kaggle_titanic.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data analysis and wrangling\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random as rnd\n",
    "\n",
    "# visualization\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.display import Image\n",
    "\n",
    "# machine learning\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC, LinearSVC\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = pd.read_csv('./train.csv')\n",
    "test_df = pd.read_csv('./test.csv')\n",
    "combine = [train_df, test_df]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['PassengerId' 'Survived' 'Pclass' 'Name' 'Sex' 'Age' 'SibSp' 'Parch'\n",
      " 'Ticket' 'Fare' 'Cabin' 'Embarked']\n"
     ]
    }
   ],
   "source": [
    "print(train_df.columns.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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",
       "    <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>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>NaN</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>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>E46</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>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</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",
       "5            6         0       3   \n",
       "6            7         0       1   \n",
       "7            8         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",
       "5                                   Moran, Mr. James    male   NaN      0   \n",
       "6                            McCarthy, Mr. Timothy J    male  54.0      0   \n",
       "7                     Palsson, Master. Gosta Leonard    male   2.0      3   \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  \n",
       "5      0            330877   8.4583   NaN        Q  \n",
       "6      0             17463  51.8625   E46        S  \n",
       "7      1            349909  21.0750   NaN        S  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head(8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# information regarding the datatypes\n",
    "\n",
    "from the source: https://www.kaggle.com/c/titanic/data\n",
    "\n",
    "![details of column](./image_1.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# understanding the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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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>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>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>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.00</td>\n",
       "      <td>B42</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>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.45</td>\n",
       "      <td>NaN</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>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.00</td>\n",
       "      <td>C148</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>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                                      Name  \\\n",
       "887          888         1       1              Graham, Miss. Margaret Edith   \n",
       "888          889         0       3  Johnston, Miss. Catherine Helen \"Carrie\"   \n",
       "889          890         1       1                     Behr, Mr. Karl Howell   \n",
       "890          891         0       3                       Dooley, Mr. Patrick   \n",
       "\n",
       "        Sex   Age  SibSp  Parch      Ticket   Fare Cabin Embarked  \n",
       "887  female  19.0      0      0      112053  30.00   B42        S  \n",
       "888  female   NaN      1      2  W./C. 6607  23.45   NaN        S  \n",
       "889    male  26.0      0      0      111369  30.00  C148        C  \n",
       "890    male  32.0      0      0      370376   7.75   NaN        Q  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.tail(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n",
      "_____________________________________________\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 11 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  418 non-null    int64  \n",
      " 1   Pclass       418 non-null    int64  \n",
      " 2   Name         418 non-null    object \n",
      " 3   Sex          418 non-null    object \n",
      " 4   Age          332 non-null    float64\n",
      " 5   SibSp        418 non-null    int64  \n",
      " 6   Parch        418 non-null    int64  \n",
      " 7   Ticket       418 non-null    object \n",
      " 8   Fare         417 non-null    float64\n",
      " 9   Cabin        91 non-null     object \n",
      " 10  Embarked     418 non-null    object \n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 36.0+ KB\n"
     ]
    }
   ],
   "source": [
    "train_df.info()\n",
    "print('_'*45)\n",
    "test_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## gaining more insight with the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "        text-align: right;\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>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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891</td>\n",
       "      <td>891</td>\n",
       "      <td>891</td>\n",
       "      <td>204</td>\n",
       "      <td>889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>891</td>\n",
       "      <td>2</td>\n",
       "      <td>681</td>\n",
       "      <td>147</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Swift, Mrs. Frederick Joel (Margaret Welles Ba...</td>\n",
       "      <td>male</td>\n",
       "      <td>347082</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>577</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>644</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     Name   Sex  Ticket  \\\n",
       "count                                                 891   891     891   \n",
       "unique                                                891     2     681   \n",
       "top     Swift, Mrs. Frederick Joel (Margaret Welles Ba...  male  347082   \n",
       "freq                                                    1   577       7   \n",
       "\n",
       "              Cabin Embarked  \n",
       "count           204      889  \n",
       "unique          147        3  \n",
       "top     C23 C25 C27        S  \n",
       "freq              4      644  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.describe(include=['O'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df[['Pclass', 'Survived']];"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\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>Pclass</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.629630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.472826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.242363</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass  Survived\n",
       "0       1  0.629630\n",
       "1       2  0.472826\n",
       "2       3  0.242363"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[['Pclass', 'Survived']].groupby(['Pclass'], as_index=False).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\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>Sex</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>female</td>\n",
       "      <td>0.742038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>male</td>\n",
       "      <td>0.188908</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Sex  Survived\n",
       "0  female  0.742038\n",
       "1    male  0.188908"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[[\"Sex\", \"Survived\"]].groupby(['Sex'], as_index=False).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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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>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.345395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.535885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.464286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SibSp  Survived\n",
       "0      0  0.345395\n",
       "1      1  0.535885\n",
       "2      2  0.464286\n",
       "3      3  0.250000\n",
       "4      4  0.166667\n",
       "5      5  0.000000\n",
       "6      8  0.000000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[[\"SibSp\", \"Survived\"]].groupby(['SibSp'], as_index=False).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Parch</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.343658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.550847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Parch  Survived\n",
       "0      0  0.343658\n",
       "1      1  0.550847\n",
       "2      2  0.500000\n",
       "3      3  0.600000\n",
       "4      4  0.000000\n",
       "5      5  0.200000\n",
       "6      6  0.000000"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[[\"Parch\", \"Survived\"]].groupby(['Parch'], as_index=False).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importance of different column on deciding the survival probabilty\n",
    "\n",
    "Certainly, if a person belongs to higher class, is female by gender and has less siblings/spouse or parents/children, ther survival probabilty is high."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyze by visualizing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7ff072e9c0d0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(train_df, col='Survived', row='Sex')\n",
    "g.map(plt.hist, 'Age', bins=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clearly we see females of all ages has a good chance of survival, while, male of any ages has bad chance of survival."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MZ5D+GlgNLGtuK+c3HA2IeTzaefwZ4Nj5DmIYWQD0zxeBXwFI8tqmMt+U5KMTF0zy+0luae7/1O5vHElOa76FbEryhabtqCQ3N99Qbk+yrJcgq+pLVfVQM/sVYOkMN7EE2Dpue7c3cZ6Y5JpxffxQkjOa6fuTvD3JTcBbk9w8brnDkuzexvokK5L8YZL/OW6ZM5J8sJl+9bjn4292fxAl+a9Jvp7k88DxM+zT4yRZAvxSVX25OpfM/AhwSq/b1dAzj0coj5u+faWqtvVjW6PGAqAPkiwGXgzckeQo4FzgpKo6BjhrklWurKrfaO7fDJzZtL8deFHT/rKm7fXAB6pqObCCcUk77vHXNYk08fbaaUI/E/j7GXb3w8AlSW5Mcm6Sp3W53r9W1QlV9R5g7yRPb9pfAVw+YdkrgN8bN/8KYF2SI5rp45vn41HgVc0/63fQ+cB4ITDp8GOS35riefrSJIsfzGOf661Nm0aUedyVhZbH2oPF8x3AArdPktua6S8ClwB/AFxRVTsBqup7k6x3dJJ3AfsB+wLXNe3/CKxJcjlwZdP2ZeDcJEvpfOBsmbixqnrFTANP8lt0PjhOmMl6VXVdk/Qr6XxY3prk6C5WXTdu+nLg5cD5dD4IHhN/Ve1Icl+S44AtwDPpPDdvAJ4N3JLO7vh9gO3Ac4D1VbWj6ds64D9MEvuNwPIuuzrZ/v7qcl0tLObx6Oax9sACoDc/airYn0nnHT3dP4o1wClVtakZXjsRoKpen+Q5wEuA25Isr6qPJ9nQtF2X5HVV9bkJj7mOTnJN9P6q+sjExiS/BlwMvLiq/ncX/XyM5sPw48DHm+HC3wQe5LEjSk+YsNoPx02vAz6Z5MrO5h7/Ydgs83Lgn4BPV1U1z+3aqjpnQn9OoYt/zs2H5QWT3PUvVfW8CW1beeyw6lLggekeQwuSeTy6eaw9qSpvs7wBj0zSdhTwdeCpzfz+zd/zgLc00zuBA4G9gOuBNU37M8Zt51Y6Ve7TgTRtFwJv6jHmQ4F7gedNs9wa4MRJ2k8CnthMP5nO0OdvAIcA9wO/CPw7OgcpndEsdz9wwITt3AJ8FHjruLb1wIpm+inAfcCNwLFN25F0vkkcuPu5Bf49nf2Z/ww8tXlOvwh8qA+v7y3AcXRGA/4e+J35fs956//NPB7tPN7T69z2myMAfVZVdyV5N/D5JI/S+QA4Y8JifwZsoPNmv4NOAgK8rzk4KMANwCY6R+e+OslPgO/SOXq3F2+nk2B/1Qy/7aqqFTNY/9nAh5LsovNN4eKqugWgGfK8nU5y3zrNdtYB7wMOn+zOqnooyd3AkVV1c9N2d5L/AfxDkl8AfgK8oaq+kuQ8OsOs24CvAVMepTwDf0jnA3QfOgXATPezaoEyj0cnj9M5EPG/AE9MspVOX8/rdbujYHdFKj1GkjV0vtGsn+dQJM2Seaw98VcAkiS1kAWApvJ3dPb5SVq4zGNNyV0AkiS1kCMAkiS1kAWAJEktNKc/A1y5cmVde+21c/mQkqY2q6sbmsfS0JlVLs/pCMDOnTvn8uEkDYB5LI0GdwFIktRCFgCSJLWQBYAkSS1kASBJUgtZAEiS1EIWAJIktZAFgCRJLdR1AZBkUZJbk1zTzB+eZEOSLUnWJdl7cGFKkqR+mskIwFnA5nHz7wUuqKplwEPAmf0MTJIkDU5XBUCSpcBLgIub+QAnAVc0i6wFThlEgJIkqf+6HQG4EHgr8NNm/qnAw1W1q5nfChzc59gkSdKATFsAJPldYHtVfXV88ySL1hTrr06yMcnGHTt2zDJMSfPJPJZGTzcjAMcDL0tyP3AZnaH/C4H9kuy+muBS4IHJVq6qi6pqRVWtGBsb60PIkuaaeSyNnmkLgKo6p6qWVtVhwCuBz1XVq4AbgVObxVYBVw0sSkmS1Fe9nAfgbcB/T3IvnWMCLulPSJIkadAWT7/Iz1XVemB9M30fcGz/Q5IkSYPmmQAlSWohCwBJklrIAkCSpBayAJAkqYUsACRJaiELAEmSWsgCQJKkFrIAkCSphSwAJElqIQsASZJayAJAkqQWsgCQJKmFLAAkSWohCwBJklrIAkCSpBayAJAkqYWmLQCSPCHJzUk2JbkryTua9sOTbEiyJcm6JHsPPlxJktQP3YwA/BtwUlUdAywHViY5DngvcEFVLQMeAs4cXJiSJKmfpi0AquORZnav5lbAScAVTfta4JSBRChJkvquq2MAkixKchuwHbge+AbwcFXtahbZChw8mBAlSVK/dVUAVNWjVbUcWAocCxwx2WKTrZtkdZKNSTbu2LFj9pFKmjfmsTR6ZvQrgKp6GFgPHAfsl2Rxc9dS4IEp1rmoqlZU1YqxsbFeYpU0T8xjafR08yuAsST7NdP7AC8ANgM3Aqc2i60CrhpUkJIkqb8WT78IS4C1SRbRKRgur6prktwNXJbkXcCtwCUDjFOSJPXRtAVAVd0OPGuS9vvoHA8gSZIWGM8EKElSC1kASJLUQhYAkiS1kAWAJEktZAEgSVILWQBIktRCFgCSJLWQBYAkSS1kASBJUgtZAEiS1EIWAJIktZAFgCRJLWQBIElSC1kASJLUQhYAkiS1kAWAJEktZAEgSVILTVsAJDkkyY1JNie5K8lZTfv+Sa5PsqX5+5TBhytJkvqhmxGAXcCbq+oI4DjgDUmOBM4GbqiqZcANzbwkSVoApi0AqmpbVX2tmf4BsBk4GDgZWNssthY4ZVBBSpKk/prRMQBJDgOeBWwADqqqbdApEoAD+x2cJEkajK4LgCT7Ap8C3lRV35/BequTbEyycceOHbOJUdI8M4+l0dNVAZBkLzr//D9WVVc2zQ8mWdLcvwTYPtm6VXVRVa2oqhVjY2P9iFnSHDOPpdHTza8AAlwCbK6q94+762pgVTO9Criq/+FJkqRBWNzFMscDrwHuSHJb0/anwPnA5UnOBL4FnDaYECVJUr9NWwBU1U1Aprj7+f0NR5IkzQXPBChJUgtZAEiS1EIWAJIktZAFgCRJLWQBIElSC1kASJLUQhYAkiS1kAWAJEktZAEgSVILWQBIktRCFgCSJLWQBYAkSS1kASBJUgtZAEiS1EIWAJIktZAFgCRJLWQBIElSC01bACS5NMn2JHeOa9s/yfVJtjR/nzLYMCVJUj8t7mKZNcCHgI+MazsbuKGqzk9ydjP/tv6Hp91e+sGbprzvM288YQ4jkSSNgmlHAKrqC8D3JjSfDKxtptcCp/Q5LkmSNECzPQbgoKraBtD8PbB/IUmSpEHrZhdAT5KsBlYDHHrooYN+uKE3iKF8dw9o0Gabx3t6b4Lvz+n4/GmQZjsC8GCSJQDN3+1TLVhVF1XViqpaMTY2NsuHkzSfzGNp9Mx2BOBqYBVwfvP3qr5FJEkLhN/QtZB18zPATwBfBp6ZZGuSM+n8439hki3AC5t5SZK0QEw7AlBVp09x1/P7HIskSZojAz8IUMPLgwel+TXdLgRpkDwVsCRJLeQIwBAZxLcBv2FIUxv0QXzmn4aZIwCSJLWQBYAkSS3kLoBZcmhPkrSQOQIgSVILWQBIktRCFgCSJLWQBYAkSS3kQYCalGcJ1FzwYjq96fX58/lvN0cAJElqIQsASZJayF0A6qvZnh9htkONc/14mlvzPUS90M/3Md/xz/frpz1zBECSpBZyBEAz5kWLtFD4vurNfF8syRGCweppBCDJyiT3JLk3ydn9CkqSJA3WrAuAJIuADwMvBo4ETk9yZL8CkyRJg9PLLoBjgXur6j6AJJcBJwN39yMwaZBme56DXoaUHc7UqJnvXSzuQuhNL7sADga+PW5+a9MmSZKGXKpqdismpwEvqqrXNfOvAY6tqjdOWG41sLqZfSZwzzSbPgDYOaughpP9GX6j1qdu+7OzqlZ2s8FZ5PFM4lgo7M9wa3N/us7l8XopAJ4LnFdVL2rmzwGoqvfMaoM/3+7GqlrRyzaGif0ZfqPWp2Hpz7DE0S/2Z7jZn5nrZRfALcCyJIcn2Rt4JXB1f8KSJEmDNOuDAKtqV5I/Aq4DFgGXVtVdfYtMkiQNTE8nAqqqzwKf7VMsu13U5+3NN/sz/EatT8PSn2GJo1/sz3CzPzM062MAJEnSwuW1ACRJaiELAEmSWsgCQJKkFrIAkCSphSwAJElqIQsASZJayAJAkqQWsgCQJKmFLAAkSWohCwBJklrIAkCSpBayAJAkqYUsAHqQ5NEktyW5M8knkzxxD8uel+QtcxnfFHGcnOT2Ju6NSU6YYrn1SQ6bpP2ZzX23JdmcpG9XrEry2ST79WE7fXmuk6xMck+Se5Oc3ev2NJzM45HP40uTbE9yZ6/bGjUWAL35UVUtr6qjgR8Dr5/vgLpwA3BMVS0H/htw8QzX/0vggqbfRwAfnMnKSRZNdV9V/U5VPTzDeAaiifPDwIuBI4HTkxw5v1FpQMzjEc3jxhpg5XwHMYwsAPrni8CvACR5bVOdb0ry0YkLJvn9JLc0939q9zeOJKc130I2JflC03ZUkpubSv32JMt6CbKqHqmfXwP6ScBMrwe9BNg6bnt3NHGekeRD4/p4TZITm+lHkrwzyQbgT5NcPm65E5N8ppm+P8kBSd6b5P8et8x5Sd7cTP9J89zdnuQd45Y5t/m2/v8Bz5xhnyZzLHBvVd1XVT8GLgNO7sN2NdzM49HKY6rqC8D3+rGtUbN4vgMYBUkW0/mmeG2So4BzgeOrameS/SdZ5cqq+n+bdd8FnEmnAn878KKq+s64IbTXAx+oqo8l2Rt4XOWdZB2TJ8v7q+ojkyz/n4D3AAcCL5lhdy8APpfkS8A/AH/bRbX/JODOqnp781zdl+RJVfVD4BXAugnLXwZcCPxVM/9yYGWS3waW0fnnHODqJL8J/BB4JfAsOu/prwFfnRhEklcBfzJJfPdW1akT2g4Gvj1ufivwnGn6qQXMPB7JPNYeWAD0Zp8ktzXTXwQuAf4AuKKqdgJU1WSV59HNB8Z+wL7AdU37PwJrmsr6yqbty8C5SZbS+cDZMnFjVfWKmQRdVZ8GPt0k3V8AL5jBun+b5Do6Q2onA3+Q5JhpVnsU+FSz/q4k1wIvTXIFnQ+ut054jFuTHJjkacAY8FBVfSvJHwO/DdzaLLovnQ+SJwOfrqp/AUhy9RSxfwz4WJddzWSb6HJdLSzm8ejmsfbAAqA3P2r2wf1MkjD9P4o1wClVtSnJGcCJAFX1+iTPoZNMtyVZXlUfb4bcXgJcl+R1VfW5CY85o28Ou1XVF5I8I8kBuz/oulFVDwCXApemc2DN0cAuHrtL6Qnjpv+1qh4dN78OeAOdYblbquoHkzzMFcCpwC/T+SYBnX/K76mqvxm/YJI30cU/5xl+c9gKHDJufinwwHSPoQXJPB7dPNaeVJW3Wd6ARyZpOwr4OvDUZn7/5u95wFua6Z10hu32Aq4H1jTtzxi3nVuB5cDTgTRtFwJv6jHmXxm3vV8HvrN7fsJy64HDJmlfCezVTP8ysK35ewLwJTofHocA3wdOnOx5ojP8eT/wSeDl49rvBw4Y9zx+qXkulzRtvw1sAPZt5g9unsdfB24H9qHzLWLL7ue6h+dpMXAfcDiwN7AJOGq+33Pe+n8zj0c3j8fFdBid3Rfz/n4bppsjAH1WVXcleTfw+SSP0vkAOGPCYn9GJwH+GbiDzpsd4H3NwUGhc5TvJuBs4NVJfgJ8F3hnjyH+Z+C1zfZ+BLyimgzp0m8DH0jyr838n1TVd5M8CHyz6c+ddPbfTaqqHk1yDZ3nZdUUy9yV5MnAd6pqW9P2D0mOAL7c+YLGI8Crq+przben2+g8p1+cQX+minFXkj+iM6y7CLi0qu7qdbtaGMzj0chjgCSfoDM6c0CSrcCfV9Ul/dj2QpeZvWfUFknWA2dU1f3zHIqkWTKPtSf+DFCSpBayANBU1gDDdDIPSTO3BvNYU3AXgCRJLeQIgCRJLTSnvwJYuXJlXXvttXP5kJKmNtnJjqZlHktDZ1a5PKcjADt3dn2OCklDyjyWRoO7ACRJaiELAEmSWsgCQJKkFrIAkCSphbwWwIC89IM3dbXcZ954woAjkSTp8RwBkCSphSwAJElqoa4LgCSLktzaXP6RJIcn2ZBkS5J1SfYeXJiSJKmfZjICcBaweQGwjpEAAAwcSURBVNz8e4ELqmoZ8BBwZj8DkyRJg9NVAZBkKfAS4OJmPsBJwBXNImuBUwYRoCRJ6r9uRwAuBN4K/LSZfyrwcFXtaua3Agf3OTZJkjQg0xYASX4X2F5VXx3fPMmik15XOMnqJBuTbNyxY8csw5Q0n8xjafR0MwJwPPCyJPcDl9EZ+r8Q2C/J7vMILAUemGzlqrqoqlZU1YqxsbE+hCxprpnH0uiZtgCoqnOqamlVHQa8EvhcVb0KuBE4tVlsFXDVwKKUJEl91cuZAN8GXJbkXcCtwCX9CWn4dXuWP0mShtWMCoCqWg+sb6bvA47tf0iSJGnQPBOgJEktZAEgSVILWQBIktRCFgCSJLWQBYAkSS1kASBJUgv1ch4A9UE35xT4zBtPmINIJElt4giAJEktZAEgSVILWQBIktRCFgCSJLWQBYAkSS1kASBJUgtZAEiS1EIWAJIktZAFgCRJLeSZABeAbs4WCJ4xUJLUvWlHAJI8IcnNSTYluSvJO5r2w5NsSLIlybokew8+XEmS1A/d7AL4N+CkqjoGWA6sTHIc8F7ggqpaBjwEnDm4MCVJUj9NWwBUxyPN7F7NrYCTgCua9rXAKQOJUJIk9V1XBwEmWZTkNmA7cD3wDeDhqtrVLLIVOHgwIUqSpH7rqgCoqkerajmwFDgWOGKyxSZbN8nqJBuTbNyxY8fsI5U0b8xjafTM6GeAVfUwsB44Dtgvye5fESwFHphinYuqakVVrRgbG+slVknzxDyWRk83vwIYS7JfM70P8AJgM3AjcGqz2CrgqkEFKUmS+qub8wAsAdYmWUSnYLi8qq5JcjdwWZJ3AbcClwwwTkmS1EfTFgBVdTvwrEna76NzPIAkSVpgPBWwJEktZAEgSVILWQBIktRCFgCSJLWQBYAkSS1kASBJUgtZAEiS1EIWAJIktZAFgCRJLWQBIElSC1kASJLUQhYAkiS1kAWAJEktZAEgSVILWQBIktRCFgCSJLWQBYAkSS00bQGQ5JAkNybZnOSuJGc17fsnuT7JlubvUwYfriRJ6oduRgB2AW+uqiOA44A3JDkSOBu4oaqWATc085IkaQGYtgCoqm1V9bVm+gfAZuBg4GRgbbPYWuCUQQUpSZL6a0bHACQ5DHgWsAE4qKq2QadIAA7sd3CSJGkwFne7YJJ9gU8Bb6qq7yfpdr3VwGqAQw89dDYxqksv/eBN0y7zmTeeMAeRaNSYx9Lo6WoEIMledP75f6yqrmyaH0yypLl/CbB9snWr6qKqWlFVK8bGxvoRs6Q5Zh5Lo2faEYB0vupfAmyuqvePu+tqYBVwfvP3qoFEqHnhaIIkjbZudgEcD7wGuCPJbU3bn9L5x395kjOBbwGnDSZESZLUb9MWAFV1EzDVDv/n9zccSZI0FzwToCRJLWQBIElSC3X9M0CNhm4O7pMkjT5HACRJaiELAEmSWsgCQJKkFrIAkCSphSwAJElqIQsASZJayAJAkqQWsgCQJKmFLAAkSWohCwBJklrIAkCSpBayAJAkqYWG+mJA3Vy45jNvPGEOIpEkabRMOwKQ5NIk25PcOa5t/yTXJ9nS/H3KYMOUJEn91M0ugDXAygltZwM3VNUy4IZmXpIkLRDT7gKoqi8kOWxC88nAic30WmA98LY+xtV37k6QJOnnZnsQ4EFVtQ2g+Xtg/0KSJEmDNvCDAJOsBlYDHHrooYN+OM2hbkZVuuXoy3Brcx5P9z6f7r3b6/rSoMx2BODBJEsAmr/bp1qwqi6qqhVVtWJsbGyWDydpPpnH0uiZbQFwNbCqmV4FXNWfcCRJ0lyYdhdAkk/QOeDvgCRbgT8HzgcuT3Im8C3gtEEGKWlhcvh7ej5Hmi/d/Arg9Cnuen6fY5EkSXNkqM8EKGm0Dfrbbz8PVJVGjdcCkCSphSwAJElqIXcBSFIP3M2ghcoRAEmSWsgRAEkaYl7HRIPiCIAkSS1kASBJUgst+F0A/TwAx4N55k+3z71DnZLUH44ASJLUQhYAkiS1kAWAJEktZAEgSVILWQBIktRCFgCSJLWQBYAkSS204M8DIC0Unutg5qZ7znyuOno9h4nPYzv1NAKQZGWSe5Lcm+TsfgUlSZIGa9YjAEkWAR8GXghsBW5JcnVV3d2v4KSJhvVsjX6Dmh/D+n5YaEZhBMHRopnrZQTgWODeqrqvqn4MXAac3J+wJEnSIPVSABwMfHvc/NamTZIkDbleDgLMJG31uIWS1cDqZvaRJPdMs90DgJ09xDVs7M/w67lP+eM+RdKfbXXbn2uramU3G5xFHs8kjoXC/kyhn+//HuyxP0MS40zM5PXpOpfHS9Xj/md3t2LyXOC8qnpRM38OQFW9Z1Yb/Pl2N1bVil62MUzsz/AbtT4NS3+GJY5+sT/Dzf7MXC+7AG4BliU5PMnewCuBq/sTliRJGqRZ7wKoql1J/gi4DlgEXFpVd/UtMkmSNDA9nQioqj4LfLZPsex2UZ+3N9/sz/AbtT4NS3+GJY5+sT/Dzf7M0KyPAZAkSQuX1wKQJKmFhqoAWOinFk5ySJIbk2xOcleSs5r2/ZNcn2RL8/cp8x3rTCRZlOTWJNc084cn2dD0Z11zEOiCkGS/JFck+afmdXruQn59kvw/zXvtziSfSPKE+X59zOPhNEp5DOZyPwxNATDu1MIvBo4ETk9y5PxGNWO7gDdX1RHAccAbmj6cDdxQVcuAG5r5heQsYPO4+fcCFzT9eQg4c16imp0P0PnN7K8Cx9Dp14J8fZIcDPwxsKKqjqZzMO4rmcfXxzweaqOUx2Au966qhuIGPBe4btz8OcA58x1Xj326is61Eu4BljRtS4B75ju2GfRhKZ1EOgm4hs4JoHYCiyd73Yb5BvwS8E2aY1/GtS/I14efn41zfzoH9F4DvGg+Xx/zeDhvo5THTbzmch9uQzMCwIidWjjJYcCzgA3AQVW1DaD5e+D8RTZjFwJvBX7azD8VeLiqdjXzC+l1ejqwA/jbZij04iRPYoG+PlX1HeB/Ad8CtgH/B/gq8/v6mMfDaZTyGMzlvhimAqCrUwsvBEn2BT4FvKmqvj/f8cxWkt8FtlfVV8c3T7LoQnmdFgO/Dvx1VT0L+CELZIhwMs3+zZOBw4GnAU+iM/Q+0Vy+Pgv5/fEY5vFQM5f7YJgKgK3AIePmlwIPzFMss5ZkLzofGh+rqiub5geTLGnuXwJsn6/4Zuh44GVJ7qdztceT6HyT2C/J7nNILKTXaSuwtao2NPNX0PkQWaivzwuAb1bVjqr6CXAl8Dzm9/Uxj4fPqOUxmMt9MUwFwII/tXCSAJcAm6vq/ePuuhpY1UyvorNPcehV1TlVtbSqDqPzenyuql4F3Aic2iy2kPrzXeDbSZ7ZND0fuJsF+vrQGS48LskTm/fe7v7M5+tjHg+ZUctjMJf7Zr4PfphwIMTvAF8HvgGcO9/xzCL+E+gM0dwO3NbcfofO/rYbgC3N3/3nO9ZZ9O1E4Jpm+unAzcC9wCeBX5zv+GbQj+XAxuY1+jvgKQv59QHeAfwTcCfwUeAX5/v1MY+H9zYqedzEby73ePNMgJIktdAw7QKQJElzxAJAkqQWsgCQJKmFLAAkSWohCwBJklrIAkCPk+Q/JakkvzrfsUiaPXNZe2IBoMmcDtxE56QhkhYuc1lTsgDQYzTnPz+ezmUnX9m0/UKSv2quVX1Nks8mObW579lJPp/kq0mu230aTknzy1zWdCwANNEpdK6x/XXge0l+Hfg94DDgPwKvo3NZyt3nS/8gcGpVPRu4FHj3fAQt6XHMZe3R4ukXUcucTudCIdC5cMjpwF7AJ6vqp8B3k9zY3P9M4Gjg+s7pq1lE51KWkuafuaw9sgDQzyR5Kp0rhR2dpOh8CBTw6alWAe6qqufOUYiSumAuqxvuAtB4pwIfqap/X1WHVdUhwDeBncB/bvYfHkTngiIA9wBjSX42jJjkqPkIXNJjmMualgWAxjudx39D+BTwNDrX374T+BtgA/B/qurHdD5o3ptkE52rpj1v7sKVNAVzWdPyaoDqSpJ9q+qRZmjxZuD46lyTW9ICYi5rN48BULeuSbIfsDfwF35gSAuWuSzAEQBJklrJYwAkSWohCwBJklrIAkCSpBayAJAkqYUsACRJaiELAEmSWuj/B5ArRPznrMDTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 514.88x475.2 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# grid = sns.FacetGrid(train_df, col='Pclass', hue='Survived')\n",
    "grid = sns.FacetGrid(train_df, col='Survived', row='Pclass', height=2.2, aspect=1.6)\n",
    "grid.map(plt.hist, 'Age', alpha=.8, bins=20)\n",
    "grid.add_legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Younger people from calss=3 has a good chance of survival."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 514.88x475.2 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# grid = sns.FacetGrid(train_df, col='Pclass', hue='Survived')\n",
    "grid = sns.FacetGrid(train_df, col='Survived', row='Pclass', height=2.2, aspect=1.6)\n",
    "grid.map(plt.hist, 'Fare', alpha=.8, bins=20)\n",
    "grid.add_legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/animeshnanda/anaconda3/lib/python3.7/site-packages/seaborn/axisgrid.py:728: UserWarning: Using the pointplot function without specifying `order` is likely to produce an incorrect plot.\n",
      "  warnings.warn(warning)\n",
      "/Users/animeshnanda/anaconda3/lib/python3.7/site-packages/seaborn/axisgrid.py:733: UserWarning: Using the pointplot function without specifying `hue_order` is likely to produce an incorrect plot.\n",
      "  warnings.warn(warning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7ff0735bab10>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 482.34x475.2 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# grid = sns.FacetGrid(train_df, col='Embarked')\n",
    "grid = sns.FacetGrid(train_df, row='Embarked', height=2.2, aspect=2.6)\n",
    "grid.map(sns.pointplot, 'Pclass', 'Survived', 'Sex', palette='deep')\n",
    "grid.add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/animeshnanda/anaconda3/lib/python3.7/site-packages/seaborn/axisgrid.py:728: UserWarning: Using the barplot function without specifying `order` is likely to produce an incorrect plot.\n",
      "  warnings.warn(warning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7ff073f11110>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 514.88x475.2 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# grid = sns.FacetGrid(train_df, col='Embarked', hue='Survived', palette={0: 'k', 1: 'w'})\n",
    "grid = sns.FacetGrid(train_df, row='Embarked', col='Survived', height=2.2, aspect=1.6)\n",
    "grid.map(sns.barplot, 'Sex', 'Fare', alpha=.5, ci=None)\n",
    "grid.add_legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data cleaning\n",
    "\n",
    "Now we will do the following things:\n",
    "\n",
    "* Delete not so useful data\n",
    "* Assign numerical values to categorical data such as male/female etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before (891, 12) (418, 11) (891, 12) (418, 11)\n",
      "After (891, 10) (418, 9) (891, 10) (418, 9)\n"
     ]
    }
   ],
   "source": [
    "print(\"Before\", train_df.shape, test_df.shape, combine[0].shape, combine[1].shape)\n",
    "\n",
    "train_df = train_df.drop(['Ticket', 'Cabin'], axis=1)\n",
    "test_df = test_df.drop(['Ticket', 'Cabin'], axis=1)\n",
    "combine = [train_df, test_df]\n",
    "\n",
    "print(\"After\", train_df.shape, test_df.shape, combine[0].shape, combine[1].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Sex</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Capt</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Col</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Countess</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Don</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dr</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jonkheer</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lady</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Major</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Master</th>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Miss</th>\n",
       "      <td>182</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mlle</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mme</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mr</th>\n",
       "      <td>0</td>\n",
       "      <td>517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mrs</th>\n",
       "      <td>125</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ms</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rev</th>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sir</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Sex       female  male\n",
       "Title                 \n",
       "Capt           0     1\n",
       "Col            0     2\n",
       "Countess       1     0\n",
       "Don            0     1\n",
       "Dr             1     6\n",
       "Jonkheer       0     1\n",
       "Lady           1     0\n",
       "Major          0     2\n",
       "Master         0    40\n",
       "Miss         182     0\n",
       "Mlle           2     0\n",
       "Mme            1     0\n",
       "Mr             0   517\n",
       "Mrs          125     0\n",
       "Ms             1     0\n",
       "Rev            0     6\n",
       "Sir            0     1"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['Title'] = dataset.Name.str.extract(' ([A-Za-z]+)\\.', expand=False)\n",
    "\n",
    "pd.crosstab(train_df['Title'], train_df['Sex'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Master</td>\n",
       "      <td>0.575000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Miss</td>\n",
       "      <td>0.702703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mr</td>\n",
       "      <td>0.156673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mrs</td>\n",
       "      <td>0.793651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Rare</td>\n",
       "      <td>0.347826</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Title  Survived\n",
       "0  Master  0.575000\n",
       "1    Miss  0.702703\n",
       "2      Mr  0.156673\n",
       "3     Mrs  0.793651\n",
       "4    Rare  0.347826"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['Title'] = dataset['Title'].replace(['Lady', 'Countess','Capt', 'Col',\\\n",
    " \t'Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')\n",
    "\n",
    "    dataset['Title'] = dataset['Title'].replace('Mlle', 'Miss')\n",
    "    dataset['Title'] = dataset['Title'].replace('Ms', 'Miss')\n",
    "    dataset['Title'] = dataset['Title'].replace('Mme', 'Mrs')\n",
    "    \n",
    "train_df[['Title', 'Survived']].groupby(['Title'], as_index=False).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Mr', 'Mrs', 'Miss', 'Master', 'Rare'], dtype=object)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['Title'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</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>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</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>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>3</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>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>2</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>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</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>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</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     Fare Embarked  Title  \n",
       "0      0   7.2500        S      1  \n",
       "1      0  71.2833        C      3  \n",
       "2      0   7.9250        S      2  \n",
       "3      0  53.1000        S      3  \n",
       "4      0   8.0500        S      1  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "title_mapping = {\"Mr\": 1, \"Miss\": 2, \"Mrs\": 3, \"Master\": 4, \"Rare\": 5}\n",
    "for dataset in combine:\n",
    "    dataset['Title'] = dataset['Title'].map(title_mapping)\n",
    "    dataset['Title'] = dataset['Title'].fillna(0)\n",
    "\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((891, 9), (418, 9))"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = train_df.drop(['Name', 'PassengerId'], axis=1)\n",
    "test_df = test_df.drop(['Name'], axis=1)\n",
    "combine = [train_df, test_df]\n",
    "train_df.shape, test_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex   Age  SibSp  Parch     Fare Embarked  Title\n",
       "0         0       3    0  22.0      1      0   7.2500        S      1\n",
       "1         1       1    1  38.0      1      0  71.2833        C      3\n",
       "2         1       3    1  26.0      0      0   7.9250        S      2\n",
       "3         1       1    1  35.0      1      0  53.1000        S      3\n",
       "4         0       3    0  35.0      0      0   8.0500        S      1"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['Sex'] = dataset['Sex'].map( {'female': 1, 'male': 0} ).astype(int)\n",
    "\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7ff0739a2a90>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 514.88x475.2 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# grid = sns.FacetGrid(train_df, col='Pclass', hue='Gender')\n",
    "grid = sns.FacetGrid(train_df, row='Pclass', col='Sex', height=2.2, aspect=1.6)\n",
    "grid.map(plt.hist, 'Age', alpha=.8, bins=20)\n",
    "grid.add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  SibSp  Parch     Fare Embarked  Title\n",
       "0         0       3    0   22      1      0   7.2500        S      1\n",
       "1         1       1    1   38      1      0  71.2833        C      3\n",
       "2         1       3    1   26      0      0   7.9250        S      2\n",
       "3         1       1    1   35      1      0  53.1000        S      3\n",
       "4         0       3    0   35      0      0   8.0500        S      1"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "guess_ages = np.zeros((2,3))\n",
    "guess_ages\n",
    "\n",
    "for dataset in combine:\n",
    "    for i in range(0, 2):\n",
    "        for j in range(0, 3):\n",
    "            guess_df = dataset[(dataset['Sex'] == i) & \\\n",
    "                                  (dataset['Pclass'] == j+1)]['Age'].dropna()\n",
    "\n",
    "            # age_mean = guess_df.mean()\n",
    "            # age_std = guess_df.std()\n",
    "            # age_guess = rnd.uniform(age_mean - age_std, age_mean + age_std)\n",
    "\n",
    "            age_guess = guess_df.median()\n",
    "\n",
    "            # Convert random age float to nearest .5 age\n",
    "            guess_ages[i,j] = int( age_guess/0.5 + 0.5 ) * 0.5\n",
    "            \n",
    "    for i in range(0, 2):\n",
    "        for j in range(0, 3):\n",
    "            dataset.loc[ (dataset.Age.isnull()) & (dataset.Sex == i) & (dataset.Pclass == j+1),\\\n",
    "                    'Age'] = guess_ages[i,j]\n",
    "\n",
    "    dataset['Age'] = dataset['Age'].astype(int)\n",
    "\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AgeBand</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(-0.08, 16.0]</td>\n",
       "      <td>0.550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(16.0, 32.0]</td>\n",
       "      <td>0.337374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(32.0, 48.0]</td>\n",
       "      <td>0.412037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(48.0, 64.0]</td>\n",
       "      <td>0.434783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(64.0, 80.0]</td>\n",
       "      <td>0.090909</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         AgeBand  Survived\n",
       "0  (-0.08, 16.0]  0.550000\n",
       "1   (16.0, 32.0]  0.337374\n",
       "2   (32.0, 48.0]  0.412037\n",
       "3   (48.0, 64.0]  0.434783\n",
       "4   (64.0, 80.0]  0.090909"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['AgeBand'] = pd.cut(train_df['Age'], 5)\n",
    "train_df[['AgeBand', 'Survived']].groupby(['AgeBand'], as_index=False).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
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       "      <th>0</th>\n",
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       "      <td>1</td>\n",
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       "      <th>3</th>\n",
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       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "      <td>(32.0, 48.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>(32.0, 48.0]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  SibSp  Parch     Fare Embarked  Title  \\\n",
       "0         0       3    0    1      1      0   7.2500        S      1   \n",
       "1         1       1    1    2      1      0  71.2833        C      3   \n",
       "2         1       3    1    1      0      0   7.9250        S      2   \n",
       "3         1       1    1    2      1      0  53.1000        S      3   \n",
       "4         0       3    0    2      0      0   8.0500        S      1   \n",
       "\n",
       "        AgeBand  \n",
       "0  (16.0, 32.0]  \n",
       "1  (32.0, 48.0]  \n",
       "2  (16.0, 32.0]  \n",
       "3  (32.0, 48.0]  \n",
       "4  (32.0, 48.0]  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:    \n",
    "    dataset.loc[ dataset['Age'] <= 16, 'Age'] = 0\n",
    "    dataset.loc[(dataset['Age'] > 16) & (dataset['Age'] <= 32), 'Age'] = 1\n",
    "    dataset.loc[(dataset['Age'] > 32) & (dataset['Age'] <= 48), 'Age'] = 2\n",
    "    dataset.loc[(dataset['Age'] > 48) & (dataset['Age'] <= 64), 'Age'] = 3\n",
    "    dataset.loc[ dataset['Age'] > 64, 'Age']\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
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       "      <th>Title</th>\n",
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       "      <th>0</th>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>C</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  SibSp  Parch     Fare Embarked  Title\n",
       "0         0       3    0    1      1      0   7.2500        S      1\n",
       "1         1       1    1    2      1      0  71.2833        C      3\n",
       "2         1       3    1    1      0      0   7.9250        S      2\n",
       "3         1       1    1    2      1      0  53.1000        S      3\n",
       "4         0       3    0    2      0      0   8.0500        S      1"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = train_df.drop(['AgeBand'], axis=1)\n",
    "combine = [train_df, test_df]\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create new feature combining existing features\n",
    "\n",
    "We can create a new feature for FamilySize which combines Parch and SibSp. This will enable us to drop Parch and SibSp from our datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FamilySize</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.724138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.578431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.552795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.303538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0.136364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   FamilySize  Survived\n",
       "3           4  0.724138\n",
       "2           3  0.578431\n",
       "1           2  0.552795\n",
       "6           7  0.333333\n",
       "0           1  0.303538\n",
       "4           5  0.200000\n",
       "5           6  0.136364\n",
       "7           8  0.000000\n",
       "8          11  0.000000"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['FamilySize'] = dataset['SibSp'] + dataset['Parch'] + 1\n",
    "\n",
    "x=train_df[['FamilySize', 'Survived']].groupby(['FamilySize'], as_index=False).mean().sort_values(by='Survived', ascending=False)\n",
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## It's better to plot the above data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff073715b10>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x['FamilySize'],x['Survived'],color='y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>IsAlone</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.505650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.303538</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   IsAlone  Survived\n",
       "0        0  0.505650\n",
       "1        1  0.303538"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['IsAlone'] = 0\n",
    "    dataset.loc[dataset['FamilySize'] == 1, 'IsAlone'] = 1\n",
    "\n",
    "train_df[['IsAlone', 'Survived']].groupby(['IsAlone'], as_index=False).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>IsAlone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age     Fare Embarked  Title  IsAlone\n",
       "0         0       3    0    1   7.2500        S      1        0\n",
       "1         1       1    1    2  71.2833        C      3        0\n",
       "2         1       3    1    1   7.9250        S      2        1\n",
       "3         1       1    1    2  53.1000        S      3        0\n",
       "4         0       3    0    2   8.0500        S      1        1"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = train_df.drop(['Parch', 'SibSp', 'FamilySize'], axis=1)\n",
    "test_df = test_df.drop(['Parch', 'SibSp', 'FamilySize'], axis=1)\n",
    "combine = [train_df, test_df]\n",
    "\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C</td>\n",
       "      <td>0.553571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Q</td>\n",
       "      <td>0.389610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>S</td>\n",
       "      <td>0.339009</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Embarked  Survived\n",
       "0        C  0.553571\n",
       "1        Q  0.389610\n",
       "2        S  0.339009"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freq_port = train_df.Embarked.dropna().mode()[0]\n",
    "freq_port\n",
    "\n",
    "for dataset in combine:\n",
    "    dataset['Embarked'] = dataset['Embarked'].fillna(freq_port)\n",
    "    \n",
    "train_df[['Embarked', 'Survived']].groupby(['Embarked'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
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       "      <td>1</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
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       "      <td>2</td>\n",
       "      <td>53.1000</td>\n",
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       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age     Fare  Embarked  Title  IsAlone\n",
       "0         0       3    0    1   7.2500         0      1        0\n",
       "1         1       1    1    2  71.2833         1      3        0\n",
       "2         1       3    1    1   7.9250         0      2        1\n",
       "3         1       1    1    2  53.1000         0      3        0\n",
       "4         0       3    0    2   8.0500         0      1        1"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['Embarked'] = dataset['Embarked'].map( {'S': 0, 'C': 1, 'Q': 2} ).astype(int)\n",
    "\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass  Sex  Age     Fare  Embarked  Title  IsAlone\n",
       "0          892       3    0    2   7.8292         2      1        1\n",
       "1          893       3    1    2   7.0000         0      3        0\n",
       "2          894       2    0    3   9.6875         2      1        1\n",
       "3          895       3    0    1   8.6625         0      1        1\n",
       "4          896       3    1    1  12.2875         0      3        0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df['Fare'].fillna(test_df['Fare'].dropna().median(), inplace=True)\n",
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FareBand</th>\n",
       "      <th>Survived</th>\n",
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       "  <tbody>\n",
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       "      <th>3</th>\n",
       "      <td>(31.0, 512.329]</td>\n",
       "      <td>0.581081</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "          FareBand  Survived\n",
       "0   (-0.001, 7.91]  0.197309\n",
       "1   (7.91, 14.454]  0.303571\n",
       "2   (14.454, 31.0]  0.454955\n",
       "3  (31.0, 512.329]  0.581081"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['FareBand'] = pd.qcut(train_df['Fare'], 4)\n",
    "train_df[['FareBand', 'Survived']].groupby(['FareBand'], as_index=False).mean().sort_values(by='FareBand', ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "      <td>2</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  Fare  Embarked  Title  IsAlone\n",
       "0         0       3    0    1     0         0      1        0\n",
       "1         1       1    1    2     3         1      3        0\n",
       "2         1       3    1    1     1         0      2        1\n",
       "3         1       1    1    2     3         0      3        0\n",
       "4         0       3    0    2     1         0      1        1\n",
       "5         0       3    0    1     1         2      1        1\n",
       "6         0       1    0    3     3         0      1        1\n",
       "7         0       3    0    0     2         0      4        0\n",
       "8         1       3    1    1     1         0      3        0\n",
       "9         1       2    1    0     2         1      3        0"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset.loc[ dataset['Fare'] <= 7.91, 'Fare'] = 0\n",
    "    dataset.loc[(dataset['Fare'] > 7.91) & (dataset['Fare'] <= 14.454), 'Fare'] = 1\n",
    "    dataset.loc[(dataset['Fare'] > 14.454) & (dataset['Fare'] <= 31), 'Fare']   = 2\n",
    "    dataset.loc[ dataset['Fare'] > 31, 'Fare'] = 3\n",
    "    dataset['Fare'] = dataset['Fare'].astype(int)\n",
    "\n",
    "train_df = train_df.drop(['FareBand'], axis=1)\n",
    "combine = [train_df, test_df]\n",
    "    \n",
    "train_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>898</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>899</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>900</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>901</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass  Sex  Age  Fare  Embarked  Title  IsAlone\n",
       "0          892       3    0    2     0         2      1        1\n",
       "1          893       3    1    2     0         0      3        0\n",
       "2          894       2    0    3     1         2      1        1\n",
       "3          895       3    0    1     1         0      1        1\n",
       "4          896       3    1    1     1         0      3        0\n",
       "5          897       3    0    0     1         0      1        1\n",
       "6          898       3    1    1     0         2      2        1\n",
       "7          899       2    0    1     2         0      1        0\n",
       "8          900       3    1    1     0         1      3        1\n",
       "9          901       3    0    1     2         0      1        0"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## logistic regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((891, 7), (891,), (418, 7))"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = train_df.drop(\"Survived\", axis=1)\n",
    "Y_train = train_df[\"Survived\"]\n",
    "X_test  = test_df.drop(\"PassengerId\", axis=1).copy()\n",
    "X_train.shape, Y_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "78.56"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logreg = LogisticRegression()\n",
    "logreg.fit(X_train, Y_train)\n",
    "Y_pred = logreg.predict(X_test)\n",
    "acc_log = round(logreg.score(X_train, Y_train) * 100, 2)\n",
    "acc_log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature</th>\n",
       "      <th>Correlation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sex</td>\n",
       "      <td>2.146718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Title</td>\n",
       "      <td>0.413012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Embarked</td>\n",
       "      <td>0.312915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>IsAlone</td>\n",
       "      <td>0.053617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Age</td>\n",
       "      <td>-0.032550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Fare</td>\n",
       "      <td>-0.037113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Pclass</td>\n",
       "      <td>-0.997823</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Feature  Correlation\n",
       "1       Sex     2.146718\n",
       "5     Title     0.413012\n",
       "4  Embarked     0.312915\n",
       "6   IsAlone     0.053617\n",
       "2       Age    -0.032550\n",
       "3      Fare    -0.037113\n",
       "0    Pclass    -0.997823"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coeff_df = pd.DataFrame(train_df.columns.delete(0))\n",
    "coeff_df.columns = ['Feature']\n",
    "coeff_df[\"Correlation\"] = pd.Series(logreg.coef_[0])\n",
    "\n",
    "coeff_df.sort_values(by='Correlation', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Support vector machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "78.34"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc = SVC()\n",
    "svc.fit(X_train, Y_train)\n",
    "Y_pred = svc.predict(X_test)\n",
    "acc_svc = round(svc.score(X_train, Y_train) * 100, 2)\n",
    "acc_svc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## k-NN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "84.62"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors = 3)\n",
    "knn.fit(X_train, Y_train)\n",
    "Y_pred = knn.predict(X_test)\n",
    "acc_knn = round(knn.score(X_train, Y_train) * 100, 2)\n",
    "acc_knn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gaussian naive baise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "76.99"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gaussian = GaussianNB()\n",
    "gaussian.fit(X_train, Y_train)\n",
    "Y_pred = gaussian.predict(X_test)\n",
    "acc_gaussian = round(gaussian.score(X_train, Y_train) * 100, 2)\n",
    "acc_gaussian"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## linear SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/animeshnanda/anaconda3/lib/python3.7/site-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "78.23"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_svc = LinearSVC(max_iter=1000)\n",
    "linear_svc.fit(X_train, Y_train)\n",
    "Y_pred = linear_svc.predict(X_test)\n",
    "acc_linear_svc = round(linear_svc.score(X_train, Y_train) * 100, 2)\n",
    "acc_linear_svc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stochastic Gradient descent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "78.0"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sgd = SGDClassifier()\n",
    "sgd.fit(X_train, Y_train)\n",
    "Y_pred = sgd.predict(X_test)\n",
    "acc_sgd = round(sgd.score(X_train, Y_train) * 100, 2)\n",
    "acc_sgd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## decission tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "86.76"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decision_tree = DecisionTreeClassifier()\n",
    "decision_tree.fit(X_train, Y_train)\n",
    "Y_pred = decision_tree.predict(X_test)\n",
    "acc_decision_tree = round(decision_tree.score(X_train, Y_train) * 100, 2)\n",
    "acc_decision_tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## random forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "86.76"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_forest = RandomForestClassifier(n_estimators=100)\n",
    "random_forest.fit(X_train, Y_train)\n",
    "Y_pred = random_forest.predict(X_test)\n",
    "random_forest.score(X_train, Y_train)\n",
    "acc_random_forest = round(random_forest.score(X_train, Y_train) * 100, 2)\n",
    "acc_random_forest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Summary of the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>86.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>86.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KNN</td>\n",
       "      <td>84.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Log-Regression</td>\n",
       "      <td>78.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SVM</td>\n",
       "      <td>78.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Linear SVC</td>\n",
       "      <td>78.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>SGD</td>\n",
       "      <td>78.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Naive Bayes</td>\n",
       "      <td>76.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Model  Score\n",
       "3   Random Forest  86.76\n",
       "7   Decision Tree  86.76\n",
       "1             KNN  84.62\n",
       "2  Log-Regression  78.56\n",
       "0             SVM  78.34\n",
       "6      Linear SVC  78.23\n",
       "5             SGD  78.00\n",
       "4     Naive Bayes  76.99"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models = pd.DataFrame({\n",
    "    'Model': ['SVM', 'KNN', 'Log-Regression', 'Random Forest', 'Naive Bayes', \n",
    "              'SGD', 'Linear SVC', 'Decision Tree'],\n",
    "    'Score': [acc_svc, acc_knn, acc_log, acc_random_forest, acc_gaussian, \n",
    "              acc_sgd, acc_linear_svc, acc_decision_tree]})\n",
    "models.sort_values(by='Score', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "plt.ylim(0,100)\n",
    "sns.barplot(models['Model'],models['Score'],color='y')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}