Kaggle.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubantu/anaconda3/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  return f(*args, **kwds)\n",
      "/home/ubantu/anaconda3/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  return f(*args, **kwds)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import  matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from sklearn.model_selection import train_test_split\n",
    "import seaborn as sns\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"/home/ubantu/Documents/DS/comptkaggle/compt1/train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26</td>\n",
       "      <td>19</td>\n",
       "      <td>Private</td>\n",
       "      <td>168294</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Own-child</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27</td>\n",
       "      <td>54</td>\n",
       "      <td>?</td>\n",
       "      <td>180211</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>?</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>South</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28</td>\n",
       "      <td>39</td>\n",
       "      <td>Private</td>\n",
       "      <td>367260</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Exec-managerial</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>80</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29</td>\n",
       "      <td>49</td>\n",
       "      <td>Private</td>\n",
       "      <td>193366</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Craft-repair</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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>70</td>\n",
       "      <td>19</td>\n",
       "      <td>Private</td>\n",
       "      <td>101509</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>71</td>\n",
       "      <td>31</td>\n",
       "      <td>Private</td>\n",
       "      <td>309974</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Separated</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>72</td>\n",
       "      <td>29</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>162298</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>73</td>\n",
       "      <td>23</td>\n",
       "      <td>Private</td>\n",
       "      <td>211678</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Machine-op-inspct</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>74</td>\n",
       "      <td>79</td>\n",
       "      <td>Private</td>\n",
       "      <td>124744</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Other-relative</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>75</td>\n",
       "      <td>27</td>\n",
       "      <td>Private</td>\n",
       "      <td>213921</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>76</td>\n",
       "      <td>40</td>\n",
       "      <td>Private</td>\n",
       "      <td>32214</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Adm-clerical</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>77</td>\n",
       "      <td>67</td>\n",
       "      <td>?</td>\n",
       "      <td>212759</td>\n",
       "      <td>10th</td>\n",
       "      <td>6</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>?</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>78</td>\n",
       "      <td>18</td>\n",
       "      <td>Private</td>\n",
       "      <td>309634</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>79</td>\n",
       "      <td>31</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>125927</td>\n",
       "      <td>7th-8th</td>\n",
       "      <td>4</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</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>80</td>\n",
       "      <td>18</td>\n",
       "      <td>Private</td>\n",
       "      <td>446839</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Sales</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>30</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>81</td>\n",
       "      <td>52</td>\n",
       "      <td>Private</td>\n",
       "      <td>276515</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Other-service</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>Cuba</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>82</td>\n",
       "      <td>46</td>\n",
       "      <td>Private</td>\n",
       "      <td>51618</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Wife</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>83</td>\n",
       "      <td>59</td>\n",
       "      <td>Private</td>\n",
       "      <td>159937</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>84</td>\n",
       "      <td>44</td>\n",
       "      <td>Private</td>\n",
       "      <td>343591</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>14344</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>85</td>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>346253</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>86</td>\n",
       "      <td>49</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>268234</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</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</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>87</td>\n",
       "      <td>33</td>\n",
       "      <td>Private</td>\n",
       "      <td>202051</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</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</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>88</td>\n",
       "      <td>30</td>\n",
       "      <td>Private</td>\n",
       "      <td>54334</td>\n",
       "      <td>9th</td>\n",
       "      <td>5</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Sales</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>89</td>\n",
       "      <td>43</td>\n",
       "      <td>Federal-gov</td>\n",
       "      <td>410867</td>\n",
       "      <td>Doctorate</td>\n",
       "      <td>16</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>90</td>\n",
       "      <td>57</td>\n",
       "      <td>Private</td>\n",
       "      <td>249977</td>\n",
       "      <td>Assoc-voc</td>\n",
       "      <td>11</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</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>91</td>\n",
       "      <td>37</td>\n",
       "      <td>Private</td>\n",
       "      <td>286730</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Craft-repair</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>92</td>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>212563</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>93</td>\n",
       "      <td>30</td>\n",
       "      <td>Private</td>\n",
       "      <td>117747</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>1573</td>\n",
       "      <td>35</td>\n",
       "      <td>?</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>94</td>\n",
       "      <td>34</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>226296</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</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</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>95</td>\n",
       "      <td>29</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>115585</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Never-married</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>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>96</td>\n",
       "      <td>48</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>191277</td>\n",
       "      <td>Doctorate</td>\n",
       "      <td>16</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</td>\n",
       "      <td>1902</td>\n",
       "      <td>60</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>97</td>\n",
       "      <td>37</td>\n",
       "      <td>Private</td>\n",
       "      <td>202683</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>98</td>\n",
       "      <td>48</td>\n",
       "      <td>Private</td>\n",
       "      <td>171095</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Exec-managerial</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>England</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>99</td>\n",
       "      <td>32</td>\n",
       "      <td>Federal-gov</td>\n",
       "      <td>249409</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Own-child</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>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0  Age          WorkClass  fnlwgt      Education  EducationNum  \\\n",
       "0            0   39          State-gov   77516      Bachelors            13   \n",
       "1            1   50   Self-emp-not-inc   83311      Bachelors            13   \n",
       "2            2   38            Private  215646        HS-grad             9   \n",
       "3            3   53            Private  234721           11th             7   \n",
       "4            4   28            Private  338409      Bachelors            13   \n",
       "5            5   37            Private  284582        Masters            14   \n",
       "6            6   49            Private  160187            9th             5   \n",
       "7            7   52   Self-emp-not-inc  209642        HS-grad             9   \n",
       "8            8   31            Private   45781        Masters            14   \n",
       "9            9   42            Private  159449      Bachelors            13   \n",
       "10          10   37            Private  280464   Some-college            10   \n",
       "11          11   30          State-gov  141297      Bachelors            13   \n",
       "12          12   23            Private  122272      Bachelors            13   \n",
       "13          13   32            Private  205019     Assoc-acdm            12   \n",
       "14          14   40            Private  121772      Assoc-voc            11   \n",
       "15          15   34            Private  245487        7th-8th             4   \n",
       "16          16   25   Self-emp-not-inc  176756        HS-grad             9   \n",
       "17          17   32            Private  186824        HS-grad             9   \n",
       "18          18   38            Private   28887           11th             7   \n",
       "19          19   43   Self-emp-not-inc  292175        Masters            14   \n",
       "20          20   40            Private  193524      Doctorate            16   \n",
       "21          21   54            Private  302146        HS-grad             9   \n",
       "22          22   35        Federal-gov   76845            9th             5   \n",
       "23          23   43            Private  117037           11th             7   \n",
       "24          24   59            Private  109015        HS-grad             9   \n",
       "25          25   56          Local-gov  216851      Bachelors            13   \n",
       "26          26   19            Private  168294        HS-grad             9   \n",
       "27          27   54                  ?  180211   Some-college            10   \n",
       "28          28   39            Private  367260        HS-grad             9   \n",
       "29          29   49            Private  193366        HS-grad             9   \n",
       "..         ...  ...                ...     ...            ...           ...   \n",
       "70          70   19            Private  101509   Some-college            10   \n",
       "71          71   31            Private  309974      Bachelors            13   \n",
       "72          72   29   Self-emp-not-inc  162298      Bachelors            13   \n",
       "73          73   23            Private  211678   Some-college            10   \n",
       "74          74   79            Private  124744   Some-college            10   \n",
       "75          75   27            Private  213921        HS-grad             9   \n",
       "76          76   40            Private   32214     Assoc-acdm            12   \n",
       "77          77   67                  ?  212759           10th             6   \n",
       "78          78   18            Private  309634           11th             7   \n",
       "79          79   31          Local-gov  125927        7th-8th             4   \n",
       "80          80   18            Private  446839        HS-grad             9   \n",
       "81          81   52            Private  276515      Bachelors            13   \n",
       "82          82   46            Private   51618        HS-grad             9   \n",
       "83          83   59            Private  159937        HS-grad             9   \n",
       "84          84   44            Private  343591        HS-grad             9   \n",
       "85          85   53            Private  346253        HS-grad             9   \n",
       "86          86   49          Local-gov  268234        HS-grad             9   \n",
       "87          87   33            Private  202051        Masters            14   \n",
       "88          88   30            Private   54334            9th             5   \n",
       "89          89   43        Federal-gov  410867      Doctorate            16   \n",
       "90          90   57            Private  249977      Assoc-voc            11   \n",
       "91          91   37            Private  286730   Some-college            10   \n",
       "92          92   28            Private  212563   Some-college            10   \n",
       "93          93   30            Private  117747        HS-grad             9   \n",
       "94          94   34          Local-gov  226296      Bachelors            13   \n",
       "95          95   29          Local-gov  115585   Some-college            10   \n",
       "96          96   48   Self-emp-not-inc  191277      Doctorate            16   \n",
       "97          97   37            Private  202683   Some-college            10   \n",
       "98          98   48            Private  171095     Assoc-acdm            12   \n",
       "99          99   32        Federal-gov  249409        HS-grad             9   \n",
       "\n",
       "            Marital-Status          Occupation     Relationship  \\\n",
       "0            Never-married        Adm-clerical    Not-in-family   \n",
       "1       Married-civ-spouse     Exec-managerial          Husband   \n",
       "2                 Divorced   Handlers-cleaners    Not-in-family   \n",
       "3       Married-civ-spouse   Handlers-cleaners          Husband   \n",
       "4       Married-civ-spouse      Prof-specialty             Wife   \n",
       "5       Married-civ-spouse     Exec-managerial             Wife   \n",
       "6    Married-spouse-absent       Other-service    Not-in-family   \n",
       "7       Married-civ-spouse     Exec-managerial          Husband   \n",
       "8            Never-married      Prof-specialty    Not-in-family   \n",
       "9       Married-civ-spouse     Exec-managerial          Husband   \n",
       "10      Married-civ-spouse     Exec-managerial          Husband   \n",
       "11      Married-civ-spouse      Prof-specialty          Husband   \n",
       "12           Never-married        Adm-clerical        Own-child   \n",
       "13           Never-married               Sales    Not-in-family   \n",
       "14      Married-civ-spouse        Craft-repair          Husband   \n",
       "15      Married-civ-spouse    Transport-moving          Husband   \n",
       "16           Never-married     Farming-fishing        Own-child   \n",
       "17           Never-married   Machine-op-inspct        Unmarried   \n",
       "18      Married-civ-spouse               Sales          Husband   \n",
       "19                Divorced     Exec-managerial        Unmarried   \n",
       "20      Married-civ-spouse      Prof-specialty          Husband   \n",
       "21               Separated       Other-service        Unmarried   \n",
       "22      Married-civ-spouse     Farming-fishing          Husband   \n",
       "23      Married-civ-spouse    Transport-moving          Husband   \n",
       "24                Divorced        Tech-support        Unmarried   \n",
       "25      Married-civ-spouse        Tech-support          Husband   \n",
       "26           Never-married        Craft-repair        Own-child   \n",
       "27      Married-civ-spouse                   ?          Husband   \n",
       "28                Divorced     Exec-managerial    Not-in-family   \n",
       "29      Married-civ-spouse        Craft-repair          Husband   \n",
       "..                     ...                 ...              ...   \n",
       "70           Never-married      Prof-specialty        Own-child   \n",
       "71               Separated               Sales        Own-child   \n",
       "72      Married-civ-spouse               Sales          Husband   \n",
       "73           Never-married   Machine-op-inspct    Not-in-family   \n",
       "74      Married-civ-spouse      Prof-specialty   Other-relative   \n",
       "75           Never-married       Other-service        Own-child   \n",
       "76      Married-civ-spouse        Adm-clerical          Husband   \n",
       "77      Married-civ-spouse                   ?          Husband   \n",
       "78           Never-married       Other-service        Own-child   \n",
       "79      Married-civ-spouse     Farming-fishing          Husband   \n",
       "80           Never-married               Sales    Not-in-family   \n",
       "81      Married-civ-spouse       Other-service          Husband   \n",
       "82      Married-civ-spouse       Other-service             Wife   \n",
       "83      Married-civ-spouse               Sales          Husband   \n",
       "84                Divorced        Craft-repair    Not-in-family   \n",
       "85                Divorced               Sales        Own-child   \n",
       "86      Married-civ-spouse     Protective-serv          Husband   \n",
       "87      Married-civ-spouse      Prof-specialty          Husband   \n",
       "88           Never-married               Sales    Not-in-family   \n",
       "89           Never-married      Prof-specialty    Not-in-family   \n",
       "90      Married-civ-spouse      Prof-specialty          Husband   \n",
       "91                Divorced        Craft-repair        Unmarried   \n",
       "92                Divorced   Machine-op-inspct        Unmarried   \n",
       "93      Married-civ-spouse               Sales             Wife   \n",
       "94      Married-civ-spouse     Protective-serv          Husband   \n",
       "95           Never-married   Handlers-cleaners    Not-in-family   \n",
       "96      Married-civ-spouse      Prof-specialty          Husband   \n",
       "97      Married-civ-spouse               Sales          Husband   \n",
       "98                Divorced     Exec-managerial        Unmarried   \n",
       "99           Never-married       Other-service        Own-child   \n",
       "\n",
       "                   Race      Sex  CapitalGain  CapitalLoss  HoursPerWeek  \\\n",
       "0                 White     Male         2174            0            40   \n",
       "1                 White     Male            0            0            13   \n",
       "2                 White     Male            0            0            40   \n",
       "3                 Black     Male            0            0            40   \n",
       "4                 Black   Female            0            0            40   \n",
       "5                 White   Female            0            0            40   \n",
       "6                 Black   Female            0            0            16   \n",
       "7                 White     Male            0            0            45   \n",
       "8                 White   Female        14084            0            50   \n",
       "9                 White     Male         5178            0            40   \n",
       "10                Black     Male            0            0            80   \n",
       "11   Asian-Pac-Islander     Male            0            0            40   \n",
       "12                White   Female            0            0            30   \n",
       "13                Black     Male            0            0            50   \n",
       "14   Asian-Pac-Islander     Male            0            0            40   \n",
       "15   Amer-Indian-Eskimo     Male            0            0            45   \n",
       "16                White     Male            0            0            35   \n",
       "17                White     Male            0            0            40   \n",
       "18                White     Male            0            0            50   \n",
       "19                White   Female            0            0            45   \n",
       "20                White     Male            0            0            60   \n",
       "21                Black   Female            0            0            20   \n",
       "22                Black     Male            0            0            40   \n",
       "23                White     Male            0         2042            40   \n",
       "24                White   Female            0            0            40   \n",
       "25                White     Male            0            0            40   \n",
       "26                White     Male            0            0            40   \n",
       "27   Asian-Pac-Islander     Male            0            0            60   \n",
       "28                White     Male            0            0            80   \n",
       "29                White     Male            0            0            40   \n",
       "..                  ...      ...          ...          ...           ...   \n",
       "70                White     Male            0            0            32   \n",
       "71                Black   Female            0            0            40   \n",
       "72                White     Male            0            0            70   \n",
       "73                White     Male            0            0            40   \n",
       "74                White     Male            0            0            20   \n",
       "75                White     Male            0            0            40   \n",
       "76                White     Male            0            0            40   \n",
       "77                White     Male            0            0             2   \n",
       "78                White   Female            0            0            22   \n",
       "79                White     Male            0            0            40   \n",
       "80                White     Male            0            0            30   \n",
       "81                White     Male            0            0            40   \n",
       "82                White   Female            0            0            40   \n",
       "83                White     Male            0            0            48   \n",
       "84                White   Female        14344            0            40   \n",
       "85                White   Female            0            0            35   \n",
       "86                White     Male            0            0            40   \n",
       "87                White     Male            0            0            50   \n",
       "88                White     Male            0            0            40   \n",
       "89                White   Female            0            0            50   \n",
       "90                White     Male            0            0            40   \n",
       "91                White   Female            0            0            40   \n",
       "92                Black   Female            0            0            25   \n",
       "93   Asian-Pac-Islander   Female            0         1573            35   \n",
       "94                White     Male            0            0            40   \n",
       "95                White     Male            0            0            50   \n",
       "96                White     Male            0         1902            60   \n",
       "97                White     Male            0            0            48   \n",
       "98                White   Female            0            0            40   \n",
       "99                Black     Male            0            0            40   \n",
       "\n",
       "     NativeCountry  Target  \n",
       "0    United-States       0  \n",
       "1    United-States       0  \n",
       "2    United-States       0  \n",
       "3    United-States       0  \n",
       "4             Cuba       0  \n",
       "5    United-States       0  \n",
       "6          Jamaica       0  \n",
       "7    United-States       1  \n",
       "8    United-States       1  \n",
       "9    United-States       1  \n",
       "10   United-States       1  \n",
       "11           India       1  \n",
       "12   United-States       0  \n",
       "13   United-States       0  \n",
       "14               ?       1  \n",
       "15          Mexico       0  \n",
       "16   United-States       0  \n",
       "17   United-States       0  \n",
       "18   United-States       0  \n",
       "19   United-States       1  \n",
       "20   United-States       1  \n",
       "21   United-States       0  \n",
       "22   United-States       0  \n",
       "23   United-States       0  \n",
       "24   United-States       0  \n",
       "25   United-States       1  \n",
       "26   United-States       0  \n",
       "27           South       1  \n",
       "28   United-States       0  \n",
       "29   United-States       0  \n",
       "..             ...     ...  \n",
       "70   United-States       0  \n",
       "71   United-States       0  \n",
       "72   United-States       1  \n",
       "73   United-States       0  \n",
       "74   United-States       0  \n",
       "75          Mexico       0  \n",
       "76   United-States       0  \n",
       "77   United-States       0  \n",
       "78   United-States       0  \n",
       "79   United-States       0  \n",
       "80   United-States       0  \n",
       "81            Cuba       0  \n",
       "82   United-States       0  \n",
       "83   United-States       0  \n",
       "84   United-States       1  \n",
       "85   United-States       0  \n",
       "86   United-States       1  \n",
       "87   United-States       0  \n",
       "88   United-States       0  \n",
       "89   United-States       1  \n",
       "90   United-States       0  \n",
       "91   United-States       0  \n",
       "92   United-States       0  \n",
       "93               ?       0  \n",
       "94   United-States       1  \n",
       "95   United-States       0  \n",
       "96   United-States       1  \n",
       "97   United-States       1  \n",
       "98         England       0  \n",
       "99   United-States       0  \n",
       "\n",
       "[100 rows x 16 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0         int64\n",
       "Age                int64\n",
       "WorkClass         object\n",
       "fnlwgt             int64\n",
       "Education         object\n",
       "EducationNum       int64\n",
       "Marital-Status    object\n",
       "Occupation        object\n",
       "Relationship      object\n",
       "Race              object\n",
       "Sex               object\n",
       "CapitalGain        int64\n",
       "CapitalLoss        int64\n",
       "HoursPerWeek       int64\n",
       "NativeCountry     object\n",
       "Target             int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 32561 entries, 0 to 32560\n",
      "Data columns (total 16 columns):\n",
      "Unnamed: 0        32561 non-null int64\n",
      "Age               32561 non-null int64\n",
      "WorkClass         32561 non-null object\n",
      "fnlwgt            32561 non-null int64\n",
      "Education         32561 non-null object\n",
      "EducationNum      32561 non-null int64\n",
      "Marital-Status    32561 non-null object\n",
      "Occupation        32561 non-null object\n",
      "Relationship      32561 non-null object\n",
      "Race              32561 non-null object\n",
      "Sex               32561 non-null object\n",
      "CapitalGain       32561 non-null int64\n",
      "CapitalLoss       32561 non-null int64\n",
      "HoursPerWeek      32561 non-null int64\n",
      "NativeCountry     32561 non-null object\n",
      "Target            32561 non-null int64\n",
      "dtypes: int64(8), object(8)\n",
      "memory usage: 4.0+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Unnamed: 0', 'Age', 'WorkClass', 'fnlwgt', 'Education', 'EducationNum',\n",
      "       'Marital-Status', 'Occupation', 'Relationship', 'Race', 'Sex',\n",
      "       'CapitalGain', 'CapitalLoss', 'HoursPerWeek', 'NativeCountry',\n",
      "       'Target'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "col=data.columns\n",
    "#data.head()\n",
    "print(col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3f0356fdd8>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "\n",
    "sns.heatmap(data.corr(),annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "model1 = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "le = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Marital-Status']=le.fit_transform(data['Marital-Status'])\n",
    "data['WorkClass']=le.fit_transform(data['WorkClass'])\n",
    "data['Relationship']=le.fit_transform(data['Relationship'])\n",
    "data['Race']=le.fit_transform(data['Race'])\n",
    "data['Sex']=le.fit_transform(data['Sex'])\n",
    "data['NativeCountry']=le.fit_transform(data['NativeCountry'])\n",
    "data['Occupation']=le.fit_transform(data['Occupation'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        1\n",
       "1        0\n",
       "2        1\n",
       "3        0\n",
       "4        5\n",
       "5        5\n",
       "6        1\n",
       "7        0\n",
       "8        1\n",
       "9        0\n",
       "10       0\n",
       "11       0\n",
       "12       3\n",
       "13       1\n",
       "14       0\n",
       "15       0\n",
       "16       3\n",
       "17       4\n",
       "18       0\n",
       "19       4\n",
       "20       0\n",
       "21       4\n",
       "22       0\n",
       "23       0\n",
       "24       4\n",
       "25       0\n",
       "26       3\n",
       "27       0\n",
       "28       1\n",
       "29       0\n",
       "        ..\n",
       "32531    1\n",
       "32532    0\n",
       "32533    0\n",
       "32534    4\n",
       "32535    3\n",
       "32536    1\n",
       "32537    1\n",
       "32538    4\n",
       "32539    0\n",
       "32540    3\n",
       "32541    1\n",
       "32542    0\n",
       "32543    4\n",
       "32544    1\n",
       "32545    5\n",
       "32546    1\n",
       "32547    0\n",
       "32548    1\n",
       "32549    2\n",
       "32550    0\n",
       "32551    0\n",
       "32552    0\n",
       "32553    1\n",
       "32554    0\n",
       "32555    1\n",
       "32556    5\n",
       "32557    0\n",
       "32558    4\n",
       "32559    3\n",
       "32560    5\n",
       "Name: Relationship, Length: 32561, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.pop(\"Education\")\n",
    "data.pop(\"Unnamed: 0\")\n",
    "data.pop(\"fnlwgt\")\n",
    "data.pop(\"Relationship\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sns.pairplot(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3ef3d4dcc0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,20))\n",
    "sns.heatmap(data.corr(),annot=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>WorkClass</th>\n",
       "      <th>EducationNum</th>\n",
       "      <th>Marital-Status</th>\n",
       "      <th>Occupation</th>\n",
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       "      <th>CapitalGain</th>\n",
       "      <th>CapitalLoss</th>\n",
       "      <th>HoursPerWeek</th>\n",
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       "      <th>Target</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>5</th>\n",
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       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
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       "      <td>0</td>\n",
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       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
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       "      <th>6</th>\n",
       "      <td>49</td>\n",
       "      <td>4</td>\n",
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       "      <th>7</th>\n",
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       "      <td>14084</td>\n",
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       "      <td>39</td>\n",
       "      <td>1</td>\n",
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       "      <th>9</th>\n",
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       "      <td>39</td>\n",
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       "      <th>10</th>\n",
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       "      <td>10</td>\n",
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       "      <td>0</td>\n",
       "      <td>40</td>\n",
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       "      <td>1</td>\n",
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       "      <th>12</th>\n",
       "      <td>23</td>\n",
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       "      <td>4</td>\n",
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       "      <th>13</th>\n",
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       "      <td>4</td>\n",
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       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>34</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <th>16</th>\n",
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       "      <td>6</td>\n",
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       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>32</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>38</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>43</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>54</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>43</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2042</td>\n",
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       "      <td>39</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>59</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>56</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>39</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>80</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>49</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>23</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>52</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>44</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>45</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1408</td>\n",
       "      <td>40</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>22</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>48</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>33</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>21</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>48</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>53</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>24</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>49</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>53</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>44</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>41</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>29</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age  WorkClass  EducationNum  Marital-Status  Occupation  Race  Sex  \\\n",
       "0    39          7            13               4           1     4    1   \n",
       "1    50          6            13               2           4     4    1   \n",
       "2    38          4             9               0           6     4    1   \n",
       "3    53          4             7               2           6     2    1   \n",
       "4    28          4            13               2          10     2    0   \n",
       "5    37          4            14               2           4     4    0   \n",
       "6    49          4             5               3           8     2    0   \n",
       "7    52          6             9               2           4     4    1   \n",
       "8    31          4            14               4          10     4    0   \n",
       "9    42          4            13               2           4     4    1   \n",
       "10   37          4            10               2           4     2    1   \n",
       "11   30          7            13               2          10     1    1   \n",
       "12   23          4            13               4           1     4    0   \n",
       "13   32          4            12               4          12     2    1   \n",
       "14   40          4            11               2           3     1    1   \n",
       "15   34          4             4               2          14     0    1   \n",
       "16   25          6             9               4           5     4    1   \n",
       "17   32          4             9               4           7     4    1   \n",
       "18   38          4             7               2          12     4    1   \n",
       "19   43          6            14               0           4     4    0   \n",
       "20   40          4            16               2          10     4    1   \n",
       "21   54          4             9               5           8     2    0   \n",
       "22   35          1             5               2           5     2    1   \n",
       "23   43          4             7               2          14     4    1   \n",
       "24   59          4             9               0          13     4    0   \n",
       "25   56          2            13               2          13     4    1   \n",
       "26   19          4             9               4           3     4    1   \n",
       "27   54          0            10               2           0     1    1   \n",
       "28   39          4             9               0           4     4    1   \n",
       "29   49          4             9               2           3     4    1   \n",
       "30   23          2            12               4          11     4    1   \n",
       "31   20          4            10               4          12     2    1   \n",
       "32   45          4            13               0           4     4    1   \n",
       "33   30          1            10               2           1     4    1   \n",
       "34   22          7            10               2           8     2    1   \n",
       "35   48          4             7               4           7     4    1   \n",
       "36   21          4            10               4           7     4    1   \n",
       "37   19          4             9               1           1     4    0   \n",
       "38   31          4            10               2          12     4    1   \n",
       "39   48          6            12               2          10     4    1   \n",
       "40   31          4             5               2           7     4    1   \n",
       "41   53          6            13               2          10     4    1   \n",
       "42   24          4            13               2          13     4    1   \n",
       "43   49          4             9               5           1     4    0   \n",
       "44   25          4             9               4           6     4    1   \n",
       "45   57          1            13               2          10     2    1   \n",
       "46   53          4             9               2           7     4    1   \n",
       "47   44          4            14               0           4     4    0   \n",
       "48   41          7            11               2           3     4    1   \n",
       "49   29          4            11               4          10     4    1   \n",
       "\n",
       "    CapitalGain  CapitalLoss  HoursPerWeek  NativeCountry  Target  \n",
       "0          2174            0            40             39       0  \n",
       "1             0            0            13             39       0  \n",
       "2             0            0            40             39       0  \n",
       "3             0            0            40             39       0  \n",
       "4             0            0            40              5       0  \n",
       "5             0            0            40             39       0  \n",
       "6             0            0            16             23       0  \n",
       "7             0            0            45             39       1  \n",
       "8         14084            0            50             39       1  \n",
       "9          5178            0            40             39       1  \n",
       "10            0            0            80             39       1  \n",
       "11            0            0            40             19       1  \n",
       "12            0            0            30             39       0  \n",
       "13            0            0            50             39       0  \n",
       "14            0            0            40              0       1  \n",
       "15            0            0            45             26       0  \n",
       "16            0            0            35             39       0  \n",
       "17            0            0            40             39       0  \n",
       "18            0            0            50             39       0  \n",
       "19            0            0            45             39       1  \n",
       "20            0            0            60             39       1  \n",
       "21            0            0            20             39       0  \n",
       "22            0            0            40             39       0  \n",
       "23            0         2042            40             39       0  \n",
       "24            0            0            40             39       0  \n",
       "25            0            0            40             39       1  \n",
       "26            0            0            40             39       0  \n",
       "27            0            0            60             35       1  \n",
       "28            0            0            80             39       0  \n",
       "29            0            0            40             39       0  \n",
       "30            0            0            52             39       0  \n",
       "31            0            0            44             39       0  \n",
       "32            0         1408            40             39       0  \n",
       "33            0            0            40             39       0  \n",
       "34            0            0            15             39       0  \n",
       "35            0            0            40             33       0  \n",
       "36            0            0            40             39       0  \n",
       "37            0            0            25             39       0  \n",
       "38            0            0            38              0       1  \n",
       "39            0            0            40             39       0  \n",
       "40            0            0            43             39       0  \n",
       "41            0            0            40             39       0  \n",
       "42            0            0            50             39       0  \n",
       "43            0            0            40             39       0  \n",
       "44            0            0            35             39       0  \n",
       "45            0            0            40             39       1  \n",
       "46            0            0            38             39       0  \n",
       "47            0            0            40             39       0  \n",
       "48            0            0            40             39       0  \n",
       "49            0            0            43             39       0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Age</th>\n",
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      ],
      "text/plain": [
       "   Age  WorkClass  EducationNum  Marital-Status  Occupation  Race  Sex  \\\n",
       "0   39          7            13               4           1     4    1   \n",
       "1   50          6            13               2           4     4    1   \n",
       "2   38          4             9               0           6     4    1   \n",
       "3   53          4             7               2           6     2    1   \n",
       "4   28          4            13               2          10     2    0   \n",
       "\n",
       "   CapitalGain  CapitalLoss  HoursPerWeek  NativeCountry  Target  \n",
       "0         2174            0            40             39       0  \n",
       "1            0            0            13             39       0  \n",
       "2            0            0            40             39       0  \n",
       "3            0            0            40             39       0  \n",
       "4            0            0            40              5       0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['NativeCountry']=data['NativeCountry'].replace(0,np.nan)\n",
    "data['WorkClass']=data['WorkClass'].replace(0,np.nan)\n",
    "data['Occupation']=data['Occupation'].replace(0,np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>28</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>37</td>\n",
       "      <td>4.0</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>49</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>52</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>31</td>\n",
       "      <td>4.0</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>14084</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>42</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>5178</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>37</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>80</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>30</td>\n",
       "      <td>7.0</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>23</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>32</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>40</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>34</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>25</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>32</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>38</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>43</td>\n",
       "      <td>6.0</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>40</td>\n",
       "      <td>4.0</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>54</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>35</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>43</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2042</td>\n",
       "      <td>40</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>59</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>56</td>\n",
       "      <td>2.0</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>13.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>19</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>54</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>39</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>80</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>49</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>23</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>52</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>20</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>44</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>45</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1408</td>\n",
       "      <td>40</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>30</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>22</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>48</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>21</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>19</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>31</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>48</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>31</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>53</td>\n",
       "      <td>6.0</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>24</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>13.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>49</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>25</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>57</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>53</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>44</td>\n",
       "      <td>4.0</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>41</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>29</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age  WorkClass  EducationNum  Marital-Status  Occupation  Race  Sex  \\\n",
       "0    39        7.0            13               4         1.0     4    1   \n",
       "1    50        6.0            13               2         4.0     4    1   \n",
       "2    38        4.0             9               0         6.0     4    1   \n",
       "3    53        4.0             7               2         6.0     2    1   \n",
       "4    28        4.0            13               2        10.0     2    0   \n",
       "5    37        4.0            14               2         4.0     4    0   \n",
       "6    49        4.0             5               3         8.0     2    0   \n",
       "7    52        6.0             9               2         4.0     4    1   \n",
       "8    31        4.0            14               4        10.0     4    0   \n",
       "9    42        4.0            13               2         4.0     4    1   \n",
       "10   37        4.0            10               2         4.0     2    1   \n",
       "11   30        7.0            13               2        10.0     1    1   \n",
       "12   23        4.0            13               4         1.0     4    0   \n",
       "13   32        4.0            12               4        12.0     2    1   \n",
       "14   40        4.0            11               2         3.0     1    1   \n",
       "15   34        4.0             4               2        14.0     0    1   \n",
       "16   25        6.0             9               4         5.0     4    1   \n",
       "17   32        4.0             9               4         7.0     4    1   \n",
       "18   38        4.0             7               2        12.0     4    1   \n",
       "19   43        6.0            14               0         4.0     4    0   \n",
       "20   40        4.0            16               2        10.0     4    1   \n",
       "21   54        4.0             9               5         8.0     2    0   \n",
       "22   35        1.0             5               2         5.0     2    1   \n",
       "23   43        4.0             7               2        14.0     4    1   \n",
       "24   59        4.0             9               0        13.0     4    0   \n",
       "25   56        2.0            13               2        13.0     4    1   \n",
       "26   19        4.0             9               4         3.0     4    1   \n",
       "27   54        NaN            10               2         NaN     1    1   \n",
       "28   39        4.0             9               0         4.0     4    1   \n",
       "29   49        4.0             9               2         3.0     4    1   \n",
       "30   23        2.0            12               4        11.0     4    1   \n",
       "31   20        4.0            10               4        12.0     2    1   \n",
       "32   45        4.0            13               0         4.0     4    1   \n",
       "33   30        1.0            10               2         1.0     4    1   \n",
       "34   22        7.0            10               2         8.0     2    1   \n",
       "35   48        4.0             7               4         7.0     4    1   \n",
       "36   21        4.0            10               4         7.0     4    1   \n",
       "37   19        4.0             9               1         1.0     4    0   \n",
       "38   31        4.0            10               2        12.0     4    1   \n",
       "39   48        6.0            12               2        10.0     4    1   \n",
       "40   31        4.0             5               2         7.0     4    1   \n",
       "41   53        6.0            13               2        10.0     4    1   \n",
       "42   24        4.0            13               2        13.0     4    1   \n",
       "43   49        4.0             9               5         1.0     4    0   \n",
       "44   25        4.0             9               4         6.0     4    1   \n",
       "45   57        1.0            13               2        10.0     2    1   \n",
       "46   53        4.0             9               2         7.0     4    1   \n",
       "47   44        4.0            14               0         4.0     4    0   \n",
       "48   41        7.0            11               2         3.0     4    1   \n",
       "49   29        4.0            11               4        10.0     4    1   \n",
       "\n",
       "    CapitalGain  CapitalLoss  HoursPerWeek  NativeCountry  Target  \n",
       "0          2174            0            40           39.0       0  \n",
       "1             0            0            13           39.0       0  \n",
       "2             0            0            40           39.0       0  \n",
       "3             0            0            40           39.0       0  \n",
       "4             0            0            40            5.0       0  \n",
       "5             0            0            40           39.0       0  \n",
       "6             0            0            16           23.0       0  \n",
       "7             0            0            45           39.0       1  \n",
       "8         14084            0            50           39.0       1  \n",
       "9          5178            0            40           39.0       1  \n",
       "10            0            0            80           39.0       1  \n",
       "11            0            0            40           19.0       1  \n",
       "12            0            0            30           39.0       0  \n",
       "13            0            0            50           39.0       0  \n",
       "14            0            0            40            NaN       1  \n",
       "15            0            0            45           26.0       0  \n",
       "16            0            0            35           39.0       0  \n",
       "17            0            0            40           39.0       0  \n",
       "18            0            0            50           39.0       0  \n",
       "19            0            0            45           39.0       1  \n",
       "20            0            0            60           39.0       1  \n",
       "21            0            0            20           39.0       0  \n",
       "22            0            0            40           39.0       0  \n",
       "23            0         2042            40           39.0       0  \n",
       "24            0            0            40           39.0       0  \n",
       "25            0            0            40           39.0       1  \n",
       "26            0            0            40           39.0       0  \n",
       "27            0            0            60           35.0       1  \n",
       "28            0            0            80           39.0       0  \n",
       "29            0            0            40           39.0       0  \n",
       "30            0            0            52           39.0       0  \n",
       "31            0            0            44           39.0       0  \n",
       "32            0         1408            40           39.0       0  \n",
       "33            0            0            40           39.0       0  \n",
       "34            0            0            15           39.0       0  \n",
       "35            0            0            40           33.0       0  \n",
       "36            0            0            40           39.0       0  \n",
       "37            0            0            25           39.0       0  \n",
       "38            0            0            38            NaN       1  \n",
       "39            0            0            40           39.0       0  \n",
       "40            0            0            43           39.0       0  \n",
       "41            0            0            40           39.0       0  \n",
       "42            0            0            50           39.0       0  \n",
       "43            0            0            40           39.0       0  \n",
       "44            0            0            35           39.0       0  \n",
       "45            0            0            40           39.0       1  \n",
       "46            0            0            38           39.0       0  \n",
       "47            0            0            40           39.0       0  \n",
       "48            0            0            40           39.0       0  \n",
       "49            0            0            43           39.0       0  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=data.fillna(data.mean())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Age  WorkClass  EducationNum  Marital-Status  Occupation  Race  Sex  \\\n",
      "0   36        4.0             9               2        10.0     4    1   \n",
      "\n",
      "   CapitalGain  CapitalLoss  HoursPerWeek  NativeCountry  Target  \n",
      "0            0            0            40           39.0       0  \n"
     ]
    }
   ],
   "source": [
    "data.head(50)\n",
    "print(data.mode())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=data.drop(labels='Target',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=data['Target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn = KNeighborsClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict=knn.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "logmodel = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubantu/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
       "          n_jobs=None, penalty='l2', random_state=None, solver='warn',\n",
       "          tol=0.0001, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logmodel.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = logmodel.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>WorkClass</th>\n",
       "      <th>EducationNum</th>\n",
       "      <th>Marital-Status</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Race</th>\n",
       "      <th>Sex</th>\n",
       "      <th>CapitalGain</th>\n",
       "      <th>CapitalLoss</th>\n",
       "      <th>HoursPerWeek</th>\n",
       "      <th>NativeCountry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27585</th>\n",
       "      <td>51</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18010</th>\n",
       "      <td>46</td>\n",
       "      <td>4.100081</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>6.967088</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22472</th>\n",
       "      <td>25</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>55</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4585</th>\n",
       "      <td>27</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11191</th>\n",
       "      <td>47</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>7688</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Age  WorkClass  EducationNum  Marital-Status  Occupation  Race  Sex  \\\n",
       "27585   51   4.000000            14               2    4.000000     4    1   \n",
       "18010   46   4.100081             9               3    6.967088     1    1   \n",
       "22472   25   4.000000            13               4   10.000000     2    0   \n",
       "4585    27   4.000000            12               4    8.000000     4    1   \n",
       "11191   47   2.000000            14               2   10.000000     4    1   \n",
       "\n",
       "       CapitalGain  CapitalLoss  HoursPerWeek  NativeCountry  \n",
       "27585            0            0            50           39.0  \n",
       "18010            0            0            40           30.0  \n",
       "22472            0            0            55           39.0  \n",
       "4585             0            0            40           39.0  \n",
       "11191         7688            0            45           39.0  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score,roc_auc_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7702458875204549"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_test,predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8411501954215522"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test,predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = pd.read_csv(\"/home/ubantu/Documents/DS/comptkaggle/compt1/testing.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "model1 = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3ef3b4b6a0>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "le = LabelEncoder()\n",
    "\n",
    "\n",
    "test['Marital-Status']=le.fit_transform(test['Marital-Status'])\n",
    "test['WorkClass']=le.fit_transform(test['WorkClass'])\n",
    "test['Relationship']=le.fit_transform(test['Relationship'])\n",
    "test['Race']=le.fit_transform(test['Race'])\n",
    "test['Sex']=le.fit_transform(test['Sex'])\n",
    "test['NativeCountry']=le.fit_transform(test['NativeCountry'])\n",
    "test['Occupation']=le.fit_transform(test['Occupation'])\n",
    "\n",
    "\n",
    "test.pop(\"Education\")\n",
    "test.pop(\"Relationship\")\n",
    "test.pop(\"Unnamed: 0\")\n",
    "test.pop(\"fnlwgt\")\n",
    "\n",
    "plt.figure(figsize=(20,20))\n",
    "sns.heatmap(test.corr(),annot=True)\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "test['NativeCountry']=test['NativeCountry'].replace(0,np.nan)\n",
    "test['WorkClass']=test['WorkClass'].replace(0,np.nan)\n",
    "test['Occupation']=test['Occupation'].replace(0,np.nan)\n",
    "\n",
    "\n",
    "test=test.fillna(test.mean())\n",
    "\n",
    "#knn.fit(x_train,y_train)\n",
    "predict=knn.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#roc_auc_score(y_test,predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#accuracy_score(y_test,predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub = pd.read_csv(\"/home/ubantu/Documents/DS/comptkaggle/compt1/submission.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16281,)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub['Target']=predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub.to_csv('abc.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}