Logistic-Regression-Adaboost/eda-2a.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>age</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</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</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</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</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</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</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</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</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</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</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</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32556</th>\n",
       "      <td>27</td>\n",
       "      <td>Private</td>\n",
       "      <td>257302</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Tech-support</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32557</th>\n",
       "      <td>40</td>\n",
       "      <td>Private</td>\n",
       "      <td>154374</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32558</th>\n",
       "      <td>58</td>\n",
       "      <td>Private</td>\n",
       "      <td>151910</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32559</th>\n",
       "      <td>22</td>\n",
       "      <td>Private</td>\n",
       "      <td>201490</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32560</th>\n",
       "      <td>52</td>\n",
       "      <td>Self-emp-inc</td>\n",
       "      <td>287927</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>15024</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32561 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age          workclass  fnlwgt    education  education-num  \\\n",
       "0       39          State-gov   77516    Bachelors             13   \n",
       "1       50   Self-emp-not-inc   83311    Bachelors             13   \n",
       "2       38            Private  215646      HS-grad              9   \n",
       "3       53            Private  234721         11th              7   \n",
       "4       28            Private  338409    Bachelors             13   \n",
       "...    ...                ...     ...          ...            ...   \n",
       "32556   27            Private  257302   Assoc-acdm             12   \n",
       "32557   40            Private  154374      HS-grad              9   \n",
       "32558   58            Private  151910      HS-grad              9   \n",
       "32559   22            Private  201490      HS-grad              9   \n",
       "32560   52       Self-emp-inc  287927      HS-grad              9   \n",
       "\n",
       "            marital-status          occupation    relationship    race  \\\n",
       "0            Never-married        Adm-clerical   Not-in-family   White   \n",
       "1       Married-civ-spouse     Exec-managerial         Husband   White   \n",
       "2                 Divorced   Handlers-cleaners   Not-in-family   White   \n",
       "3       Married-civ-spouse   Handlers-cleaners         Husband   Black   \n",
       "4       Married-civ-spouse      Prof-specialty            Wife   Black   \n",
       "...                    ...                 ...             ...     ...   \n",
       "32556   Married-civ-spouse        Tech-support            Wife   White   \n",
       "32557   Married-civ-spouse   Machine-op-inspct         Husband   White   \n",
       "32558              Widowed        Adm-clerical       Unmarried   White   \n",
       "32559        Never-married        Adm-clerical       Own-child   White   \n",
       "32560   Married-civ-spouse     Exec-managerial            Wife   White   \n",
       "\n",
       "           sex  capital-gain  capital-loss  hours-per-week  native-country  \\\n",
       "0         Male          2174             0              40   United-States   \n",
       "1         Male             0             0              13   United-States   \n",
       "2         Male             0             0              40   United-States   \n",
       "3         Male             0             0              40   United-States   \n",
       "4       Female             0             0              40            Cuba   \n",
       "...        ...           ...           ...             ...             ...   \n",
       "32556   Female             0             0              38   United-States   \n",
       "32557     Male             0             0              40   United-States   \n",
       "32558   Female             0             0              40   United-States   \n",
       "32559     Male             0             0              20   United-States   \n",
       "32560   Female         15024             0              40   United-States   \n",
       "\n",
       "       income  \n",
       "0       <=50K  \n",
       "1       <=50K  \n",
       "2       <=50K  \n",
       "3       <=50K  \n",
       "4       <=50K  \n",
       "...       ...  \n",
       "32556   <=50K  \n",
       "32557    >50K  \n",
       "32558   <=50K  \n",
       "32559   <=50K  \n",
       "32560    >50K  \n",
       "\n",
       "[32561 rows x 15 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# column names for the data\n",
    "columns = [\n",
    "    'age', 'workclass', 'fnlwgt', 'education', 'education-num',\n",
    "    'marital-status', 'occupation', 'relationship', 'race', 'sex',\n",
    "    'capital-gain', 'capital-loss', 'hours-per-week', 'native-country', 'income'\n",
    "]\n",
    "\n",
    "# read in the data and set the column names\n",
    "df = pd.read_csv('Dataset-2/adult.data', names=columns)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>age</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</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>|1x3 Cross validator</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25</td>\n",
       "      <td>Private</td>\n",
       "      <td>226802.0</td>\n",
       "      <td>11th</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>89814.0</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Farming-fishing</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>28</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>336951.0</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Protective-serv</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>44</td>\n",
       "      <td>Private</td>\n",
       "      <td>160323.0</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>7688.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16277</th>\n",
       "      <td>39</td>\n",
       "      <td>Private</td>\n",
       "      <td>215419.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16278</th>\n",
       "      <td>64</td>\n",
       "      <td>?</td>\n",
       "      <td>321403.0</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>?</td>\n",
       "      <td>Other-relative</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16279</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>374983.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13.0</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16280</th>\n",
       "      <td>44</td>\n",
       "      <td>Private</td>\n",
       "      <td>83891.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Male</td>\n",
       "      <td>5455.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16281</th>\n",
       "      <td>35</td>\n",
       "      <td>Self-emp-inc</td>\n",
       "      <td>182148.0</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16282 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        age      workclass    fnlwgt      education  \\\n",
       "0      |1x3 Cross validator            NaN       NaN            NaN   \n",
       "1                        25        Private  226802.0           11th   \n",
       "2                        38        Private   89814.0        HS-grad   \n",
       "3                        28      Local-gov  336951.0     Assoc-acdm   \n",
       "4                        44        Private  160323.0   Some-college   \n",
       "...                     ...            ...       ...            ...   \n",
       "16277                    39        Private  215419.0      Bachelors   \n",
       "16278                    64              ?  321403.0        HS-grad   \n",
       "16279                    38        Private  374983.0      Bachelors   \n",
       "16280                    44        Private   83891.0      Bachelors   \n",
       "16281                    35   Self-emp-inc  182148.0      Bachelors   \n",
       "\n",
       "       education-num       marital-status          occupation  \\\n",
       "0                NaN                  NaN                 NaN   \n",
       "1                7.0        Never-married   Machine-op-inspct   \n",
       "2                9.0   Married-civ-spouse     Farming-fishing   \n",
       "3               12.0   Married-civ-spouse     Protective-serv   \n",
       "4               10.0   Married-civ-spouse   Machine-op-inspct   \n",
       "...              ...                  ...                 ...   \n",
       "16277           13.0             Divorced      Prof-specialty   \n",
       "16278            9.0              Widowed                   ?   \n",
       "16279           13.0   Married-civ-spouse      Prof-specialty   \n",
       "16280           13.0             Divorced        Adm-clerical   \n",
       "16281           13.0   Married-civ-spouse     Exec-managerial   \n",
       "\n",
       "          relationship                 race      sex  capital-gain  \\\n",
       "0                  NaN                  NaN      NaN           NaN   \n",
       "1            Own-child                Black     Male           0.0   \n",
       "2              Husband                White     Male           0.0   \n",
       "3              Husband                White     Male           0.0   \n",
       "4              Husband                Black     Male        7688.0   \n",
       "...                ...                  ...      ...           ...   \n",
       "16277    Not-in-family                White   Female           0.0   \n",
       "16278   Other-relative                Black     Male           0.0   \n",
       "16279          Husband                White     Male           0.0   \n",
       "16280        Own-child   Asian-Pac-Islander     Male        5455.0   \n",
       "16281          Husband                White     Male           0.0   \n",
       "\n",
       "       capital-loss  hours-per-week  native-country   income  \n",
       "0               NaN             NaN             NaN      NaN  \n",
       "1               0.0            40.0   United-States   <=50K.  \n",
       "2               0.0            50.0   United-States   <=50K.  \n",
       "3               0.0            40.0   United-States    >50K.  \n",
       "4               0.0            40.0   United-States    >50K.  \n",
       "...             ...             ...             ...      ...  \n",
       "16277           0.0            36.0   United-States   <=50K.  \n",
       "16278           0.0            40.0   United-States   <=50K.  \n",
       "16279           0.0            50.0   United-States   <=50K.  \n",
       "16280           0.0            40.0   United-States   <=50K.  \n",
       "16281           0.0            60.0   United-States    >50K.  \n",
       "\n",
       "[16282 rows x 15 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read the test data\n",
    "df_test = pd.read_csv('Dataset-2/adult.test', names=columns)\n",
    "df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data shape:  (32561, 15)\n",
      "Test data shape:  (16282, 15)\n"
     ]
    }
   ],
   "source": [
    "# percentage of train and test data\n",
    "print('Train data shape: ', df.shape)\n",
    "print('Test data shape: ', df_test.shape)\n",
    "\n",
    "# 1/3 of the data is test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                int64\n",
       "workclass         object\n",
       "fnlwgt             int64\n",
       "education         object\n",
       "education-num      int64\n",
       "marital-status    object\n",
       "occupation        object\n",
       "relationship      object\n",
       "race              object\n",
       "sex               object\n",
       "capital-gain       int64\n",
       "capital-loss       int64\n",
       "hours-per-week     int64\n",
       "native-country    object\n",
       "income            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find data types of the columns\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16281, 15)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()\n",
    "df.isna().sum()\n",
    "\n",
    "# look for missing values in the data\n",
    "df_test.isnull().sum()\n",
    "df_test.isna().sum()\n",
    "\n",
    "df_test.drop(0, axis = 0, inplace=True)\n",
    "df_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([39, 50, 38, 53, 28, 37, 49, 52, 31, 42, 30, 23, 32, 40, 34, 25, 43,\n",
       "       54, 35, 59, 56, 19, 20, 45, 22, 48, 21, 24, 57, 44, 41, 29, 18, 47,\n",
       "       46, 36, 79, 27, 67, 33, 76, 17, 55, 61, 70, 64, 71, 68, 66, 51, 58,\n",
       "       26, 60, 90, 75, 65, 77, 62, 63, 80, 72, 74, 69, 73, 81, 78, 88, 82,\n",
       "       83, 84, 85, 86, 87])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the unique values of the age column\n",
    "df['age'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>age</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>32561.000000</td>\n",
       "      <td>3.256100e+04</td>\n",
       "      <td>32561.000000</td>\n",
       "      <td>32561.000000</td>\n",
       "      <td>32561.000000</td>\n",
       "      <td>32561.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>38.581647</td>\n",
       "      <td>1.897784e+05</td>\n",
       "      <td>10.080679</td>\n",
       "      <td>1077.648844</td>\n",
       "      <td>87.303830</td>\n",
       "      <td>40.437456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13.640433</td>\n",
       "      <td>1.055500e+05</td>\n",
       "      <td>2.572720</td>\n",
       "      <td>7385.292085</td>\n",
       "      <td>402.960219</td>\n",
       "      <td>12.347429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>1.228500e+04</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>1.178270e+05</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>37.000000</td>\n",
       "      <td>1.783560e+05</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>48.000000</td>\n",
       "      <td>2.370510e+05</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>45.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>90.000000</td>\n",
       "      <td>1.484705e+06</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>99999.000000</td>\n",
       "      <td>4356.000000</td>\n",
       "      <td>99.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                age        fnlwgt  education-num  capital-gain  capital-loss  \\\n",
       "count  32561.000000  3.256100e+04   32561.000000  32561.000000  32561.000000   \n",
       "mean      38.581647  1.897784e+05      10.080679   1077.648844     87.303830   \n",
       "std       13.640433  1.055500e+05       2.572720   7385.292085    402.960219   \n",
       "min       17.000000  1.228500e+04       1.000000      0.000000      0.000000   \n",
       "25%       28.000000  1.178270e+05       9.000000      0.000000      0.000000   \n",
       "50%       37.000000  1.783560e+05      10.000000      0.000000      0.000000   \n",
       "75%       48.000000  2.370510e+05      12.000000      0.000000      0.000000   \n",
       "max       90.000000  1.484705e+06      16.000000  99999.000000   4356.000000   \n",
       "\n",
       "       hours-per-week  \n",
       "count    32561.000000  \n",
       "mean        40.437456  \n",
       "std         12.347429  \n",
       "min          1.000000  \n",
       "25%         40.000000  \n",
       "50%         40.000000  \n",
       "75%         45.000000  \n",
       "max         99.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# numerical features of the data\n",
    "numerical_features = df.select_dtypes(include=['int64'])\n",
    "numerical_features.columns\n",
    "# look for outliers in all the numerical columns\n",
    "df.boxplot(numerical_features.columns.to_list())\n",
    "df.describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# look for the distribution of the numerical columns\n",
    "df.hist(numerical_features.columns.to_list())\n",
    "\n",
    "# increase vertical spacing between the plots\n",
    "plt.subplots_adjust(hspace=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x2500 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "category_features = df.select_dtypes(include=['object'])\n",
    "category_features.columns\n",
    "\n",
    "# look for the distribution of the categorical columns\n",
    "# combine all the plots into one\n",
    "fig, axes = plt.subplots(9, 1, figsize=(15, 25))\n",
    "axes = axes.flatten()\n",
    "for i, j in zip(category_features.columns.to_list(), axes):\n",
    "    sns.countplot(x=i, data=df, ax=j)\n",
    "    plt.tight_layout()\n",
    "    plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# replace '?' with 'unknown'\n",
    "df.replace(' ?', 'unknown', inplace=True)\n",
    "df_test.replace(' ?', 'unknown', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "workclass 0\n",
      "workclass 0\n",
      "education 0\n",
      "education 0\n",
      "marital-status 0\n",
      "marital-status 0\n",
      "occupation 0\n",
      "occupation 0\n",
      "relationship 0\n",
      "relationship 0\n",
      "race 0\n",
      "race 0\n",
      "sex 0\n",
      "sex 0\n",
      "native-country 0\n",
      "native-country 0\n",
      "income 0\n",
      "income 0\n"
     ]
    }
   ],
   "source": [
    "# How many category columns have '?' values\n",
    "for i in category_features.columns.to_list():\n",
    "    print(i, df[i].str.contains('\\?').sum())\n",
    "    print(i, df_test[i].str.contains('\\?').sum())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "workclass {' Private': 22696, ' Self-emp-not-inc': 2541, ' Local-gov': 2093, 'unknown': 1836, ' State-gov': 1298, ' Self-emp-inc': 1116, ' Federal-gov': 960, ' Without-pay': 14, ' Never-worked': 7}\n",
      "education {' HS-grad': 10501, ' Some-college': 7291, ' Bachelors': 5355, ' Masters': 1723, ' Assoc-voc': 1382, ' 11th': 1175, ' Assoc-acdm': 1067, ' 10th': 933, ' 7th-8th': 646, ' Prof-school': 576, ' 9th': 514, ' 12th': 433, ' Doctorate': 413, ' 5th-6th': 333, ' 1st-4th': 168, ' Preschool': 51}\n",
      "marital-status {' Married-civ-spouse': 14976, ' Never-married': 10683, ' Divorced': 4443, ' Separated': 1025, ' Widowed': 993, ' Married-spouse-absent': 418, ' Married-AF-spouse': 23}\n",
      "occupation {' Prof-specialty': 4140, ' Craft-repair': 4099, ' Exec-managerial': 4066, ' Adm-clerical': 3770, ' Sales': 3650, ' Other-service': 3295, ' Machine-op-inspct': 2002, 'unknown': 1843, ' Transport-moving': 1597, ' Handlers-cleaners': 1370, ' Farming-fishing': 994, ' Tech-support': 928, ' Protective-serv': 649, ' Priv-house-serv': 149, ' Armed-Forces': 9}\n",
      "relationship {' Husband': 13193, ' Not-in-family': 8305, ' Own-child': 5068, ' Unmarried': 3446, ' Wife': 1568, ' Other-relative': 981}\n",
      "race {' White': 27816, ' Black': 3124, ' Asian-Pac-Islander': 1039, ' Amer-Indian-Eskimo': 311, ' Other': 271}\n",
      "sex {' Male': 21790, ' Female': 10771}\n",
      "native-country {' United-States': 29170, ' Mexico': 643, 'unknown': 583, ' Philippines': 198, ' Germany': 137, ' Canada': 121, ' Puerto-Rico': 114, ' El-Salvador': 106, ' India': 100, ' Cuba': 95, ' England': 90, ' Jamaica': 81, ' South': 80, ' China': 75, ' Italy': 73, ' Dominican-Republic': 70, ' Vietnam': 67, ' Guatemala': 64, ' Japan': 62, ' Poland': 60, ' Columbia': 59, ' Taiwan': 51, ' Haiti': 44, ' Iran': 43, ' Portugal': 37, ' Nicaragua': 34, ' Peru': 31, ' France': 29, ' Greece': 29, ' Ecuador': 28, ' Ireland': 24, ' Hong': 20, ' Cambodia': 19, ' Trinadad&Tobago': 19, ' Laos': 18, ' Thailand': 18, ' Yugoslavia': 16, ' Outlying-US(Guam-USVI-etc)': 14, ' Honduras': 13, ' Hungary': 13, ' Scotland': 12, ' Holand-Netherlands': 1}\n",
      "income {' <=50K': 24720, ' >50K': 7841}\n"
     ]
    }
   ],
   "source": [
    "# look for values in the category columns with count 0\n",
    "for i in category_features.columns.to_list():\n",
    "    print(i, df[i].value_counts().to_dict())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "workclass 9\n",
      "education 16\n",
      "marital-status 7\n",
      "occupation 15\n",
      "relationship 6\n",
      "race 5\n",
      "sex 2\n",
      "native-country 42\n",
      "income 2\n"
     ]
    }
   ],
   "source": [
    "# count the unique values in the category columns\n",
    "for i in category_features.columns.to_list():\n",
    "    print(i, df[i].nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# impute the unknown values with the mode of the column\n",
    "for i in category_features.columns.to_list():\n",
    "    df[i] = df[i].replace('unknown', df[i].mode()[0])\n",
    "    df_test[i] = df_test[i].replace('unknown', df_test[i].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# modify the income column to 0 and 1\n",
    "df['income'] = df['income'].replace(' <=50K', 0)\n",
    "df['income'] = df['income'].replace(' >50K', 1)\n",
    "df_test['income'] = df_test['income'].replace(' <=50K.', 0)\n",
    "df_test['income'] = df_test['income'].replace(' >50K.', 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(32561, 14) (32561,)\n"
     ]
    }
   ],
   "source": [
    "y = df['income']\n",
    "X = df.drop(['income'], axis=1)\n",
    "print(X.shape, y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>age</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>sex_ Male</th>\n",
       "      <th>workclass_ Federal-gov</th>\n",
       "      <th>workclass_ Local-gov</th>\n",
       "      <th>workclass_ Never-worked</th>\n",
       "      <th>...</th>\n",
       "      <th>native-country_ Portugal</th>\n",
       "      <th>native-country_ Puerto-Rico</th>\n",
       "      <th>native-country_ Scotland</th>\n",
       "      <th>native-country_ South</th>\n",
       "      <th>native-country_ Taiwan</th>\n",
       "      <th>native-country_ Thailand</th>\n",
       "      <th>native-country_ Trinadad&amp;Tobago</th>\n",
       "      <th>native-country_ United-States</th>\n",
       "      <th>native-country_ Vietnam</th>\n",
       "      <th>native-country_ Yugoslavia</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>77516</td>\n",
       "      <td>13</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>83311</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>215646</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>234721</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>338409</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32556</th>\n",
       "      <td>27</td>\n",
       "      <td>257302</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32557</th>\n",
       "      <td>40</td>\n",
       "      <td>154374</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32558</th>\n",
       "      <td>58</td>\n",
       "      <td>151910</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32559</th>\n",
       "      <td>22</td>\n",
       "      <td>201490</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32560</th>\n",
       "      <td>52</td>\n",
       "      <td>287927</td>\n",
       "      <td>9</td>\n",
       "      <td>15024</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32561 rows × 104 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  fnlwgt  education-num  capital-gain  capital-loss  hours-per-week  \\\n",
       "0       39   77516             13          2174             0              40   \n",
       "1       50   83311             13             0             0              13   \n",
       "2       38  215646              9             0             0              40   \n",
       "3       53  234721              7             0             0              40   \n",
       "4       28  338409             13             0             0              40   \n",
       "...    ...     ...            ...           ...           ...             ...   \n",
       "32556   27  257302             12             0             0              38   \n",
       "32557   40  154374              9             0             0              40   \n",
       "32558   58  151910              9             0             0              40   \n",
       "32559   22  201490              9             0             0              20   \n",
       "32560   52  287927              9         15024             0              40   \n",
       "\n",
       "       sex_ Male  workclass_ Federal-gov  workclass_ Local-gov  \\\n",
       "0              1                       0                     0   \n",
       "1              1                       0                     0   \n",
       "2              1                       0                     0   \n",
       "3              1                       0                     0   \n",
       "4              0                       0                     0   \n",
       "...          ...                     ...                   ...   \n",
       "32556          0                       0                     0   \n",
       "32557          1                       0                     0   \n",
       "32558          0                       0                     0   \n",
       "32559          1                       0                     0   \n",
       "32560          0                       0                     0   \n",
       "\n",
       "       workclass_ Never-worked  ...  native-country_ Portugal  \\\n",
       "0                            0  ...                         0   \n",
       "1                            0  ...                         0   \n",
       "2                            0  ...                         0   \n",
       "3                            0  ...                         0   \n",
       "4                            0  ...                         0   \n",
       "...                        ...  ...                       ...   \n",
       "32556                        0  ...                         0   \n",
       "32557                        0  ...                         0   \n",
       "32558                        0  ...                         0   \n",
       "32559                        0  ...                         0   \n",
       "32560                        0  ...                         0   \n",
       "\n",
       "       native-country_ Puerto-Rico  native-country_ Scotland  \\\n",
       "0                                0                         0   \n",
       "1                                0                         0   \n",
       "2                                0                         0   \n",
       "3                                0                         0   \n",
       "4                                0                         0   \n",
       "...                            ...                       ...   \n",
       "32556                            0                         0   \n",
       "32557                            0                         0   \n",
       "32558                            0                         0   \n",
       "32559                            0                         0   \n",
       "32560                            0                         0   \n",
       "\n",
       "       native-country_ South  native-country_ Taiwan  \\\n",
       "0                          0                       0   \n",
       "1                          0                       0   \n",
       "2                          0                       0   \n",
       "3                          0                       0   \n",
       "4                          0                       0   \n",
       "...                      ...                     ...   \n",
       "32556                      0                       0   \n",
       "32557                      0                       0   \n",
       "32558                      0                       0   \n",
       "32559                      0                       0   \n",
       "32560                      0                       0   \n",
       "\n",
       "       native-country_ Thailand  native-country_ Trinadad&Tobago  \\\n",
       "0                             0                                0   \n",
       "1                             0                                0   \n",
       "2                             0                                0   \n",
       "3                             0                                0   \n",
       "4                             0                                0   \n",
       "...                         ...                              ...   \n",
       "32556                         0                                0   \n",
       "32557                         0                                0   \n",
       "32558                         0                                0   \n",
       "32559                         0                                0   \n",
       "32560                         0                                0   \n",
       "\n",
       "       native-country_ United-States  native-country_ Vietnam  \\\n",
       "0                                  1                        0   \n",
       "1                                  1                        0   \n",
       "2                                  1                        0   \n",
       "3                                  1                        0   \n",
       "4                                  0                        0   \n",
       "...                              ...                      ...   \n",
       "32556                              1                        0   \n",
       "32557                              1                        0   \n",
       "32558                              1                        0   \n",
       "32559                              1                        0   \n",
       "32560                              1                        0   \n",
       "\n",
       "       native-country_ Yugoslavia  \n",
       "0                               0  \n",
       "1                               0  \n",
       "2                               0  \n",
       "3                               0  \n",
       "4                               0  \n",
       "...                           ...  \n",
       "32556                           0  \n",
       "32557                           0  \n",
       "32558                           0  \n",
       "32559                           0  \n",
       "32560                           0  \n",
       "\n",
       "[32561 rows x 104 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "category_features.drop(['sex', 'income'], axis=1, inplace=True)\n",
    "# one hot encode the 'sex' column\n",
    "X = pd.get_dummies(X, drop_first=True, columns=['sex'])\n",
    "X = pd.get_dummies(X, columns=category_features.columns.to_list()).astype('int64')\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['age', 'fnlwgt', 'education-num', 'capital-gain', 'capital-loss', 'hours-per-week', 'sex_ Male', 'workclass_ Federal-gov', 'workclass_ Local-gov', 'workclass_ Never-worked', 'workclass_ Private', 'workclass_ Self-emp-inc', 'workclass_ Self-emp-not-inc', 'workclass_ State-gov', 'workclass_ Without-pay', 'education_ 10th', 'education_ 11th', 'education_ 12th', 'education_ 1st-4th', 'education_ 5th-6th', 'education_ 7th-8th', 'education_ 9th', 'education_ Assoc-acdm', 'education_ Assoc-voc', 'education_ Bachelors', 'education_ Doctorate', 'education_ HS-grad', 'education_ Masters', 'education_ Preschool', 'education_ Prof-school', 'education_ Some-college', 'marital-status_ Divorced', 'marital-status_ Married-AF-spouse', 'marital-status_ Married-civ-spouse', 'marital-status_ Married-spouse-absent', 'marital-status_ Never-married', 'marital-status_ Separated', 'marital-status_ Widowed', 'occupation_ Adm-clerical', 'occupation_ Armed-Forces', 'occupation_ Craft-repair', 'occupation_ Exec-managerial', 'occupation_ Farming-fishing', 'occupation_ Handlers-cleaners', 'occupation_ Machine-op-inspct', 'occupation_ Other-service', 'occupation_ Priv-house-serv', 'occupation_ Prof-specialty', 'occupation_ Protective-serv', 'occupation_ Sales', 'occupation_ Tech-support', 'occupation_ Transport-moving', 'relationship_ Husband', 'relationship_ Not-in-family', 'relationship_ Other-relative', 'relationship_ Own-child', 'relationship_ Unmarried', 'relationship_ Wife', 'race_ Amer-Indian-Eskimo', 'race_ Asian-Pac-Islander', 'race_ Black', 'race_ Other', 'race_ White', 'native-country_ Cambodia', 'native-country_ Canada', 'native-country_ China', 'native-country_ Columbia', 'native-country_ Cuba', 'native-country_ Dominican-Republic', 'native-country_ Ecuador', 'native-country_ El-Salvador', 'native-country_ England', 'native-country_ France', 'native-country_ Germany', 'native-country_ Greece', 'native-country_ Guatemala', 'native-country_ Haiti', 'native-country_ Holand-Netherlands', 'native-country_ Honduras', 'native-country_ Hong', 'native-country_ Hungary', 'native-country_ India', 'native-country_ Iran', 'native-country_ Ireland', 'native-country_ Italy', 'native-country_ Jamaica', 'native-country_ Japan', 'native-country_ Laos', 'native-country_ Mexico', 'native-country_ Nicaragua', 'native-country_ Outlying-US(Guam-USVI-etc)', 'native-country_ Peru', 'native-country_ Philippines', 'native-country_ Poland', 'native-country_ Portugal', 'native-country_ Puerto-Rico', 'native-country_ Scotland', 'native-country_ South', 'native-country_ Taiwan', 'native-country_ Thailand', 'native-country_ Trinadad&Tobago', 'native-country_ United-States', 'native-country_ Vietnam', 'native-country_ Yugoslavia']\n"
     ]
    }
   ],
   "source": [
    "# print all the columns\n",
    "print(X.columns.to_list())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "age                           1256257\n",
      "fnlwgt                     6179373392\n",
      "education-num                  328237\n",
      "capital-gain                 35089324\n",
      "capital-loss                  2842700\n",
      "hours-per-week                1316684\n",
      "sex_ Male                       21790\n",
      "workclass_ Federal-gov            960\n",
      "workclass_ Local-gov             2093\n",
      "workclass_ Never-worked             7\n",
      "dtype: int64\n",
      "workclass_ Private             24532\n",
      "workclass_ Self-emp-inc         1116\n",
      "workclass_ Self-emp-not-inc     2541\n",
      "workclass_ State-gov            1298\n",
      "workclass_ Without-pay            14\n",
      "education_ 10th                  933\n",
      "education_ 11th                 1175\n",
      "education_ 12th                  433\n",
      "education_ 1st-4th               168\n",
      "education_ 5th-6th               333\n",
      "dtype: int64\n",
      "education_ 7th-8th          646\n",
      "education_ 9th              514\n",
      "education_ Assoc-acdm      1067\n",
      "education_ Assoc-voc       1382\n",
      "education_ Bachelors       5355\n",
      "education_ Doctorate        413\n",
      "education_ HS-grad        10501\n",
      "education_ Masters         1723\n",
      "education_ Preschool         51\n",
      "education_ Prof-school      576\n",
      "dtype: int64\n",
      "education_ Some-college                   7291\n",
      "marital-status_ Divorced                  4443\n",
      "marital-status_ Married-AF-spouse           23\n",
      "marital-status_ Married-civ-spouse       14976\n",
      "marital-status_ Married-spouse-absent      418\n",
      "marital-status_ Never-married            10683\n",
      "marital-status_ Separated                 1025\n",
      "marital-status_ Widowed                    993\n",
      "occupation_ Adm-clerical                  3770\n",
      "occupation_ Armed-Forces                     9\n",
      "dtype: int64\n",
      "occupation_ Craft-repair         4099\n",
      "occupation_ Exec-managerial      4066\n",
      "occupation_ Farming-fishing       994\n",
      "occupation_ Handlers-cleaners    1370\n",
      "occupation_ Machine-op-inspct    2002\n",
      "occupation_ Other-service        3295\n",
      "occupation_ Priv-house-serv       149\n",
      "occupation_ Prof-specialty       5983\n",
      "occupation_ Protective-serv       649\n",
      "occupation_ Sales                3650\n",
      "dtype: int64\n",
      "occupation_ Tech-support          928\n",
      "occupation_ Transport-moving     1597\n",
      "relationship_ Husband           13193\n",
      "relationship_ Not-in-family      8305\n",
      "relationship_ Other-relative      981\n",
      "relationship_ Own-child          5068\n",
      "relationship_ Unmarried          3446\n",
      "relationship_ Wife               1568\n",
      "race_ Amer-Indian-Eskimo          311\n",
      "race_ Asian-Pac-Islander         1039\n",
      "dtype: int64\n",
      "race_ Black                            3124\n",
      "race_ Other                             271\n",
      "race_ White                           27816\n",
      "native-country_ Cambodia                 19\n",
      "native-country_ Canada                  121\n",
      "native-country_ China                    75\n",
      "native-country_ Columbia                 59\n",
      "native-country_ Cuba                     95\n",
      "native-country_ Dominican-Republic       70\n",
      "native-country_ Ecuador                  28\n",
      "dtype: int64\n",
      "native-country_ El-Salvador           106\n",
      "native-country_ England                90\n",
      "native-country_ France                 29\n",
      "native-country_ Germany               137\n",
      "native-country_ Greece                 29\n",
      "native-country_ Guatemala              64\n",
      "native-country_ Haiti                  44\n",
      "native-country_ Holand-Netherlands      1\n",
      "native-country_ Honduras               13\n",
      "native-country_ Hong                   20\n",
      "dtype: int64\n",
      "native-country_ Hungary       13\n",
      "native-country_ India        100\n",
      "native-country_ Iran          43\n",
      "native-country_ Ireland       24\n",
      "native-country_ Italy         73\n",
      "native-country_ Jamaica       81\n",
      "native-country_ Japan         62\n",
      "native-country_ Laos          18\n",
      "native-country_ Mexico       643\n",
      "native-country_ Nicaragua     34\n",
      "dtype: int64\n",
      "native-country_ Outlying-US(Guam-USVI-etc)     14\n",
      "native-country_ Peru                           31\n",
      "native-country_ Philippines                   198\n",
      "native-country_ Poland                         60\n",
      "native-country_ Portugal                       37\n",
      "native-country_ Puerto-Rico                   114\n",
      "native-country_ Scotland                       12\n",
      "native-country_ South                          80\n",
      "native-country_ Taiwan                         51\n",
      "native-country_ Thailand                       18\n",
      "dtype: int64\n",
      "native-country_ Trinadad&Tobago       19\n",
      "native-country_ United-States      29753\n",
      "native-country_ Vietnam               67\n",
      "native-country_ Yugoslavia            16\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# count the sum of entries in all the columns\n",
    "# show the values in a loop of 10 columns\n",
    "\n",
    "for i in range(0, len(X.columns.to_list()), 10):\n",
    "    print(X.iloc[:, i:i+10].sum())\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pytorch-learn",
   "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": "3.11.3"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}