AgeCat)+Fare_Per_Person+Fare+Family_Size' \n",
    "\n",
    "y_p,x_test = dmatrices(formula_ml, data=df_test, return_type='dataframe')\n",
    "y_p = np.asarray(y_p).ravel()\n",
    "print  y_p.shape,x_test.shape\n",
    "#serialize training\n",
    "model_file=MODEL_PATH+'model-rf.pkl'\n",
    "clf = joblib.load(model_file)\n",
    "####estimate prediction on test data set\n",
    "y_p=clf.predict(x_test).astype(int)\n",
    "print y_p.shape\n",
    "\n",
    "outfile=SUBMISSION_PATH+'prediction-BS.csv'\n",
    "dfjo = DataFrame(dict(Survived=y_p,PassengerId=ID), columns=['PassengerId','Survived'])\n",
    "dfjo.to_csv(outfile,index_label=None,index_col=False,index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "collapsed": true
   },
   "outputs": [],
   "source": []
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