IBM_SPSS.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Content"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Data Exploration\n",
    "#####     1.1 Categorical Features Exploration\n",
    "#####     1.2 Numeric Features Exploration\n",
    "#####     1.3 Data Exploration Conclusion: VERY IDEAL DATA\n",
    "### 2. Data Preprocessing\n",
    "#####     2.1 consistant naming\n",
    "#####     2.2 Handle missing values\n",
    "#####     2.3 Feature Engineering\n",
    "### 3. Final dataset for modeling\n",
    "### 4. Modeling\n",
    "### 5. Compare different models' accuracy score & best score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_excel('train_test_data.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test=pd.read_excel('valid_data.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(32500, 16)\n",
      "(13500, 15)\n"
     ]
    }
   ],
   "source": [
    "print(df.shape)\n",
    "print(df_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cit_index</th>\n",
       "      <th>birth_date</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education_num</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital_gain</th>\n",
       "      <th>capital_loss</th>\n",
       "      <th>hours_per_week</th>\n",
       "      <th>native_country</th>\n",
       "      <th>income_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1001</td>\n",
       "      <td>1981-10-09 00:00:00</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1002</td>\n",
       "      <td>1970-09-05 00:00:00</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1003</td>\n",
       "      <td>1982-08-18 00:00:00</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646.0</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1004</td>\n",
       "      <td>1967-12-27 00:00:00</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721.0</td>\n",
       "      <td>11th</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1005</td>\n",
       "      <td>1992-01-25 00:00:00</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cit_index           birth_date          workclass    fnlwgt   education  \\\n",
       "0       1001  1981-10-09 00:00:00          State-gov   77516.0   Bachelors   \n",
       "1       1002  1970-09-05 00:00:00   Self-emp-not-inc   83311.0   Bachelors   \n",
       "2       1003  1982-08-18 00:00:00            Private  215646.0     HS-grad   \n",
       "3       1004  1967-12-27 00:00:00            Private  234721.0        11th   \n",
       "4       1005  1992-01-25 00:00:00            Private  338409.0   Bachelors   \n",
       "\n",
       "   education_num       marital_status          occupation    relationship  \\\n",
       "0           13.0        Never-married        Adm-clerical   Not-in-family   \n",
       "1           13.0   Married-civ-spouse     Exec-managerial         Husband   \n",
       "2            9.0             Divorced   Handlers-cleaners   Not-in-family   \n",
       "3            7.0   Married-civ-spouse   Handlers-cleaners         Husband   \n",
       "4           13.0   Married-civ-spouse      Prof-specialty            Wife   \n",
       "\n",
       "     race      sex  capital_gain  capital_loss  hours_per_week  \\\n",
       "0   White     Male          2174             0            40.0   \n",
       "1   White     Male             0             0            13.0   \n",
       "2   White     Male             0             0            40.0   \n",
       "3   Black     Male             0             0            40.0   \n",
       "4   Black   Female             0             0            40.0   \n",
       "\n",
       "   native_country income_class  \n",
       "0   United-States        <=50K  \n",
       "1   United-States        <=50K  \n",
       "2   United-States        <=50K  \n",
       "3   United-States        <=50K  \n",
       "4            Cuba        <=50K  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " <=50K    24675\n",
       " >50K      7825\n",
       "Name: income_class, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['income_class'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cit_index</th>\n",
       "      <th>birth_date</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education_num</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital_gain</th>\n",
       "      <th>capital_loss</th>\n",
       "      <th>hours_per_week</th>\n",
       "      <th>native_country</th>\n",
       "      <th>income_class</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1001</td>\n",
       "      <td>1981-10-09 00:00:00</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1002</td>\n",
       "      <td>1970-09-05 00:00:00</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1003</td>\n",
       "      <td>1982-08-18 00:00:00</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646.0</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1004</td>\n",
       "      <td>1967-12-27 00:00:00</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721.0</td>\n",
       "      <td>11th</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1005</td>\n",
       "      <td>1992-01-25 00:00:00</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cit_index           birth_date          workclass    fnlwgt   education  \\\n",
       "0       1001  1981-10-09 00:00:00          State-gov   77516.0   Bachelors   \n",
       "1       1002  1970-09-05 00:00:00   Self-emp-not-inc   83311.0   Bachelors   \n",
       "2       1003  1982-08-18 00:00:00            Private  215646.0     HS-grad   \n",
       "3       1004  1967-12-27 00:00:00            Private  234721.0        11th   \n",
       "4       1005  1992-01-25 00:00:00            Private  338409.0   Bachelors   \n",
       "\n",
       "   education_num       marital_status          occupation    relationship  \\\n",
       "0           13.0        Never-married        Adm-clerical   Not-in-family   \n",
       "1           13.0   Married-civ-spouse     Exec-managerial         Husband   \n",
       "2            9.0             Divorced   Handlers-cleaners   Not-in-family   \n",
       "3            7.0   Married-civ-spouse   Handlers-cleaners         Husband   \n",
       "4           13.0   Married-civ-spouse      Prof-specialty            Wife   \n",
       "\n",
       "     race      sex  capital_gain  capital_loss  hours_per_week  \\\n",
       "0   White     Male          2174             0            40.0   \n",
       "1   White     Male             0             0            13.0   \n",
       "2   White     Male             0             0            40.0   \n",
       "3   Black     Male             0             0            40.0   \n",
       "4   Black   Female             0             0            40.0   \n",
       "\n",
       "   native_country income_class  class  \n",
       "0   United-States        <=50K      0  \n",
       "1   United-States        <=50K      0  \n",
       "2   United-States        <=50K      0  \n",
       "3   United-States        <=50K      0  \n",
       "4            Cuba        <=50K      0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_dict={' <=50K':0,' >50K':1}\n",
    "df['class']=df['income_class'].map(class_dict)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.759231\n",
       "1    0.240769\n",
       "Name: class, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['class'].value_counts()/df.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Note: imbalanced dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Data Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1 Categorical Features Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# workclass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f26bbb4e130>"
      ]
     },
     "execution_count": 9,
     "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": [
    "df['small']= 1 - df['class']\n",
    "df.groupby('workclass').agg('sum')[['class','small']].plot(kind='bar',stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f26bba74700>"
      ]
     },
     "execution_count": 10,
     "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": [
    "df_test.groupby('workclass').count().race.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# education"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f26bb9be970>"
      ]
     },
     "execution_count": 11,
     "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": [
    "df.groupby('education').agg('sum')[['class','small']].plot(kind='bar',stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f26bba58fa0>"
      ]
     },
     "execution_count": 12,
     "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": [
    "df_test.groupby('education').count().race.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: bachelors need to be changed to Bachelors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# martial_status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f26bb7951f0>"
      ]
     },
     "execution_count": 13,
     "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": [
    "df.groupby('marital_status').agg('sum')[['class','small']].plot(kind='bar',stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f26bb6e43d0>"
      ]
     },
     "execution_count": 14,
     "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": [
    "df_test.groupby('marital_status').count().race.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# occupation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f26bb63e640>"
      ]
     },
     "execution_count": 15,
     "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": [
    "df.groupby('occupation').agg('sum')[['class','small']].plot(kind='bar',stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f26bb571430>"
      ]
     },
     "execution_count": 16,
     "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": [
    "df_test.groupby('occupation').count().race.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# relationship"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f26bb4b3e20>"
      ]
     },
     "execution_count": 17,
     "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": [
    "df.groupby('relationship').agg('sum')[['class','small']].plot(kind='bar',stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f26bb3c46a0>"
      ]
     },
     "execution_count": 18,
     "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": [
    "df_test.groupby('relationship').count().race.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# race"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f26bbe104c0>"
      ]
     },
     "execution_count": 19,
     "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": [
    "df.groupby('race').agg('sum')[['class','small']].plot(kind='bar',stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f26bb2f2a60>"
      ]
     },
     "execution_count": 20,
     "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": [
    "df_test.groupby('race').count().sex.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f26bb243f10>"
      ]
     },
     "execution_count": 21,
     "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": [
    "df.groupby('sex').agg('sum')[['class','small']].plot(kind='bar',stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f26bb1cfaf0>"
      ]
     },
     "execution_count": 22,
     "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": [
    "df_test.groupby('sex').count().race.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "note: need to rename"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f26bb765100>"
      ]
     },
     "execution_count": 23,
     "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": [
    "df.groupby('native_country').agg('sum')[['class','small']].plot(kind='bar',stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f26bbae9df0>"
      ]
     },
     "execution_count": 24,
     "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": [
    "df_test.groupby('native_country').count().race.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1.2 Numeric Features Exploration"
   ]
  },
  {
   "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>cit_index</th>\n",
       "      <th>birth_date</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education_num</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital_gain</th>\n",
       "      <th>capital_loss</th>\n",
       "      <th>hours_per_week</th>\n",
       "      <th>native_country</th>\n",
       "      <th>income_class</th>\n",
       "      <th>class</th>\n",
       "      <th>small</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1001</td>\n",
       "      <td>1981-10-09 00:00:00</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1002</td>\n",
       "      <td>1970-09-05 00:00:00</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1003</td>\n",
       "      <td>1982-08-18 00:00:00</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646.0</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1004</td>\n",
       "      <td>1967-12-27 00:00:00</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721.0</td>\n",
       "      <td>11th</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1005</td>\n",
       "      <td>1992-01-25 00:00:00</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cit_index           birth_date          workclass    fnlwgt   education  \\\n",
       "0       1001  1981-10-09 00:00:00          State-gov   77516.0   Bachelors   \n",
       "1       1002  1970-09-05 00:00:00   Self-emp-not-inc   83311.0   Bachelors   \n",
       "2       1003  1982-08-18 00:00:00            Private  215646.0     HS-grad   \n",
       "3       1004  1967-12-27 00:00:00            Private  234721.0        11th   \n",
       "4       1005  1992-01-25 00:00:00            Private  338409.0   Bachelors   \n",
       "\n",
       "   education_num       marital_status          occupation    relationship  \\\n",
       "0           13.0        Never-married        Adm-clerical   Not-in-family   \n",
       "1           13.0   Married-civ-spouse     Exec-managerial         Husband   \n",
       "2            9.0             Divorced   Handlers-cleaners   Not-in-family   \n",
       "3            7.0   Married-civ-spouse   Handlers-cleaners         Husband   \n",
       "4           13.0   Married-civ-spouse      Prof-specialty            Wife   \n",
       "\n",
       "     race      sex  capital_gain  capital_loss  hours_per_week  \\\n",
       "0   White     Male          2174             0            40.0   \n",
       "1   White     Male             0             0            13.0   \n",
       "2   White     Male             0             0            40.0   \n",
       "3   Black     Male             0             0            40.0   \n",
       "4   Black   Female             0             0            40.0   \n",
       "\n",
       "   native_country income_class  class  small  \n",
       "0   United-States        <=50K      0      1  \n",
       "1   United-States        <=50K      0      1  \n",
       "2   United-States        <=50K      0      1  \n",
       "3   United-States        <=50K      0      1  \n",
       "4            Cuba        <=50K      0      1  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# fnlwgt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f26bb469b20>"
      ]
     },
     "execution_count": 26,
     "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": [
    "df['fnlwgt'].plot(kind='hist')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f26bb466700>"
      ]
     },
     "execution_count": 27,
     "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": [
    "df_test['fnlwgt'].plot(kind='hist')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# education_num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f26bbbdbfa0>"
      ]
     },
     "execution_count": 28,
     "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": [
    "df['education_num'].plot(kind='hist')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f26baaede80>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ng/nPOO/5YLiMh8thqN3ACVPzG9qYJGkFHC5l8TFgY5ITkzwJOA+4fuRMkrRqHBaHoapqX5J/CnwQWANcWVV3jBzrsZjLw2NT5j0fmHE5zHs+mP+M854PBsqYqhrifSVJjyOHy2EoSdKILAtJUpdlMZAkJyS5KcmdSe5I8qqxMy0lyZokH0/y/rGzLCbJ0UmuTfInSe5K8tNjZ5qW5J+3v+NPJ3lXkiPmINOVSR5I8umpsWOTbE9yT/t+zJzl+7ft7/j2JP89ydFj5Vsq49Sy1ySpJMeNkW0qx6IZk/xq+295R5J/sxzbsiyGsw94TVWdBJwKXDjHjyh5FXDX2CEO4U3A/6qqZwDPZo6yJlkP/DNgU1X9JJMLMM4bNxUAVwFnHTB2EXBjVW0EbmzzY7mKg/NtB36yqp4F/F/g4pUOdYCrODgjSU4AzgQ+v9KBFnEVB2RM8gImT7h4dlU9E/h3y7Ehy2IgVXV/Vd3Wpr/K5B+49eOmOliSDcDZwNvGzrKYJE8B/iZwBUBVPVpVXxk31UHWAkcmWQscBXxh5DxU1R8Dew8Y3gxsa9PbgHNXNNSUxfJV1R9W1b42+1Em91ONZon/hgCXAb8GjH510BIZ/zGwtaoeaes8sBzbsixWQJIF4LnAzeMmWdQbmfyP/62xgyzhRGAP8F/bobK3JXny2KH2q6rdTH5z+zxwP/BQVf3huKmWdHxV3d+mvwgcP2aYjn8I/M+xQxwoyWZgd1V9cuwsh/ATwM8kuTnJR5I8bzne1LIYWJLvB94DvLqqHh47z7QkLwUeqKpbx85yCGuBk4G3VtVzga8x7uGT79KO+29mUmp/BXhykleMm6qvJtfMj/6b8WKS/Csmh3HfOXaWaUmOAl4H/MbYWTrWAscyOfz9L4FrkuR7fVPLYkBJnsikKN5ZVe8dO88iTgPOSXIvkyf5np7k98eNdJBdwK6q2r9Xdi2T8pgXfxv4bFXtqaq/AN4L/I2RMy3lS0meCtC+L8vhieWU5B8ALwVeXvN3E9iPMfml4JPtZ2YDcFuSHxk11cF2Ae+tiVuYHDX4nk/EWxYDaU1+BXBXVf3u2HkWU1UXV9WGqlpgclL2w1U1V78VV9UXgfuSPL0NnQHcOWKkA30eODXJUe3v/Azm6AT8Aa4HtrTpLcB1I2Y5SJKzmBwSPaeqvj52ngNV1aeq6oeraqH9zOwCTm7/j86T9wEvAEjyE8CTWIYn5VoWwzkN+CUmv61/on29ZOxQh6lfBd6Z5HbgOcDvjJzn29oez7XAbcCnmPxMjf5IiCTvAv4P8PQku5KcD2wFXpjkHiZ7RFvnLN9/BH4A2N5+Xv7zWPkOkXGuLJHxSuBp7XLaq4Ety7GX5uM+JEld7llIkrosC0lSl2UhSeqyLCRJXZaFJKnLspAkdVkWkqSu/w/pLPdSrHSnGAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_test['education_num'].plot(kind='hist')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# capital_gain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f26baa649a0>"
      ]
     },
     "execution_count": 30,
     "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": [
    "df['capital_gain'].plot(kind='hist')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f26baa50160>"
      ]
     },
     "execution_count": 31,
     "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": [
    "df_test['capital_gain'].plot(kind='hist')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# capital_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-200.0, 5000.0)"
      ]
     },
     "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": [
    "df['capital_loss'].plot(kind='hist')\n",
    "plt.xlim([-200, 5000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-200.0, 5000.0)"
      ]
     },
     "execution_count": 33,
     "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": [
    "df_test['capital_loss'].plot(kind='hist')\n",
    "plt.xlim([-200, 5000])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# hours_per_week"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f26ba8fd280>"
      ]
     },
     "execution_count": 34,
     "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": [
    "df['hours_per_week'].plot(kind='hist')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f26ba8888e0>"
      ]
     },
     "execution_count": 35,
     "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": [
    "df_test['hours_per_week'].plot(kind='hist')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.3 Data Exploration Conclusion: \n",
    "## Same distribution between train and test datasets for each feature! \n",
    "## VERY IDEAL DATA!!!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Data Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1 consistant naming"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([' Bachelors', ' HS-grad', ' 11th', ' Masters', ' 9th',\n",
       "       ' Some-college', ' Assoc-acdm', ' Assoc-voc', ' 7th-8th',\n",
       "       ' Doctorate', ' Prof-school', ' 5th-6th', ' 10th', ' 1st-4th',\n",
       "       ' Preschool', ' 12th', ' bachelors'], dtype=object)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['education'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"education\"].replace({' bachelors':' Bachelors'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([' Bachelors', ' HS-grad', ' 11th', ' Masters', ' 9th',\n",
       "       ' Some-college', ' Assoc-acdm', ' Assoc-voc', ' 7th-8th',\n",
       "       ' Doctorate', ' Prof-school', ' 5th-6th', ' 10th', ' 1st-4th',\n",
       "       ' Preschool', ' 12th'], dtype=object)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['education'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([' Male', ' Female', ' F', 'M', 'male', 'm', ' f', ' female'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['sex'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([' Female', ' Male', ' f', ' F'], dtype=object)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test['sex'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Male', 'Female'], dtype=object)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sex_dict={' F':'Female',' Female':'Female',' Male':'Male',' f':'Female',' female':'Female', 'M':'Male','m':'Male','male':'Male'}\n",
    "df['sex']=df['sex'].map(sex_dict)\n",
    "df['sex'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Female', 'Male'], dtype=object)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test['sex']=df_test['sex'].map(sex_dict)\n",
    "df_test['sex'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2.2 Handle missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cit_index         0.000000\n",
       "birth_date        0.000000\n",
       "workclass         5.630769\n",
       "fnlwgt            0.070769\n",
       "education         0.000000\n",
       "education_num     0.123077\n",
       "marital_status    0.000000\n",
       "occupation        5.652308\n",
       "relationship      0.000000\n",
       "race              0.000000\n",
       "sex               0.000000\n",
       "capital_gain      0.000000\n",
       "capital_loss      0.000000\n",
       "hours_per_week    0.076923\n",
       "native_country    1.787692\n",
       "income_class      0.000000\n",
       "class             0.000000\n",
       "small             0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()/df.shape[0]*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cit_index         0.000000\n",
       "birth_date        0.000000\n",
       "workclass         5.896296\n",
       "fnlwgt            0.000000\n",
       "education         0.000000\n",
       "education_num     0.000000\n",
       "marital_status    0.000000\n",
       "occupation        5.918519\n",
       "relationship      0.000000\n",
       "race              0.000000\n",
       "sex               0.000000\n",
       "capital_gain      0.000000\n",
       "capital_loss      0.000000\n",
       "hours_per_week    0.000000\n",
       "native_country    1.688889\n",
       "dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.isnull().sum()/df_test.shape[0]*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>cit_index</th>\n",
       "      <th>birth_date</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education_num</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital_gain</th>\n",
       "      <th>capital_loss</th>\n",
       "      <th>hours_per_week</th>\n",
       "      <th>native_country</th>\n",
       "      <th>income_class</th>\n",
       "      <th>class</th>\n",
       "      <th>small</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>32500.000000</td>\n",
       "      <td>32500</td>\n",
       "      <td>30670</td>\n",
       "      <td>3.247700e+04</td>\n",
       "      <td>32500</td>\n",
       "      <td>32460.000000</td>\n",
       "      <td>32500</td>\n",
       "      <td>30663</td>\n",
       "      <td>32500</td>\n",
       "      <td>32500</td>\n",
       "      <td>32500</td>\n",
       "      <td>32500.000000</td>\n",
       "      <td>32500.000000</td>\n",
       "      <td>32475.000000</td>\n",
       "      <td>31919</td>\n",
       "      <td>32500</td>\n",
       "      <td>32500.000000</td>\n",
       "      <td>32500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>15147</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1989-03-02 00:00:00</td>\n",
       "      <td>Private</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "      <td>22652</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10482</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14946</td>\n",
       "      <td>4132</td>\n",
       "      <td>13169</td>\n",
       "      <td>27769</td>\n",
       "      <td>21757</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29115</td>\n",
       "      <td>24675</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>17250.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.897837e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.080376</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1075.529969</td>\n",
       "      <td>87.440615</td>\n",
       "      <td>40.437229</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.240769</td>\n",
       "      <td>0.759231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9382.086211</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.055972e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.574006</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7370.866087</td>\n",
       "      <td>403.296778</td>\n",
       "      <td>12.348144</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.427557</td>\n",
       "      <td>0.427557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1001.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.228500e+04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9125.750000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.177890e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>17250.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.783530e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>25375.250000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.370510e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>33500.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.484705e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99999.000000</td>\n",
       "      <td>4356.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           cit_index           birth_date workclass        fnlwgt education  \\\n",
       "count   32500.000000                32500     30670  3.247700e+04     32500   \n",
       "unique           NaN                15147         8           NaN        16   \n",
       "top              NaN  1989-03-02 00:00:00   Private           NaN   HS-grad   \n",
       "freq             NaN                   12     22652           NaN     10482   \n",
       "mean    17250.500000                  NaN       NaN  1.897837e+05       NaN   \n",
       "std      9382.086211                  NaN       NaN  1.055972e+05       NaN   \n",
       "min      1001.000000                  NaN       NaN  1.228500e+04       NaN   \n",
       "25%      9125.750000                  NaN       NaN  1.177890e+05       NaN   \n",
       "50%     17250.500000                  NaN       NaN  1.783530e+05       NaN   \n",
       "75%     25375.250000                  NaN       NaN  2.370510e+05       NaN   \n",
       "max     33500.000000                  NaN       NaN  1.484705e+06       NaN   \n",
       "\n",
       "        education_num       marital_status       occupation relationship  \\\n",
       "count    32460.000000                32500            30663        32500   \n",
       "unique            NaN                    7               14            6   \n",
       "top               NaN   Married-civ-spouse   Prof-specialty      Husband   \n",
       "freq              NaN                14946             4132        13169   \n",
       "mean        10.080376                  NaN              NaN          NaN   \n",
       "std          2.574006                  NaN              NaN          NaN   \n",
       "min          1.000000                  NaN              NaN          NaN   \n",
       "25%          9.000000                  NaN              NaN          NaN   \n",
       "50%         10.000000                  NaN              NaN          NaN   \n",
       "75%         12.000000                  NaN              NaN          NaN   \n",
       "max         16.000000                  NaN              NaN          NaN   \n",
       "\n",
       "          race    sex  capital_gain  capital_loss  hours_per_week  \\\n",
       "count    32500  32500  32500.000000  32500.000000    32475.000000   \n",
       "unique       5      2           NaN           NaN             NaN   \n",
       "top      White   Male           NaN           NaN             NaN   \n",
       "freq     27769  21757           NaN           NaN             NaN   \n",
       "mean       NaN    NaN   1075.529969     87.440615       40.437229   \n",
       "std        NaN    NaN   7370.866087    403.296778       12.348144   \n",
       "min        NaN    NaN      0.000000      0.000000        1.000000   \n",
       "25%        NaN    NaN      0.000000      0.000000       40.000000   \n",
       "50%        NaN    NaN      0.000000      0.000000       40.000000   \n",
       "75%        NaN    NaN      0.000000      0.000000       45.000000   \n",
       "max        NaN    NaN  99999.000000   4356.000000       99.000000   \n",
       "\n",
       "        native_country income_class         class         small  \n",
       "count            31919        32500  32500.000000  32500.000000  \n",
       "unique              41            2           NaN           NaN  \n",
       "top      United-States        <=50K           NaN           NaN  \n",
       "freq             29115        24675           NaN           NaN  \n",
       "mean               NaN          NaN      0.240769      0.759231  \n",
       "std                NaN          NaN      0.427557      0.427557  \n",
       "min                NaN          NaN      0.000000      0.000000  \n",
       "25%                NaN          NaN      0.000000      1.000000  \n",
       "50%                NaN          NaN      0.000000      1.000000  \n",
       "75%                NaN          NaN      0.000000      1.000000  \n",
       "max                NaN          NaN      1.000000      1.000000  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"education_num\"] = pd.to_numeric(df[\"education_num\"], downcast=\"float\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['workclass']=df['workclass'].fillna(df['workclass'].mode()[0])\n",
    "df['occupation']=df['occupation'].fillna(df['occupation'].mode()[0])\n",
    "df['native_country']=df['native_country'].fillna(df['native_country'].mode()[0])\n",
    "df['fnlwgt']=df['fnlwgt'].fillna(df['fnlwgt'].median())\n",
    "df['education_num']=df['education_num'].fillna(df['education_num'].median())\n",
    "df['hours_per_week']=df['hours_per_week'].fillna(40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cit_index         0\n",
       "birth_date        0\n",
       "workclass         0\n",
       "fnlwgt            0\n",
       "education         0\n",
       "education_num     0\n",
       "marital_status    0\n",
       "occupation        0\n",
       "relationship      0\n",
       "race              0\n",
       "sex               0\n",
       "capital_gain      0\n",
       "capital_loss      0\n",
       "hours_per_week    0\n",
       "native_country    0\n",
       "income_class      0\n",
       "class             0\n",
       "small             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([' Private', ' Local-gov', ' Federal-gov', 'Private',\n",
       "       ' Self-emp-not-inc', ' Self-emp-inc', ' State-gov', ' Without-pay',\n",
       "       ' Never-worked'], dtype=object)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test['workclass']=df_test['workclass'].fillna(' Private')\n",
    "df_test['workclass'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([' Machine-op-inspct', ' Other-service', ' Prof-specialty',\n",
       "       ' Protective-serv', 'Pro-specialty', ' Exec-managerial',\n",
       "       ' Craft-repair', ' Farming-fishing', ' Adm-clerical', ' Sales',\n",
       "       ' Transport-moving', ' Tech-support', ' Handlers-cleaners',\n",
       "       ' Priv-house-serv', ' Armed-Forces'], dtype=object)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test['occupation']=df_test['occupation'].fillna(' Pro-specialty')\n",
    "df_test['occupation'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([' United-States', ' South', ' Canada', ' El-Salvador', ' Mexico',\n",
       "       ' Taiwan', ' Philippines', ' England', ' Columbia', ' Scotland',\n",
       "       ' India', ' China', ' Thailand', ' Poland', 'United_States',\n",
       "       ' Jamaica', ' Portugal', ' Japan', ' Puerto-Rico', ' Germany',\n",
       "       ' Haiti', ' Dominican-Republic', ' Guatemala', ' Trinadad&Tobago',\n",
       "       ' Italy', ' Cuba', ' Honduras', ' Greece', ' Vietnam', ' Hong',\n",
       "       ' Ecuador', ' Iran', ' Ireland', ' Nicaragua', ' Cambodia',\n",
       "       ' Peru', ' Outlying-US(Guam-USVI-etc)', ' France', ' Yugoslavia',\n",
       "       ' Hungary', ' Laos'], dtype=object)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test['native_country']=df_test['native_country'].fillna(' United_States')\n",
    "df_test['native_country'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cit_index         0\n",
       "birth_date        0\n",
       "workclass         0\n",
       "fnlwgt            0\n",
       "education         0\n",
       "education_num     0\n",
       "marital_status    0\n",
       "occupation        0\n",
       "relationship      0\n",
       "race              0\n",
       "sex               0\n",
       "capital_gain      0\n",
       "capital_loss      0\n",
       "hours_per_week    0\n",
       "native_country    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.3 Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## New features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['age']=[2020- int(str(i)[0:4]) for i in df['birth_date']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>cit_index</th>\n",
       "      <th>birth_date</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education_num</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital_gain</th>\n",
       "      <th>capital_loss</th>\n",
       "      <th>hours_per_week</th>\n",
       "      <th>native_country</th>\n",
       "      <th>income_class</th>\n",
       "      <th>class</th>\n",
       "      <th>small</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1001</td>\n",
       "      <td>1981-10-09 00:00:00</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1002</td>\n",
       "      <td>1970-09-05 00:00:00</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1003</td>\n",
       "      <td>1982-08-18 00:00:00</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646.0</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1004</td>\n",
       "      <td>1967-12-27 00:00:00</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721.0</td>\n",
       "      <td>11th</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1005</td>\n",
       "      <td>1992-01-25 00:00:00</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cit_index           birth_date          workclass    fnlwgt   education  \\\n",
       "0       1001  1981-10-09 00:00:00          State-gov   77516.0   Bachelors   \n",
       "1       1002  1970-09-05 00:00:00   Self-emp-not-inc   83311.0   Bachelors   \n",
       "2       1003  1982-08-18 00:00:00            Private  215646.0     HS-grad   \n",
       "3       1004  1967-12-27 00:00:00            Private  234721.0        11th   \n",
       "4       1005  1992-01-25 00:00:00            Private  338409.0   Bachelors   \n",
       "\n",
       "   education_num       marital_status          occupation    relationship  \\\n",
       "0           13.0        Never-married        Adm-clerical   Not-in-family   \n",
       "1           13.0   Married-civ-spouse     Exec-managerial         Husband   \n",
       "2            9.0             Divorced   Handlers-cleaners   Not-in-family   \n",
       "3            7.0   Married-civ-spouse   Handlers-cleaners         Husband   \n",
       "4           13.0   Married-civ-spouse      Prof-specialty            Wife   \n",
       "\n",
       "     race     sex  capital_gain  capital_loss  hours_per_week  native_country  \\\n",
       "0   White    Male          2174             0            40.0   United-States   \n",
       "1   White    Male             0             0            13.0   United-States   \n",
       "2   White    Male             0             0            40.0   United-States   \n",
       "3   Black    Male             0             0            40.0   United-States   \n",
       "4   Black  Female             0             0            40.0            Cuba   \n",
       "\n",
       "  income_class  class  small  age  \n",
       "0        <=50K      0      1   39  \n",
       "1        <=50K      0      1   50  \n",
       "2        <=50K      0      1   38  \n",
       "3        <=50K      0      1   53  \n",
       "4        <=50K      0      1   28  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "38.57713969162589"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "positive_age_mean=df[df['age']>0]['age'].mean()\n",
    "positive_age_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['age']=df['age'].where(df['age']>0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['age'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['age']=df['age'].fillna(positive_age_mean)\n",
    "df['age'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "bins= [14,25,35,45,55,65,75,100]\n",
    "labels = [1,2,3,4,5,6,7]\n",
    "df['agecat'] = pd.cut(df['age'], bins=bins, labels=labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>cit_index</th>\n",
       "      <th>birth_date</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education_num</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital_gain</th>\n",
       "      <th>capital_loss</th>\n",
       "      <th>hours_per_week</th>\n",
       "      <th>native_country</th>\n",
       "      <th>income_class</th>\n",
       "      <th>class</th>\n",
       "      <th>small</th>\n",
       "      <th>age</th>\n",
       "      <th>agecat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1001</td>\n",
       "      <td>1981-10-09 00:00:00</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>39.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1002</td>\n",
       "      <td>1970-09-05 00:00:00</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>50.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1003</td>\n",
       "      <td>1982-08-18 00:00:00</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646.0</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1004</td>\n",
       "      <td>1967-12-27 00:00:00</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721.0</td>\n",
       "      <td>11th</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>53.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1005</td>\n",
       "      <td>1992-01-25 00:00:00</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>28.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cit_index           birth_date          workclass    fnlwgt   education  \\\n",
       "0       1001  1981-10-09 00:00:00          State-gov   77516.0   Bachelors   \n",
       "1       1002  1970-09-05 00:00:00   Self-emp-not-inc   83311.0   Bachelors   \n",
       "2       1003  1982-08-18 00:00:00            Private  215646.0     HS-grad   \n",
       "3       1004  1967-12-27 00:00:00            Private  234721.0        11th   \n",
       "4       1005  1992-01-25 00:00:00            Private  338409.0   Bachelors   \n",
       "\n",
       "   education_num       marital_status          occupation    relationship  \\\n",
       "0           13.0        Never-married        Adm-clerical   Not-in-family   \n",
       "1           13.0   Married-civ-spouse     Exec-managerial         Husband   \n",
       "2            9.0             Divorced   Handlers-cleaners   Not-in-family   \n",
       "3            7.0   Married-civ-spouse   Handlers-cleaners         Husband   \n",
       "4           13.0   Married-civ-spouse      Prof-specialty            Wife   \n",
       "\n",
       "     race     sex  capital_gain  capital_loss  hours_per_week  native_country  \\\n",
       "0   White    Male          2174             0            40.0   United-States   \n",
       "1   White    Male             0             0            13.0   United-States   \n",
       "2   White    Male             0             0            40.0   United-States   \n",
       "3   Black    Male             0             0            40.0   United-States   \n",
       "4   Black  Female             0             0            40.0            Cuba   \n",
       "\n",
       "  income_class  class  small   age agecat  \n",
       "0        <=50K      0      1  39.0      3  \n",
       "1        <=50K      0      1  50.0      4  \n",
       "2        <=50K      0      1  38.0      3  \n",
       "3        <=50K      0      1  53.0      4  \n",
       "4        <=50K      0      1  28.0      2  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test['age']=[2020- int(str(i)[0:4]) for i in df_test['birth_date']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    13500.000000\n",
       "mean        38.769481\n",
       "std         13.854916\n",
       "min         17.000000\n",
       "25%         28.000000\n",
       "50%         37.000000\n",
       "75%         48.000000\n",
       "max         90.000000\n",
       "Name: age, dtype: float64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test['age'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test['age'].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"fnlwgt\"] = pd.to_numeric(df[\"fnlwgt\"], downcast=\"float\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['fnlwgt']=(df['fnlwgt']-df['fnlwgt'].min())/(df['fnlwgt'].max()-df['fnlwgt'].min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['education_num']=(df['education_num']-df['education_num'].min())/(df['education_num'].max()-df['education_num'].min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['capital_gain']=(df['capital_gain']-df['capital_gain'].min())/(df['capital_gain'].max()-df['capital_gain'].min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['capital_loss']=(df['capital_loss']-df['capital_loss'].min())/(df['capital_loss'].max()-df['capital_loss'].min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['hours_per_week']=(df['hours_per_week']-df['hours_per_week'].min())/(df['hours_per_week'].max()-df['hours_per_week'].min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['age']=(df['age']-df['age'].min())/(df['age'].max()-df['age'].min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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>cit_index</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education_num</th>\n",
       "      <th>capital_gain</th>\n",
       "      <th>capital_loss</th>\n",
       "      <th>hours_per_week</th>\n",
       "      <th>class</th>\n",
       "      <th>small</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>32500.000000</td>\n",
       "      <td>32500.000000</td>\n",
       "      <td>32500.000000</td>\n",
       "      <td>32500.000000</td>\n",
       "      <td>32500.000000</td>\n",
       "      <td>32500.000000</td>\n",
       "      <td>32500.000000</td>\n",
       "      <td>32500.000000</td>\n",
       "      <td>32500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>17250.500000</td>\n",
       "      <td>0.120543</td>\n",
       "      <td>0.605285</td>\n",
       "      <td>0.010755</td>\n",
       "      <td>0.020074</td>\n",
       "      <td>0.402417</td>\n",
       "      <td>0.240769</td>\n",
       "      <td>0.759231</td>\n",
       "      <td>0.295577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9382.086211</td>\n",
       "      <td>0.071691</td>\n",
       "      <td>0.171490</td>\n",
       "      <td>0.073709</td>\n",
       "      <td>0.092584</td>\n",
       "      <td>0.125953</td>\n",
       "      <td>0.427557</td>\n",
       "      <td>0.427557</td>\n",
       "      <td>0.186837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1001.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</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>9125.750000</td>\n",
       "      <td>0.071677</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.150685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>17250.500000</td>\n",
       "      <td>0.112786</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.273973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>25375.250000</td>\n",
       "      <td>0.152607</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.448980</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.424658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>33500.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          cit_index        fnlwgt  education_num  capital_gain  capital_loss  \\\n",
       "count  32500.000000  32500.000000   32500.000000  32500.000000  32500.000000   \n",
       "mean   17250.500000      0.120543       0.605285      0.010755      0.020074   \n",
       "std     9382.086211      0.071691       0.171490      0.073709      0.092584   \n",
       "min     1001.000000      0.000000       0.000000      0.000000      0.000000   \n",
       "25%     9125.750000      0.071677       0.533333      0.000000      0.000000   \n",
       "50%    17250.500000      0.112786       0.600000      0.000000      0.000000   \n",
       "75%    25375.250000      0.152607       0.733333      0.000000      0.000000   \n",
       "max    33500.000000      1.000000       1.000000      1.000000      1.000000   \n",
       "\n",
       "       hours_per_week         class         small           age  \n",
       "count    32500.000000  32500.000000  32500.000000  32500.000000  \n",
       "mean         0.402417      0.240769      0.759231      0.295577  \n",
       "std          0.125953      0.427557      0.427557      0.186837  \n",
       "min          0.000000      0.000000      0.000000      0.000000  \n",
       "25%          0.397959      0.000000      1.000000      0.150685  \n",
       "50%          0.397959      0.000000      1.000000      0.273973  \n",
       "75%          0.448980      0.000000      1.000000      0.424658  \n",
       "max          1.000000      1.000000      1.000000      1.000000  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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>cit_index</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education_num</th>\n",
       "      <th>capital_gain</th>\n",
       "      <th>capital_loss</th>\n",
       "      <th>hours_per_week</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>13500.000000</td>\n",
       "      <td>13500.000000</td>\n",
       "      <td>13500.000000</td>\n",
       "      <td>13500.000000</td>\n",
       "      <td>13500.000000</td>\n",
       "      <td>13500.000000</td>\n",
       "      <td>13500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>16750.500000</td>\n",
       "      <td>0.119322</td>\n",
       "      <td>0.604706</td>\n",
       "      <td>0.010237</td>\n",
       "      <td>0.023363</td>\n",
       "      <td>0.401767</td>\n",
       "      <td>0.298212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3897.258652</td>\n",
       "      <td>0.072236</td>\n",
       "      <td>0.170157</td>\n",
       "      <td>0.072181</td>\n",
       "      <td>0.106955</td>\n",
       "      <td>0.127658</td>\n",
       "      <td>0.189793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>10001.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</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>13375.750000</td>\n",
       "      <td>0.069828</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>0.150685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>16750.500000</td>\n",
       "      <td>0.111241</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>0.273973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>20125.250000</td>\n",
       "      <td>0.152396</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.448980</td>\n",
       "      <td>0.424658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>23500.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          cit_index        fnlwgt  education_num  capital_gain  capital_loss  \\\n",
       "count  13500.000000  13500.000000   13500.000000  13500.000000  13500.000000   \n",
       "mean   16750.500000      0.119322       0.604706      0.010237      0.023363   \n",
       "std     3897.258652      0.072236       0.170157      0.072181      0.106955   \n",
       "min    10001.000000      0.000000       0.000000      0.000000      0.000000   \n",
       "25%    13375.750000      0.069828       0.533333      0.000000      0.000000   \n",
       "50%    16750.500000      0.111241       0.600000      0.000000      0.000000   \n",
       "75%    20125.250000      0.152396       0.733333      0.000000      0.000000   \n",
       "max    23500.000000      1.000000       1.000000      1.000000      1.000000   \n",
       "\n",
       "       hours_per_week           age  \n",
       "count    13500.000000  13500.000000  \n",
       "mean         0.401767      0.298212  \n",
       "std          0.127658      0.189793  \n",
       "min          0.000000      0.000000  \n",
       "25%          0.397959      0.150685  \n",
       "50%          0.397959      0.273973  \n",
       "75%          0.448980      0.424658  \n",
       "max          1.000000      1.000000  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test['fnlwgt']=(df_test['fnlwgt']-df_test['fnlwgt'].min())/(df_test['fnlwgt'].max()-df_test['fnlwgt'].min())\n",
    "df_test['education_num']=(df_test['education_num']-df_test['education_num'].min())/(df_test['education_num'].max()-df_test['education_num'].min())\n",
    "df_test['capital_gain']=(df_test['capital_gain']-df_test['capital_gain'].min())/(df_test['capital_gain'].max()-df_test['capital_gain'].min())\n",
    "df_test['capital_loss']=(df_test['capital_loss']-df_test['capital_loss'].min())/(df_test['capital_loss'].max()-df_test['capital_loss'].min())\n",
    "df_test['hours_per_week']=(df_test['hours_per_week']-df_test['hours_per_week'].min())/(df_test['hours_per_week'].max()-df_test['hours_per_week'].min())\n",
    "df_test['age']=(df_test['age']-df_test['age'].min())/(df_test['age'].max()-df_test['age'].min())\n",
    "df_test.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Final dataset for modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train=df['class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train=df.drop(['cit_index','birth_date','income_class','class','small'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.759231\n",
       "1    0.240769\n",
       "Name: class, dtype: float64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.value_counts()/y_train.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education_num</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital_gain</th>\n",
       "      <th>capital_loss</th>\n",
       "      <th>hours_per_week</th>\n",
       "      <th>native_country</th>\n",
       "      <th>age</th>\n",
       "      <th>agecat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>State-gov</td>\n",
       "      <td>0.044302</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.02174</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0.301370</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>0.048238</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.122449</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0.452055</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Private</td>\n",
       "      <td>0.138113</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0.287671</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Private</td>\n",
       "      <td>0.151068</td>\n",
       "      <td>11th</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0.493151</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Private</td>\n",
       "      <td>0.221488</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>0.150685</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           workclass    fnlwgt   education  education_num  \\\n",
       "0          State-gov  0.044302   Bachelors       0.800000   \n",
       "1   Self-emp-not-inc  0.048238   Bachelors       0.800000   \n",
       "2            Private  0.138113     HS-grad       0.533333   \n",
       "3            Private  0.151068        11th       0.400000   \n",
       "4            Private  0.221488   Bachelors       0.800000   \n",
       "\n",
       "        marital_status          occupation    relationship    race     sex  \\\n",
       "0        Never-married        Adm-clerical   Not-in-family   White    Male   \n",
       "1   Married-civ-spouse     Exec-managerial         Husband   White    Male   \n",
       "2             Divorced   Handlers-cleaners   Not-in-family   White    Male   \n",
       "3   Married-civ-spouse   Handlers-cleaners         Husband   Black    Male   \n",
       "4   Married-civ-spouse      Prof-specialty            Wife   Black  Female   \n",
       "\n",
       "   capital_gain  capital_loss  hours_per_week  native_country       age agecat  \n",
       "0       0.02174           0.0        0.397959   United-States  0.301370      3  \n",
       "1       0.00000           0.0        0.122449   United-States  0.452055      4  \n",
       "2       0.00000           0.0        0.397959   United-States  0.287671      3  \n",
       "3       0.00000           0.0        0.397959   United-States  0.493151      4  \n",
       "4       0.00000           0.0        0.397959            Cuba  0.150685      2  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 32500 entries, 0 to 32499\n",
      "Data columns (total 15 columns):\n",
      " #   Column          Non-Null Count  Dtype   \n",
      "---  ------          --------------  -----   \n",
      " 0   workclass       32500 non-null  object  \n",
      " 1   fnlwgt          32500 non-null  float32 \n",
      " 2   education       32500 non-null  object  \n",
      " 3   education_num   32500 non-null  float32 \n",
      " 4   marital_status  32500 non-null  object  \n",
      " 5   occupation      32500 non-null  object  \n",
      " 6   relationship    32500 non-null  object  \n",
      " 7   race            32500 non-null  object  \n",
      " 8   sex             32500 non-null  object  \n",
      " 9   capital_gain    32500 non-null  float64 \n",
      " 10  capital_loss    32500 non-null  float64 \n",
      " 11  hours_per_week  32500 non-null  float64 \n",
      " 12  native_country  32500 non-null  object  \n",
      " 13  age             32500 non-null  float64 \n",
      " 14  agecat          32500 non-null  category\n",
      "dtypes: category(1), float32(2), float64(4), object(8)\n",
      "memory usage: 3.3+ MB\n"
     ]
    }
   ],
   "source": [
    "X_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train[\"fnlwgt\"] = pd.to_numeric(X_train[\"fnlwgt\"], downcast=\"float\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 32500 entries, 0 to 32499\n",
      "Data columns (total 15 columns):\n",
      " #   Column          Non-Null Count  Dtype   \n",
      "---  ------          --------------  -----   \n",
      " 0   workclass       32500 non-null  object  \n",
      " 1   fnlwgt          32500 non-null  float32 \n",
      " 2   education       32500 non-null  object  \n",
      " 3   education_num   32500 non-null  float32 \n",
      " 4   marital_status  32500 non-null  object  \n",
      " 5   occupation      32500 non-null  object  \n",
      " 6   relationship    32500 non-null  object  \n",
      " 7   race            32500 non-null  object  \n",
      " 8   sex             32500 non-null  object  \n",
      " 9   capital_gain    32500 non-null  float64 \n",
      " 10  capital_loss    32500 non-null  float64 \n",
      " 11  hours_per_week  32500 non-null  float64 \n",
      " 12  native_country  32500 non-null  object  \n",
      " 13  age             32500 non-null  float64 \n",
      " 14  agecat          32500 non-null  category\n",
      "dtypes: category(1), float32(2), float64(4), object(8)\n",
      "memory usage: 3.3+ MB\n"
     ]
    }
   ],
   "source": [
    "X_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train=pd.get_dummies(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32500, 112)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV, StratifiedKFold\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC, LinearSVC\n",
    "from sklearn.ensemble import AdaBoostClassifier, ExtraTreesClassifier, GradientBoostingClassifier, RandomForestClassifier, VotingClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.1 SVM (Support-vector machine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=StratifiedKFold(n_splits=2, random_state=None, shuffle=False),\n",
       "             estimator=SVC(random_state=42), n_jobs=-1,\n",
       "             param_grid={'C': [1, 10, 50, 100, 200, 300, 1000],\n",
       "                         'gamma': [0.001, 0.01, 0.1, 1], 'kernel': ['rbf']},\n",
       "             scoring='accuracy')"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svr_params = {'kernel': ['rbf'],\n",
    "              'gamma': [0.001, 0.01, 0.1, 1],\n",
    "              'C': [1, 10, 50, 100, 200, 300, 1000]}\n",
    "svr_gridsearch = GridSearchCV(SVC(random_state=42),\n",
    "                              svr_params,\n",
    "                              cv=StratifiedKFold(n_splits=2),\n",
    "                              scoring='accuracy',\n",
    "                              n_jobs=-1)\n",
    "svr_gridsearch.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8627384615384616"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_svr = svr_gridsearch.best_estimator_.predict(X_train)\n",
    "accuracy_score(y_train, y_pred_svr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.2 Decision Trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=StratifiedKFold(n_splits=2, random_state=None, shuffle=False),\n",
       "             estimator=DecisionTreeClassifier(random_state=42), n_jobs=-1,\n",
       "             param_grid={'max_features': [1, 3, 10],\n",
       "                         'min_samples_leaf': [1, 3, 10],\n",
       "                         'min_samples_split': [2, 3, 10]},\n",
       "             scoring='accuracy')"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decisionTree_params = {'max_features': [1, 3, 10],\n",
    "                       'min_samples_split': [2, 3, 10],\n",
    "                       'min_samples_leaf': [1, 3, 10]}\n",
    "decisionTree_gridsearch = GridSearchCV(DecisionTreeClassifier(random_state=42),\n",
    "                                       decisionTree_params,\n",
    "                                       cv=StratifiedKFold(n_splits=2),\n",
    "                                       scoring='accuracy',\n",
    "                                       n_jobs=-1)\n",
    "decisionTree_gridsearch.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.869476923076923"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_decisionTree = decisionTree_gridsearch.best_estimator_.predict(X_train)\n",
    "accuracy_score(y_train, y_pred_decisionTree)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.3 Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=StratifiedKFold(n_splits=2, random_state=None, shuffle=False),\n",
       "             estimator=RandomForestClassifier(random_state=42), n_jobs=-1,\n",
       "             param_grid={'bootstrap': [False], 'criterion': ['gini'],\n",
       "                         'max_depth': [None], 'max_features': [1, 3, 10],\n",
       "                         'min_samples_leaf': [1, 3, 10],\n",
       "                         'min_samples_split': [2, 3, 10],\n",
       "                         'n_estimators': [100, 300]},\n",
       "             scoring='accuracy')"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rdmFrst_params = {'max_depth': [None],\n",
    "                  'max_features': [1, 3, 10],\n",
    "                  'min_samples_split': [2, 3, 10],\n",
    "                  'min_samples_leaf': [1, 3, 10],\n",
    "                  'bootstrap': [False],\n",
    "                  'n_estimators':[100,300],\n",
    "                  'criterion': ['gini']}\n",
    "rdmFrst_gridsearch = GridSearchCV(RandomForestClassifier(random_state=42),\n",
    "                                  rdmFrst_params,\n",
    "                                  cv=StratifiedKFold(n_splits=2),\n",
    "                                  scoring='accuracy',\n",
    "                                  n_jobs=-1)\n",
    "rdmFrst_gridsearch.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8992615384615384"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_rdmFrst = rdmFrst_gridsearch.best_estimator_.predict(X_train)\n",
    "accuracy_score(y_train, y_pred_rdmFrst)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.4 Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/envs/azureml_py38/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_reg = LogisticRegression()\n",
    "log_reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8545230769230769"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_logReg = log_reg.predict(X_train)\n",
    "accuracy_score(y_train, y_pred_logReg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.5 AdaBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=StratifiedKFold(n_splits=2, random_state=None, shuffle=False),\n",
       "             estimator=AdaBoostClassifier(base_estimator=DecisionTreeClassifier(random_state=42)),\n",
       "             n_jobs=-1,\n",
       "             param_grid={'algorithm': ['SAMME', 'SAMME.R'],\n",
       "                         'base_estimator__criterion': ['gini', 'entropy'],\n",
       "                         'base_estimator__splitter': ['best', 'random'],\n",
       "                         'learning_rate': [0.0001, 0.001, 0.01, 0.1, 0.2, 0.3,\n",
       "                                           1.5],\n",
       "                         'n_estimators': [1, 2]},\n",
       "             scoring='accuracy')"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ada_params = {'base_estimator__criterion': ['gini', 'entropy'],\n",
    "              'base_estimator__splitter': ['best', 'random'],\n",
    "              'algorithm': ['SAMME', 'SAMME.R'],\n",
    "              'n_estimators': [1, 2],\n",
    "              'learning_rate': [0.0001, 0.001, 0.01, 0.1, 0.2, 0.3, 1.5]}\n",
    "\n",
    "ada_gridsearch = GridSearchCV(AdaBoostClassifier(DecisionTreeClassifier(random_state=42)),\n",
    "                              param_grid = ada_params,\n",
    "                              cv=StratifiedKFold(n_splits=2),\n",
    "                              scoring='accuracy',\n",
    "                              n_jobs= -1)\n",
    "\n",
    "ada_gridsearch.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8151076923076923"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ada_gridsearch.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9999692307692307"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_ada = ada_gridsearch.best_estimator_.predict(X_train)\n",
    "accuracy_score(y_train, y_pred_ada)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.6 Extra Trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/envs/azureml_py38/lib/python3.8/site-packages/joblib/externals/loky/process_executor.py:702: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=StratifiedKFold(n_splits=3, random_state=None, shuffle=False),\n",
       "             estimator=ExtraTreesClassifier(random_state=42), n_jobs=-1,\n",
       "             param_grid={'bootstrap': [False], 'criterion': ['gini'],\n",
       "                         'max_depth': [None], 'max_features': [1, 3, 10],\n",
       "                         'min_samples_leaf': [1, 3, 10],\n",
       "                         'min_samples_split': [2, 3, 10],\n",
       "                         'n_estimators': [100, 300]},\n",
       "             scoring='accuracy')"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extraTree_params = {'max_depth': [None],\n",
    "                    'max_features': [1, 3, 10],\n",
    "                    'min_samples_split': [2, 3, 10],\n",
    "                    'min_samples_leaf': [1, 3, 10],\n",
    "                    'bootstrap': [False],\n",
    "                    'n_estimators': [100, 300],\n",
    "                    'criterion': ['gini']}\n",
    "extraTree_gridsearch = GridSearchCV(ExtraTreesClassifier(random_state=42),\n",
    "                                    extraTree_params,\n",
    "                                    cv=StratifiedKFold(n_splits=3),\n",
    "                                    scoring='accuracy',\n",
    "                                    n_jobs=-1)\n",
    "extraTree_gridsearch.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8476923954485516"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extraTree_gridsearch.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9341846153846154"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_extraTree = extraTree_gridsearch.best_estimator_.predict(X_train)\n",
    "accuracy_score(y_train, y_pred_extraTree)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.7 Gradient Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=StratifiedKFold(n_splits=2, random_state=None, shuffle=False),\n",
       "             estimator=GradientBoostingClassifier(random_state=42), n_jobs=-1,\n",
       "             param_grid={'learning_rate': [0.01, 0.05, 0.1],\n",
       "                         'loss': ['deviance'], 'max_depth': [4, 8],\n",
       "                         'max_features': [0.1, 0.3],\n",
       "                         'min_samples_leaf': [100, 150],\n",
       "                         'n_estimators': [100, 200, 300]},\n",
       "             scoring='accuracy')"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbrt_params = {'n_estimators': [100, 200, 300],\n",
    "               'loss': ['deviance'],\n",
    "               'learning_rate': [0.01, 0.05, 0.1],\n",
    "               'max_depth': [4, 8],\n",
    "               'min_samples_leaf': [100, 150],\n",
    "               'max_features': [0.1, 0.3]}\n",
    "gbrt_gridsearch = GridSearchCV(GradientBoostingClassifier(random_state=42),\n",
    "                               gbrt_params,\n",
    "                               cv=StratifiedKFold(n_splits=2),\n",
    "                               scoring='accuracy',\n",
    "                               n_jobs=-1)\n",
    "gbrt_gridsearch.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8692307692307693"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbrt_gridsearch.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8860615384615385"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_gbrt = gbrt_gridsearch.best_estimator_.predict(X_train)\n",
    "accuracy_score(y_train, y_pred_gbrt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.8 Voting classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/envs/azureml_py38/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "VotingClassifier(estimators=[('svm', SVC(C=100, gamma=0.01, random_state=42)),\n",
       "                             ('dt',\n",
       "                              DecisionTreeClassifier(max_features=10,\n",
       "                                                     min_samples_leaf=3,\n",
       "                                                     min_samples_split=10,\n",
       "                                                     random_state=42)),\n",
       "                             ('rf',\n",
       "                              RandomForestClassifier(bootstrap=False,\n",
       "                                                     max_features=10,\n",
       "                                                     min_samples_leaf=3,\n",
       "                                                     min_samples_split=10,\n",
       "                                                     random_state=42)),\n",
       "                             ('lr', LogisticRegression()),\n",
       "                             ('ada',\n",
       "                              AdaBoostClassifier...\n",
       "                                                 base_estimator=DecisionTreeClassifier(criterion='entropy',\n",
       "                                                                                       random_state=42),\n",
       "                                                 learning_rate=0.01,\n",
       "                                                 n_estimators=2)),\n",
       "                             ('extraTree',\n",
       "                              ExtraTreesClassifier(max_features=10,\n",
       "                                                   min_samples_split=10,\n",
       "                                                   n_estimators=300,\n",
       "                                                   random_state=42)),\n",
       "                             ('gbrt',\n",
       "                              GradientBoostingClassifier(learning_rate=0.05,\n",
       "                                                         max_depth=8,\n",
       "                                                         max_features=0.3,\n",
       "                                                         min_samples_leaf=100,\n",
       "                                                         n_estimators=300,\n",
       "                                                         random_state=42))])"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voting_hard_clf = VotingClassifier(\n",
    "    estimators=[('svm', svr_gridsearch.best_estimator_),\n",
    "                ('dt', decisionTree_gridsearch.best_estimator_),\n",
    "                ('rf', rdmFrst_gridsearch.best_estimator_),\n",
    "                ('lr', log_reg),\n",
    "                ('ada', ada_gridsearch.best_estimator_),\n",
    "                ('extraTree', extraTree_gridsearch.best_estimator_),\n",
    "                ('gbrt', gbrt_gridsearch.best_estimator_)],\n",
    "    voting='hard'\n",
    ")\n",
    "\n",
    "voting_hard_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8989846153846154"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_voting_hard = voting_hard_clf.predict(X_train)\n",
    "accuracy_score(y_train, y_pred_voting_hard)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VotingClassifier(estimators=[('svm', SVC(C=100, gamma=0.01, random_state=42)),\n",
       "                             ('rf',\n",
       "                              RandomForestClassifier(bootstrap=False,\n",
       "                                                     max_features=10,\n",
       "                                                     min_samples_leaf=3,\n",
       "                                                     min_samples_split=10,\n",
       "                                                     random_state=42)),\n",
       "                             ('ada',\n",
       "                              AdaBoostClassifier(algorithm='SAMME',\n",
       "                                                 base_estimator=DecisionTreeClassifier(criterion='entropy',\n",
       "                                                                                       random_state=42),\n",
       "                                                 learning_rate=0.01,\n",
       "                                                 n_estimators=2)),\n",
       "                             ('extraTree',\n",
       "                              ExtraTreesClassifier(max_features=10,\n",
       "                                                   min_samples_split=10,\n",
       "                                                   n_estimators=300,\n",
       "                                                   random_state=42)),\n",
       "                             ('gbrt',\n",
       "                              GradientBoostingClassifier(learning_rate=0.05,\n",
       "                                                         max_depth=8,\n",
       "                                                         max_features=0.3,\n",
       "                                                         min_samples_leaf=100,\n",
       "                                                         n_estimators=300,\n",
       "                                                         random_state=42))])"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voting_hard_clf_90 = VotingClassifier(\n",
    "    estimators=[('svm', svr_gridsearch.best_estimator_),\n",
    "                ('rf', rdmFrst_gridsearch.best_estimator_),\n",
    "                ('ada', ada_gridsearch.best_estimator_),\n",
    "                ('extraTree', extraTree_gridsearch.best_estimator_),\n",
    "                ('gbrt', gbrt_gridsearch.best_estimator_)],\n",
    "    voting='hard'\n",
    ")\n",
    "\n",
    "voting_hard_clf_90.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9070153846153847"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_voting_hard_90 = voting_hard_clf_90.predict(X_train)\n",
    "accuracy_score(y_train, y_pred_voting_hard_90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/envs/azureml_py38/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "VotingClassifier(estimators=[('svm',\n",
       "                              SVC(C=100, gamma=0.01, probability=True,\n",
       "                                  random_state=42)),\n",
       "                             ('dt',\n",
       "                              DecisionTreeClassifier(max_features=10,\n",
       "                                                     min_samples_leaf=3,\n",
       "                                                     min_samples_split=10,\n",
       "                                                     random_state=42)),\n",
       "                             ('rf',\n",
       "                              RandomForestClassifier(bootstrap=False,\n",
       "                                                     max_features=10,\n",
       "                                                     min_samples_leaf=3,\n",
       "                                                     min_samples_split=10,\n",
       "                                                     random_state=42)),\n",
       "                             ('lr', LogisticRegression()),\n",
       "                             ('ada',\n",
       "                              A...\n",
       "                                                 base_estimator=DecisionTreeClassifier(criterion='entropy',\n",
       "                                                                                       random_state=42),\n",
       "                                                 learning_rate=0.01,\n",
       "                                                 n_estimators=2)),\n",
       "                             ('extraTree',\n",
       "                              ExtraTreesClassifier(max_features=10,\n",
       "                                                   min_samples_split=10,\n",
       "                                                   n_estimators=300,\n",
       "                                                   random_state=42)),\n",
       "                             ('gbrt',\n",
       "                              GradientBoostingClassifier(learning_rate=0.05,\n",
       "                                                         max_depth=8,\n",
       "                                                         max_features=0.3,\n",
       "                                                         min_samples_leaf=100,\n",
       "                                                         n_estimators=300,\n",
       "                                                         random_state=42))],\n",
       "                 voting='soft')"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svr_gridsearch.best_estimator_.probability = True\n",
    "voting_soft_clf = VotingClassifier(\n",
    "    estimators=[('svm', svr_gridsearch.best_estimator_),\n",
    "                ('dt', decisionTree_gridsearch.best_estimator_),\n",
    "                ('rf', rdmFrst_gridsearch.best_estimator_),\n",
    "                ('lr', log_reg),\n",
    "                ('ada', ada_gridsearch.best_estimator_),\n",
    "                ('extraTree', extraTree_gridsearch.best_estimator_),\n",
    "                ('gbrt', gbrt_gridsearch.best_estimator_)],\n",
    "    voting='soft'\n",
    ")\n",
    "\n",
    "voting_soft_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9050153846153847"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_voting_soft = voting_soft_clf.predict(X_train)\n",
    "accuracy_score(y_train, y_pred_voting_soft)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Compare different models' accuracy score & best score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy_score_df = pd.DataFrame({'Models': ['SVM',\n",
    "                                             'Decision Trees',\n",
    "                                             'Random Forest',\n",
    "                                             'Logistic Regression',\n",
    "                                             'AdaBoost',\n",
    "                                             'Extra Trees',\n",
    "                                             'Gradient Boosting',\n",
    "                                             'Hard voting',\n",
    "                                             'Hard voting gt 90pct',\n",
    "                                             'Soft voting'],\n",
    "                                  'Accuracy score': [accuracy_score(y_train, y_pred_svr),\n",
    "                                                     accuracy_score(y_train, y_pred_decisionTree),\n",
    "                                                     accuracy_score(y_train, y_pred_rdmFrst),\n",
    "                                                     accuracy_score(y_train, y_pred_logReg),\n",
    "                                                     accuracy_score(y_train, y_pred_ada),\n",
    "                                                     accuracy_score(y_train, y_pred_extraTree),\n",
    "                                                     accuracy_score(y_train, y_pred_gbrt),\n",
    "                                                     accuracy_score(y_train, y_pred_voting_hard),\n",
    "                                                     accuracy_score(y_train, y_pred_voting_hard_90),\n",
    "                                                     accuracy_score(y_train, y_pred_voting_soft)]},\n",
    "                                 columns=['Models', 'Accuracy score'])\n",
    "accuracy_score_df.sort_values('Accuracy score', inplace=True, ascending=False)\n",
    "accuracy_score_df.reset_index(inplace=True, drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "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>Models</th>\n",
       "      <th>Accuracy score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AdaBoost</td>\n",
       "      <td>0.999969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Extra Trees</td>\n",
       "      <td>0.934185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Hard voting gt 90pct</td>\n",
       "      <td>0.907015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Soft voting</td>\n",
       "      <td>0.905015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.899262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Hard voting</td>\n",
       "      <td>0.898985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Gradient Boosting</td>\n",
       "      <td>0.886062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Decision Trees</td>\n",
       "      <td>0.869477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>SVM</td>\n",
       "      <td>0.862738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.854523</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Models  Accuracy score\n",
       "0              AdaBoost        0.999969\n",
       "1           Extra Trees        0.934185\n",
       "2  Hard voting gt 90pct        0.907015\n",
       "3           Soft voting        0.905015\n",
       "4         Random Forest        0.899262\n",
       "5           Hard voting        0.898985\n",
       "6     Gradient Boosting        0.886062\n",
       "7        Decision Trees        0.869477\n",
       "8                   SVM        0.862738\n",
       "9   Logistic Regression        0.854523"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 5))\n",
    "plt.barh(np.arange(len(accuracy_score_df['Models'])),\n",
    "         accuracy_score_df['Accuracy score'],\n",
    "         align='center',\n",
    "         height=0.5)\n",
    "\n",
    "plt.yticks(np.arange(len(accuracy_score_df['Models'])), accuracy_score_df['Models'])\n",
    "plt.tick_params(labelsize=12)\n",
    "plt.xlabel('Accuracy score', fontdict={'fontsize': 13})\n",
    "plt.ylabel('Models', fontdict={'fontsize': 13})\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_score_df = pd.DataFrame({'Models': ['SVM',\n",
    "                                         'Decision Trees',\n",
    "                                         'Random Forest',\n",
    "                                         'AdaBoost',\n",
    "                                         'Extra Trees',\n",
    "                                         'Gradient Boosting'],\n",
    "                              'best_score_': [svr_gridsearch.best_score_,\n",
    "                                              decisionTree_gridsearch.best_score_,\n",
    "                                              rdmFrst_gridsearch.best_score_,\n",
    "                                              ada_gridsearch.best_score_,\n",
    "                                              extraTree_gridsearch.best_score_,\n",
    "                                              gbrt_gridsearch.best_score_]},\n",
    "                             columns=['Models', 'best_score_'])\n",
    "best_score_df.sort_values('best_score_', inplace=True, ascending=False)\n",
    "best_score_df.reset_index(inplace=True, drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "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>Models</th>\n",
       "      <th>best_score_</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Gradient Boosting</td>\n",
       "      <td>0.869231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.862092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SVM</td>\n",
       "      <td>0.854154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Extra Trees</td>\n",
       "      <td>0.847692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Decision Trees</td>\n",
       "      <td>0.834246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AdaBoost</td>\n",
       "      <td>0.815108</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Models  best_score_\n",
       "0  Gradient Boosting     0.869231\n",
       "1      Random Forest     0.862092\n",
       "2                SVM     0.854154\n",
       "3        Extra Trees     0.847692\n",
       "4     Decision Trees     0.834246\n",
       "5           AdaBoost     0.815108"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_score_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 5))\n",
    "plt.barh(np.arange(len(best_score_df['Models'])), best_score_df['best_score_'], align='center', height=0.5)\n",
    "\n",
    "plt.yticks(np.arange(len(best_score_df['Models'])), best_score_df['Models'])\n",
    "plt.tick_params(labelsize=12)\n",
    "plt.xlabel('best_score_', fontdict={'fontsize': 13})\n",
    "plt.ylabel('Models', fontdict={'fontsize': 13})\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test = voting_soft_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "passengerID_test = test_df['PassengerId']\n",
    "output_df = pd.DataFrame({'PassengerId':passengerID_test,\n",
    "                          'Survived': y_test},\n",
    "                         columns=['PassengerId', 'Survived'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "output_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(output_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "output_df.to_csv('titanic_predict_survivals.csv', index=False)"
   ]
  }
 ],
 "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
}