Adult Dataset UCI.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "\n",
    "import pandas as pd\n",
    "import seaborn as sb\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('Downloads/adult.data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>39</th>\n",
       "      <th>State-gov</th>\n",
       "      <th>77516</th>\n",
       "      <th>Bachelors</th>\n",
       "      <th>13</th>\n",
       "      <th>Never-married</th>\n",
       "      <th>Adm-clerical</th>\n",
       "      <th>Not-in-family</th>\n",
       "      <th>White</th>\n",
       "      <th>Male</th>\n",
       "      <th>2174</th>\n",
       "      <th>0</th>\n",
       "      <th>40</th>\n",
       "      <th>United-States</th>\n",
       "      <th>&lt;=50K</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>37</td>\n",
       "      <td>Private</td>\n",
       "      <td>284582</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   39          State-gov   77516   Bachelors   13        Never-married  \\\n",
       "0  50   Self-emp-not-inc   83311   Bachelors   13   Married-civ-spouse   \n",
       "1  38            Private  215646     HS-grad    9             Divorced   \n",
       "2  53            Private  234721        11th    7   Married-civ-spouse   \n",
       "3  28            Private  338409   Bachelors   13   Married-civ-spouse   \n",
       "4  37            Private  284582     Masters   14   Married-civ-spouse   \n",
       "\n",
       "         Adm-clerical   Not-in-family   White     Male   2174   0   40  \\\n",
       "0     Exec-managerial         Husband   White     Male      0   0   13   \n",
       "1   Handlers-cleaners   Not-in-family   White     Male      0   0   40   \n",
       "2   Handlers-cleaners         Husband   Black     Male      0   0   40   \n",
       "3      Prof-specialty            Wife   Black   Female      0   0   40   \n",
       "4     Exec-managerial            Wife   White   Female      0   0   40   \n",
       "\n",
       "    United-States   <=50K  \n",
       "0   United-States   <=50K  \n",
       "1   United-States   <=50K  \n",
       "2   United-States   <=50K  \n",
       "3            Cuba   <=50K  \n",
       "4   United-States   <=50K  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>39</th>\n",
       "      <th>State-gov</th>\n",
       "      <th>77516</th>\n",
       "      <th>Bachelors</th>\n",
       "      <th>13</th>\n",
       "      <th>Never-married</th>\n",
       "      <th>Adm-clerical</th>\n",
       "      <th>Not-in-family</th>\n",
       "      <th>White</th>\n",
       "      <th>Male</th>\n",
       "      <th>2174</th>\n",
       "      <th>0</th>\n",
       "      <th>40</th>\n",
       "      <th>United-States</th>\n",
       "      <th>&lt;=50K</th>\n",
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       "      <th>32555</th>\n",
       "      <td>27</td>\n",
       "      <td>Private</td>\n",
       "      <td>257302</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Tech-support</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32556</th>\n",
       "      <td>40</td>\n",
       "      <td>Private</td>\n",
       "      <td>154374</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32557</th>\n",
       "      <td>58</td>\n",
       "      <td>Private</td>\n",
       "      <td>151910</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32558</th>\n",
       "      <td>22</td>\n",
       "      <td>Private</td>\n",
       "      <td>201490</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32559</th>\n",
       "      <td>52</td>\n",
       "      <td>Self-emp-inc</td>\n",
       "      <td>287927</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>15024</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       39      State-gov   77516    Bachelors   13        Never-married  \\\n",
       "32555  27        Private  257302   Assoc-acdm   12   Married-civ-spouse   \n",
       "32556  40        Private  154374      HS-grad    9   Married-civ-spouse   \n",
       "32557  58        Private  151910      HS-grad    9              Widowed   \n",
       "32558  22        Private  201490      HS-grad    9        Never-married   \n",
       "32559  52   Self-emp-inc  287927      HS-grad    9   Married-civ-spouse   \n",
       "\n",
       "             Adm-clerical  Not-in-family   White     Male   2174   0   40  \\\n",
       "32555        Tech-support           Wife   White   Female      0   0   38   \n",
       "32556   Machine-op-inspct        Husband   White     Male      0   0   40   \n",
       "32557        Adm-clerical      Unmarried   White   Female      0   0   40   \n",
       "32558        Adm-clerical      Own-child   White     Male      0   0   20   \n",
       "32559     Exec-managerial           Wife   White   Female  15024   0   40   \n",
       "\n",
       "        United-States   <=50K  \n",
       "32555   United-States   <=50K  \n",
       "32556   United-States    >50K  \n",
       "32557   United-States   <=50K  \n",
       "32558   United-States   <=50K  \n",
       "32559   United-States    >50K  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>39</th>\n",
       "      <th>77516</th>\n",
       "      <th>13</th>\n",
       "      <th>2174</th>\n",
       "      <th>0</th>\n",
       "      <th>40</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>32560.000000</td>\n",
       "      <td>3.256000e+04</td>\n",
       "      <td>32560.000000</td>\n",
       "      <td>32560.000000</td>\n",
       "      <td>32560.000000</td>\n",
       "      <td>32560.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>38.581634</td>\n",
       "      <td>1.897818e+05</td>\n",
       "      <td>10.080590</td>\n",
       "      <td>1077.615172</td>\n",
       "      <td>87.306511</td>\n",
       "      <td>40.437469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13.640642</td>\n",
       "      <td>1.055498e+05</td>\n",
       "      <td>2.572709</td>\n",
       "      <td>7385.402999</td>\n",
       "      <td>402.966116</td>\n",
       "      <td>12.347618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>1.228500e+04</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>1.178315e+05</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>37.000000</td>\n",
       "      <td>1.783630e+05</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>48.000000</td>\n",
       "      <td>2.370545e+05</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>45.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>90.000000</td>\n",
       "      <td>1.484705e+06</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>99999.000000</td>\n",
       "      <td>4356.000000</td>\n",
       "      <td>99.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 39         77516            13          2174             0  \\\n",
       "count  32560.000000  3.256000e+04  32560.000000  32560.000000  32560.000000   \n",
       "mean      38.581634  1.897818e+05     10.080590   1077.615172     87.306511   \n",
       "std       13.640642  1.055498e+05      2.572709   7385.402999    402.966116   \n",
       "min       17.000000  1.228500e+04      1.000000      0.000000      0.000000   \n",
       "25%       28.000000  1.178315e+05      9.000000      0.000000      0.000000   \n",
       "50%       37.000000  1.783630e+05     10.000000      0.000000      0.000000   \n",
       "75%       48.000000  2.370545e+05     12.000000      0.000000      0.000000   \n",
       "max       90.000000  1.484705e+06     16.000000  99999.000000   4356.000000   \n",
       "\n",
       "                 40  \n",
       "count  32560.000000  \n",
       "mean      40.437469  \n",
       "std       12.347618  \n",
       "min        1.000000  \n",
       "25%       40.000000  \n",
       "50%       40.000000  \n",
       "75%       45.000000  \n",
       "max       99.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "for c in data.columns.to_list():\n",
    "    data[c.strip()] = data[c]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.drop(list(filter(lambda x: x[0] == ' ', data.columns.to_list())), axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['39', 'State-gov', '77516', 'Bachelors', '13', 'Never-married',\n",
       "       'Adm-clerical', 'Not-in-family', 'White', 'Male', '2174', '0', '40',\n",
       "       'United-States', '<=50K'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Object `describe` not found.\n"
     ]
    }
   ],
   "source": [
    "data['<=50K'].describe?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Object `describe` not found.\n"
     ]
    }
   ],
   "source": [
    "data['<=50K'].describe?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([' <=50K', ' >50K'], dtype=object)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['<=50K'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['High-Income'] = data['<=50K'].map(lambda x: 1 if x == ' >50K' else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    32560.000000\n",
       "mean         0.240817\n",
       "std          0.427586\n",
       "min          0.000000\n",
       "25%          0.000000\n",
       "50%          0.000000\n",
       "75%          0.000000\n",
       "max          1.000000\n",
       "Name: High-Income, dtype: float64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['High-Income'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb65790d5c0>"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JdlfV+ACmtGDY89Kw1Hpeav3C/PV8rFwm2g+s6Hp9WqtJkobgWAmDe4BVSU5PcjxwCbBtxHOSpCXjmLhMVFWHklwJ7ACOAzZX1QPzeMg5X2pagOx5aVhqPS+1fmGeek5Vzcd+JUkLyLFymUiSNEKGgSRpcYfBbB9xkeR5SW5t6+9OsnL4sxycHvr9iyQPJrkvyR1Jpr3feCHp9WNMkvxJkkqy4G9D7KXnJBe37/UDST437DkOWg8/27+Z5M4k32o/3xeOYp6DkmRzkieT3D/D+iS5vn097kty1pwPWlWL8kHnjeh/B34bOB74F2D1EWPeBXyqLV8C3Drqec9zv68BfrUtv3Mh99trz23cC4C7gF3A+KjnPYTv8yrgW8DJ7fVLRj3vIfS8CXhnW14N7B31vOfY8x8AZwH3z7D+QuCrQIA1wN1zPeZiPjPo5SMu1gFb2vIXgHOTZIhzHKRZ+62qO6vqmfZyF52/51jIev0Yk2uAa4H/Gubk5kkvPb8d+ERVPQVQVU8OeY6D1kvPBbywLf8a8B9DnN/AVdVdwIFnGbIOuKk6dgEnJTllLsdczGEw3UdcnDrTmKo6BDwNvHgosxu8Xvrtdjmdf1ksZLP23E6fV1TV7cOc2Dzq5fv8MuBlSf4pya4ka4c2u/nRS88fAN6UZB+wHfiz4UxtZI72v/dZHRN/Z6DhSvImYBz4w1HPZT4leQ7wUeAtI57KsC2jc6logs7Z311JzqiqH450VvPrUuDGqrouye8Bf5PkFVX1P6Oe2EKxmM8MevmIi1+MSbKMzunlD4Yyu8Hr6SM9kvwR8FfA66rqp0Oa23yZrecXAK8AJpPspXNtddsCfxO5l+/zPmBbVf13VX0H+Dc64bBQ9dLz5cBtAFX1z8Cv0PkQu8Vq4B/hs5jDoJePuNgGrG/LbwD+sdq7MwvQrP0meSXwaTpBsNCvI8MsPVfV01W1vKpWVtVKOu+TvK6qdo9mugPRy8/139E5KyDJcjqXjR4Z5iQHrJeevwucC5Dkd+iEwX8OdZbDtQ24rN1VtAZ4uqoen8sOF+1loprhIy6SfBDYXVXbgM/SOZ2covNmzSWjm/Hc9NjvXwPPB/62vU/+3ap63cgmPUc99ryo9NjzDuC8JA8CPwf+sqoW6hlvrz1fBXwmyZ/TeTP5LQv4H3Yk+TydQF/e3ge5GnguQFV9is77IhcCU8AzwFvnfMwF/PWSJA3IYr5MJEnqkWEgSTIMJEmGgSQJw0CShGEgScIwkCQB/wsVcwCcDJ/51wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['High-Income'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fb67496be80>"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAcwAAAD4CAYAAABhR9aJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAVx0lEQVR4nO3df5heZX3n8feHoIKAcZekXio/hvJjLfIjwOBiXH9Q0SpU0QVE1LW0Vhb3ElZd2+ZqWEVcd7G0di+sq8bWpq1UXGrpZk0LKxCKUEKdEJIAKvIj/qgthAoIolTCd/94TvRhmMncE555hkner+vKNc9z3+fc53vPTM5n7nPOJKkqJEnS1u002wVIkjQXGJiSJDUwMCVJamBgSpLUwMCUJKnBzrNdgGbGggULamRkZLbLkKQ5Zc2aNfdW1cKJ+gzM7dTIyAhjY2OzXYYkzSlJvjVZn5dkJUlqYGBKktTAwJQkqYGBKUlSAwNTkqQGBqYkSQ38tZIBSzIf+DiwGAhwHXBWVT2QZARYXFV/3m17OjBaVe+enWonN7Jk5RPaNp5/wixUIkmTG+a5yhXm4P0RcGdVHVBV+wN3AX/Y9Y0AbxnUgZLMG9RY/Sb6BtxauyTNhmGfqwzMAUpyAHAU8OG+5vOA0ST7A+cDL01yU5L3dv3PS3JZkm8m+Z2+sV6d5PokNya5JMnuXfvGJB9NciNwynBmJkkyMAfrYOCmqtq8paF7fRPwQmAJ8JWqWlRVv99tsgg4FTgUODXJ3kkWAOcAx1XVkcAY8L6+4/xzVR1ZVRf3HzzJGUnGkoxt2rRppuYoSTsk72HOviur6gGAJLcC+wLPphe+1yUBeDpwfd8+X5hooKpaBiwDGB0drRmsWZJ2OAbmYN0KLEqyU1U9BpBkJ3qryFuBvSbY55G+15vpfU0CfLmqTpvkOD8cXMmSpBZekh2gqrodWEvvcuoW5wA3dn0PAns0DLUaeEl3T5QkuyU5aND1TmayJ8x8SlbSU8mwz1WuMAfvHcDHk9zRvb++awNYD2xOsg5YDtw30QBVtan7lZPPJ3lG13wOcNtMFT2e4ShpLhjmuSpV3uraHo2Ojpb/vZckTU+SNVU1OlGfl2QlSWpgYEqS1MDAlCSpgYEpSVIDA1OSpAYGpiRJDQxMSZIaGJiSJDUwMCVJamBgSpLUwMCUJKmBgSlJUgMDU5KkBgamJEkNDExJkhoYmJIkNTAwJUlqYGBKktTAwJQkqYGBKUlSAwNTkqQGBqYkSQ0MTEmSGhiYkiQ1MDAlSWpgYEqS1MDAlCSpgYEpSVIDA1OSpAYGpiRJDQxMSZIaGJiSJDUwMCVJamBgSpLUwMCUJKmBgSlJUgMDU5KkBgamJEkNDExJkhoYmJIkNdh5ujskWQSsBV5bVZcNvqQJj3ku8FBV/e409rkaeH9VjSX5a+AtVXX/AGrZCDwIbO6arqmqsyfZ9lwmqDvJed1+VzzZembKyJKVT2jbeP4Js1CJJE1umOeqbVlhngZc230cuCTTDvGpVNXxgwjLPsdW1aLuz4RhOUU9H5hrYbm1dkmaDcM+V00rMJMEOAU4HXhVkl269pEkX0+yPMltSS5KclyS65J8M8mLuu12S/LZJH+fZG2SE7v205OsSHIVcOUUNVyd5KPdGLcleWnXvmuSi5N8LcmlwK59+2xMsqB7/VdJ1iS5JckZfds8lOQjSdYlWZ3kOdP83Jyd5NYk65NcPEH/O5P8TVfn8iQn99X2P5LclGQsyZFJLk9yR5Izt3zek1yQ5OYkG5KcOp3aJElP3nRXmIuBu6rqDuBqoH/dewDwe8ALuj9vAf4d8H7gt7ttlgJXVdWLgGOBC5Ls1vUdCZxcVS9vqGPnboz3AB/s2t4FPFxVv9C1HTXJvr9WVUcBo8DZSfbs2ncDVlfV4cA1wDu3cvxVXcDdlOS9XdsS4IiqOgw4s3/jJO8Gfhl4Q1X9aILxvl1Vi4CvAMuBk4FjgA91/f8eWAQcDhxH7/P23PGDJDmjC92xTZs2baV8SdJ0TTcwTwO2rJ4u5vGXZe+qqg1V9RhwC3BlVRWwARjptnk1sCTJTfQCdxdgn67vy1X1/cY6/rL7uKZv7JcBnwOoqvXA+kn2PTvJOmA1sDdwYNf+L8CXJhh3Iv2XZH+/a1sPXJTkbcCjfdu+HXgtvR8GHplkvBXdxw3ADVX1YFVtAh5J8mx6P3h8vqo2V9XdwN8CR48fpKqWVdVoVY0uXLhwK+VLkqar+X5hknnAScCJSZYCAfZMske3SX8YPNb3/rG+4wQ4qaq+MW7sfwv8sO/9R+hWr93Ka7wtY2+e5hxeQW+F9uKqerh7MGiXrvsnXcD/dNxuzmu6thVV9YGtDH8CvdB+HbA0yaFd+wZ6q8O9gLsm2bf/czX+8zjwe7qSpOmbzgrzlcD6qtq7qkaqal/gi8AbpzHG5cBZ3b1Qkhwx0UZVtXTLCm4aY19D7zIwSQ4BDptgm/nAfV1YvoDeZc9JdSu6LSvJScMyyU7A3lW1Cvit7ji7d91rgf8IrEjyvGnMp99XgFOTzEuykF4w//02jjWlyZ4w8ylZSU8lwz5XTWf1chpw6bi2L9K7d3hN4xgfBv4nsL4Lmbvo3dsbhE8Cf5zka8DX+NnKsN9lwJndNt+gd1l2W6xKsuXXStYD7wA+l2Q+vVX0hVV1f/dzAVV1bZL3AyuTvGobjncp8GJgHVDAb1bVP21j7U0MR0lzwTDPVfnZVUhtT0ZHR2tsbGy2y5CkOSXJmqoanajPf+lHkqQGBqYkSQ0MTEmSGhiYkiQ1MDAlSWpgYEqS1MDAlCSpgYEpSVIDA1OSpAYGpiRJDQxMSZIaGJiSJDUwMCVJamBgSpLUwMCUJKmBgSlJUgMDU5KkBgamJEkNDExJkhoYmJIkNTAwJUlqYGBKktTAwJQkqYGBKUlSAwNTkqQGBqYkSQ0MTEmSGhiYkiQ1MDAlSWpgYEqS1MDAlCSpgYEpSVIDA1OSpAYGpiRJDQxMSZIaGJiSJDUwMCVJamBgSpLUwMCUJKmBgSlJUoOdBz1gkkXAWuC1VXXZFNv+IfCxqrp1AMe9Gngu8GPgIeDXquobT3LMc4GHqup3J+k/HRitqnc/meO0HGvYRpasfELbxvNPmIVKJGlywzxXzcQK8zTg2u7jVlXVrw8iLPu8taoOB/4EuGCA4z7lJBn4DztbTPQNuLV2SZoNwz5XDTQwkwQ4BTgdeFWSXbr23ZKsTLIuyc1JTu3ar04y2r3+ZJKxJLck+VDfmBuTfCjJjUk2JHlBQynXAAd0+38gyVe74y7raiTJAUmu6Gq6Mcn+U8zt7CS3Jlmf5OIJ+l+X5IYka7txn9O1n5vks91c70xydt8+S5PcluRa4N/0te+f5LIka5J8ZcuckyxP8qkkNwC/0/B5kCQNyKBXmIuBu6rqDuBqYMu6+DXA96rq8Ko6BJjoUu3SqhoFDgNenuSwvr57q+pI4JPA+xvqeB2woXv9B1V1dHfcXYFf7tovAj7RrUgXA/84xZhLgCOq6jDgzAn6rwWOqaojgIuB3+zrewHwS8CLgA8meVqSo4A3A4uA44Gj+7ZfBpxVVUd18/1ffX17AYur6n3jC0hyRvdDx9imTZummI4kaToGHZin0QsLuo9bLstuoLfi/GiSl1bVAxPs+6YkN9K7//lC4OC+vr/sPq4BRrZy/IuS3AS8hJ8F67Hdym8D8IvAC5PsATy/qi4FqKofV9XDU8xtfTf+24BHJ+jfC7i8O85vdHPYYmVVPVJV9wL3AM8BXgpcWlUPV9UPgBUASXanF+CXdHP5NL17s1tcUlWbJyqwqpZV1WhVjS5cuHCK6UiSpmNg98GSzANOAk5MshQIsGeSParqtiRH0ltJ/bckV1bVeX377kcv4I6uqvuSLAd26Rv+ke7j5i01J7mcXvCMVdWvd/1vraqxvnF3obc6G62q73QP1vSPO34OH6FbFVfVonHdJwAvo7d6XZrk0HH9H6f3ANOKJK8Azp2g/sfNYRI7AfdPcPwtfriVfSVJM2SQK8xXAuurau+qGqmqfYEvAm9M8jzg4ar6HL2HcY4ct++z6AXBA929v9dOdbCq+qWqWtQXlhPZEo73diu3k7t9HwS+m+QNAEmekeSZVbW0G/NxYZVkJ2DvqloF/BYwH9h93LHmA//Qvf6Vqeqnd5/1DUl27Va8r+tq+wFwV5JTumMnyeEN4w3MZE+Y+ZSspKeSYZ+rBvmk5WnApePavgi8C7gbuCDJY8BPurafqqp1SdYCXwe+A1w3iIKq6v4knwFuBv4J+Gpf938APp3kvK6mU4A7JxlqHvC5JPPprZwv7Mbu3+ZcepdR7wOuAvaborYbk3wBWEfvMm1/bW8FPpnkHOBp9C5vr5t6xoNjOEqaC4Z5rkpVDe1gGp7R0dEaGxubekNJ0k8lWdM9gPoE/ks/kiQ1MDAlSWpgYEqS1MDAlCSpgYEpSVIDA1OSpAYGpiRJDQxMSZIaGJiSJDUwMCVJamBgSpLUwMCUJKmBgSlJUgMDU5KkBgamJEkNDExJkhoYmJIkNTAwJUlqYGBKktTAwJQkqYGBKUlSAwNTkqQGBqYkSQ0MTEmSGhiYkiQ1MDAlSWpgYEqS1MDAlCSpgYEpSVIDA1OSpAYGpiRJDQxMSZIaGJiSJDUwMCVJamBgSpLUwMCUJKmBgSlJUgMDU5KkBgamJEkNDExJkhrsPNsFbI+SbAQeBDYD84Bzqur/dH0PVdXu2zDmcuBLVfUXAyx1UiNLVj6hbeP5Jwzj0JLUbJjnKleYM+fYqloEnAxcONvFTMdE34Bba5ek2TDsc5WBOfOeBdw3vjHJ7kmuTHJjkg1JTuzre3uS9UnWJfmzCfb9cJLlSebNcO2SpI6XZGfOqiQBfh540wT9PwbeWFU/SLIAWJ1kBXAwcA6wuKruTfKv+3dKcgGwB/CrVVXj+s4AzgDYZ599Bj4hSdqRucKcOcdW1SHAocAfJBl/3zLAf0+yHrgCeD7wHOAXgUuq6l6Aqvp+3z7/FZhfVWeOD8tu22VVNVpVowsXLpyBKUnSjsvAnGFVdQdwN72VY7+3AguBo7p7nXcDu0wx3FeBo8avOiVJM8/AnGFJfg7YD/jWuK75wD1V9ZMkxwL7du1XAack2bPbvz8cLwPOB1Ym2WOmap7sCTOfkpX0VDLsc5X3MGfOqiSbgacBS6rq7nH9FwH/N8kGYAz4OkBV3ZLkI8DfdvuvBU7fslNVXdKF5Yokx1fVj2aieMNR0lwwzHNVJrgVpu3A6OhojY2NzXYZkjSnJFlTVaMT9XlJVpKkBgamJEkNDExJkhoYmJIkNTAwJUlqYGBKktTAwJQkqYGBKUlSAwNTkqQGBqYkSQ0MTEmSGhiYkiQ1MDAlSWpgYEqS1MDAlCSpgYEpSVIDA1OSpAYGpiRJDQxMSZIaGJiSJDUwMCVJamBgSpLUwMCUJKmBgSlJUgMDU5KkBgamJEkNDExJkhoYmJIkNTAwJUlqYGBKktTAwJQkqYGBKUlSAwNTkqQGBqYkSQ0MTEmSGhiYkiQ1MDAlSWpgYEqS1MDAlCSpgYEpSVKDnWe7gO1Fkv8M7FdV7+nefxrYv6qO696fBRwIfAz4UlUdMsEY5wHXVNUVSd4DLKuqh4c2iT4jS1Y+oW3j+SfMQiWSNLlhnqtcYQ7OdcDivveHA/OTzOveLwb+bmsDVNUHquqK7u17gGcOvMoGE30Dbq1dkmbDsM9VBubg3AQclGTXJPOBH3Vth3b9i+mFKsC8JJ9JckuS/5dkV4Aky5OcnORs4HnAqiSrur5XJ7k+yY1JLkmy+1BnJ0k7OANzQKrqUWAtcDRwDHADsBpYnOT5QKrqO93mBwKfqKoXAvcDJ40b60Lge8CxVXVskgXAOcBxVXUkMAa8b3wNSc5IMpZkbNOmTTMyT0naUXkPc7D+jt5KclfgeuCbwG8Dm3j85di7quqm7vUaYGSKcY8BDgauSwLw9G78x6mqZcAygNHR0drWSUiSnsjAHKzrgDOBXYBP0AvKg3liYD7S93ozvYDdmgBfrqrTBleqJGk6vCQ7WNfTWw0urKp7qqroheWJ/Oz+ZasHgT2616uBlyQ5ACDJbkkOGlDNTzDZE2Y+JSvpqWTY5ypXmANUVfcl2QTc0td8PfASYN00h1sGXJbke919zNOBzyd5Rtd/DnDbk615MoajpLlgmOeq9BZB2t6Mjo7W2NjYbJchSXNKkjVVNTpRn5dkJUlqYGBKktTAwJQkqYGBKUlSAwNTkqQGPiW7nep+veVbAxhqAXDvAMaZK5zv9mtHmis43221b1UtnKjDwNRWJRmb7BHr7ZHz3X7tSHMF5zsTvCQrSVIDA1OSpAYGpqaybLYLGDLnu/3akeYKznfgvIcpSVIDV5iSJDUwMCVJamBgCoAkr0nyjSS3J1kyQf8zknyh678hycjwqxyMhrm+L8mtSdYnuTLJvrNR56BMNd++7U5KUknm9K8itMw3yZu6r/EtSf582DUOUsP38z5JViVZ231PHz8bdQ5Cks8muSfJzZP0J8mF3edifZIjB1pAVflnB/8DzAPuAH4eeDq9/7vz4HHb/CfgU93rNwNfmO26Z3CuxwLP7F6/a67OtXW+3XZ7ANfQ+8/KR2e77hn++h4IrAX+Vff+52a77hme7zLgXd3rg4GNs133k5jvy4AjgZsn6T8e+BsgwDHADYM8vitMAbwIuL2q7qyqfwEuBk4ct82JwJ90r/8CeGWSDLHGQZlyrlW1qqoe7t6uBvYaco2D1PK1Bfgw8FHgx8Msbga0zPedwCeq6j6AqrpnyDUOUst8C3hW93o+8L0h1jdQVXUN8P2tbHIi8KfVsxp4dpLnDur4BqYAng98p+/9d7u2CbepqkeBB4A9h1LdYLXMtd876P3EOldNOd/ustXeVbVymIXNkJav70HAQUmuS7I6yWuGVt3gtcz3XOBtSb4L/DVw1nBKmxXT/fs9LTsPaiBpe5PkbcAo8PLZrmWmJNkJ+Bhw+iyXMkw707ss+wp6Vw+uSXJoVd0/q1XNnNOA5VX1e0leDPxZkkOq6rHZLmyucYUpgH8A9u57v1fXNuE2SXamd2nnn4dS3WC1zJUkxwFLgddX1SNDqm0mTDXfPYBDgKuTbKR332fFHH7wp+Xr+11gRVX9pKruAm6jF6BzUct83wH8b4Cquh7Yhd4/VL49avr7va0MTAF8FTgwyX5Jnk7voZ4V47ZZAfxK9/pk4Krq7rLPMVPONckRwKfpheVcvr8FU8y3qh6oqgVVNVJVI/Tu2b6+qsZmp9wnreV7+a/orS5JsoDeJdo7h1nkALXM99vAKwGS/AK9wNw01CqHZwXw9u5p2WOAB6rqHwc1uJdkRVU9muTdwOX0nrr7bFXdkuQ8YKyqVgB/RO9Szu30brq/efYq3naNc70A2B24pHuu6dtV9fpZK/pJaJzvdqNxvpcDr05yK7AZ+I2qmotXS1rn+1+AzyR5L70HgE6foz/skuTz9H7YWdDdk/0g8DSAqvoUvXu0xwO3Aw8DvzrQ48/Rz5skSUPlJVlJkhoYmJIkNTAwJUlqYGBKktTAwJQkqYGBKUlSAwNTkqQG/x86EXBg6GdU4AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data['High-Income'], data['White'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fb67470d278>"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data['High-Income'], data['Adm-clerical'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fb6748a52b0>"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data['High-Income'], data['Bachelors'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "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>39</th>\n",
       "      <th>77516</th>\n",
       "      <th>13</th>\n",
       "      <th>2174</th>\n",
       "      <th>0</th>\n",
       "      <th>40</th>\n",
       "      <th>High-Income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.076646</td>\n",
       "      <td>0.036527</td>\n",
       "      <td>0.077674</td>\n",
       "      <td>0.057775</td>\n",
       "      <td>0.068756</td>\n",
       "      <td>0.234039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77516</th>\n",
       "      <td>-0.076646</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.043159</td>\n",
       "      <td>0.000437</td>\n",
       "      <td>-0.010259</td>\n",
       "      <td>-0.018770</td>\n",
       "      <td>-0.009481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.036527</td>\n",
       "      <td>-0.043159</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.122627</td>\n",
       "      <td>0.079932</td>\n",
       "      <td>0.148127</td>\n",
       "      <td>0.335182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2174</th>\n",
       "      <td>0.077674</td>\n",
       "      <td>0.000437</td>\n",
       "      <td>0.122627</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.031614</td>\n",
       "      <td>0.078409</td>\n",
       "      <td>0.223333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.057775</td>\n",
       "      <td>-0.010259</td>\n",
       "      <td>0.079932</td>\n",
       "      <td>-0.031614</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.054256</td>\n",
       "      <td>0.150523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0.068756</td>\n",
       "      <td>-0.018770</td>\n",
       "      <td>0.148127</td>\n",
       "      <td>0.078409</td>\n",
       "      <td>0.054256</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.229690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>High-Income</th>\n",
       "      <td>0.234039</td>\n",
       "      <td>-0.009481</td>\n",
       "      <td>0.335182</td>\n",
       "      <td>0.223333</td>\n",
       "      <td>0.150523</td>\n",
       "      <td>0.229690</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   39     77516        13      2174         0        40  \\\n",
       "39           1.000000 -0.076646  0.036527  0.077674  0.057775  0.068756   \n",
       "77516       -0.076646  1.000000 -0.043159  0.000437 -0.010259 -0.018770   \n",
       "13           0.036527 -0.043159  1.000000  0.122627  0.079932  0.148127   \n",
       "2174         0.077674  0.000437  0.122627  1.000000 -0.031614  0.078409   \n",
       "0            0.057775 -0.010259  0.079932 -0.031614  1.000000  0.054256   \n",
       "40           0.068756 -0.018770  0.148127  0.078409  0.054256  1.000000   \n",
       "High-Income  0.234039 -0.009481  0.335182  0.223333  0.150523  0.229690   \n",
       "\n",
       "             High-Income  \n",
       "39              0.234039  \n",
       "77516          -0.009481  \n",
       "13              0.335182  \n",
       "2174            0.223333  \n",
       "0               0.150523  \n",
       "40              0.229690  \n",
       "High-Income     1.000000  "
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get f1 Scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((22792, 14), (9768, 14), (22792,), (9768,))"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(data[filter(lambda x: x != 'High-Income' and x != '<=50K',data.columns.to_list())], data['High-Income'], test_size=0.3)\n",
    "\n",
    "(X_train.shape, X_test.shape, y_train.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>39</th>\n",
       "      <th>State-gov</th>\n",
       "      <th>77516</th>\n",
       "      <th>Bachelors</th>\n",
       "      <th>13</th>\n",
       "      <th>Never-married</th>\n",
       "      <th>Adm-clerical</th>\n",
       "      <th>Not-in-family</th>\n",
       "      <th>White</th>\n",
       "      <th>Male</th>\n",
       "      <th>2174</th>\n",
       "      <th>0</th>\n",
       "      <th>40</th>\n",
       "      <th>United-States</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21194</th>\n",
       "      <td>55</td>\n",
       "      <td>Private</td>\n",
       "      <td>196126</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Separated</td>\n",
       "      <td>Craft-repair</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>?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8722</th>\n",
       "      <td>18</td>\n",
       "      <td>Private</td>\n",
       "      <td>156874</td>\n",
       "      <td>12th</td>\n",
       "      <td>8</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>27</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6310</th>\n",
       "      <td>37</td>\n",
       "      <td>Private</td>\n",
       "      <td>31368</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11456</th>\n",
       "      <td>62</td>\n",
       "      <td>Federal-gov</td>\n",
       "      <td>159165</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>Exec-managerial</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>36</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479</th>\n",
       "      <td>33</td>\n",
       "      <td>Private</td>\n",
       "      <td>111746</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12</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>45</td>\n",
       "      <td>Portugal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       39     State-gov   77516      Bachelors  13        Never-married  \\\n",
       "21194  55       Private  196126      Bachelors  13            Separated   \n",
       "8722   18       Private  156874           12th   8        Never-married   \n",
       "6310   37       Private   31368   Some-college  10   Married-civ-spouse   \n",
       "11456  62   Federal-gov  159165        HS-grad   9              Widowed   \n",
       "479    33       Private  111746     Assoc-acdm  12   Married-civ-spouse   \n",
       "\n",
       "           Adm-clerical   Not-in-family   White     Male  2174  0  40  \\\n",
       "21194      Craft-repair   Not-in-family   White     Male     0  0  40   \n",
       "8722      Other-service       Own-child   White     Male     0  0  27   \n",
       "6310       Craft-repair         Husband   White     Male     0  0  40   \n",
       "11456   Exec-managerial   Not-in-family   White   Female     0  0  36   \n",
       "479        Craft-repair         Husband   White     Male     0  0  45   \n",
       "\n",
       "        United-States  \n",
       "21194               ?  \n",
       "8722    United-States  \n",
       "6310    United-States  \n",
       "11456   United-States  \n",
       "479          Portugal  "
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12145    0\n",
       "884      0\n",
       "23851    0\n",
       "584      0\n",
       "17630    0\n",
       "        ..\n",
       "1796     0\n",
       "29018    0\n",
       "27879    0\n",
       "25506    0\n",
       "30324    0\n",
       "Name: White, Length: 9768, dtype: int64"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = X_test['White'].map(lambda x: 0)\n",
    "\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.76      1.00      0.86      7438\n",
      "           1       0.00      0.00      0.00      2330\n",
      "\n",
      "    accuracy                           0.76      9768\n",
      "   macro avg       0.38      0.50      0.43      9768\n",
      "weighted avg       0.58      0.76      0.66      9768\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_data(data):\n",
    "    return train_test_split(data[filter(lambda x: x != 'High-Income' and x != '<=50K',data.columns.to_list())], data['High-Income'], test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Numeric Colors\n",
    "\n",
    "c_dct = {}\n",
    "\n",
    "for i, c in enumerate(data.White.unique()):\n",
    "    c_dct[c] = i\n",
    "    \n",
    "data['Color'] = data['White'].map(lambda x: c_dct[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Numeric Degrees\n",
    "\n",
    "d_dct = {}\n",
    "\n",
    "for i, d in enumerate(data['Bachelors'].unique()):\n",
    "    d_dct[d] = i\n",
    "    \n",
    "data['Degree'] = data['Bachelors'].map(lambda x: d_dct[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Numeric Jobs\n",
    "\n",
    "j_dct = {}\n",
    "\n",
    "for i, j in enumerate(data['Adm-clerical'].unique()):\n",
    "    j_dct[j] = i\n",
    "    \n",
    "data['Job'] = data['Adm-clerical'].map(lambda x: j_dct[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "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>39</th>\n",
       "      <th>77516</th>\n",
       "      <th>13</th>\n",
       "      <th>2174</th>\n",
       "      <th>0</th>\n",
       "      <th>40</th>\n",
       "      <th>High-Income</th>\n",
       "      <th>Color</th>\n",
       "      <th>Degree</th>\n",
       "      <th>Job</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.076646</td>\n",
       "      <td>0.036527</td>\n",
       "      <td>0.077674</td>\n",
       "      <td>0.057775</td>\n",
       "      <td>0.068756</td>\n",
       "      <td>0.234039</td>\n",
       "      <td>-0.039999</td>\n",
       "      <td>0.007768</td>\n",
       "      <td>-0.000245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77516</th>\n",
       "      <td>-0.076646</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.043159</td>\n",
       "      <td>0.000437</td>\n",
       "      <td>-0.010259</td>\n",
       "      <td>-0.018770</td>\n",
       "      <td>-0.009481</td>\n",
       "      <td>0.000488</td>\n",
       "      <td>0.024157</td>\n",
       "      <td>0.013120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.036527</td>\n",
       "      <td>-0.043159</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.122627</td>\n",
       "      <td>0.079932</td>\n",
       "      <td>0.148127</td>\n",
       "      <td>0.335182</td>\n",
       "      <td>-0.039748</td>\n",
       "      <td>-0.279589</td>\n",
       "      <td>-0.284144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2174</th>\n",
       "      <td>0.077674</td>\n",
       "      <td>0.000437</td>\n",
       "      <td>0.122627</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.031614</td>\n",
       "      <td>0.078409</td>\n",
       "      <td>0.223333</td>\n",
       "      <td>-0.007990</td>\n",
       "      <td>0.024159</td>\n",
       "      <td>-0.066277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.057775</td>\n",
       "      <td>-0.010259</td>\n",
       "      <td>0.079932</td>\n",
       "      <td>-0.031614</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.054256</td>\n",
       "      <td>0.150523</td>\n",
       "      <td>-0.017192</td>\n",
       "      <td>-0.003199</td>\n",
       "      <td>-0.037941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0.068756</td>\n",
       "      <td>-0.018770</td>\n",
       "      <td>0.148127</td>\n",
       "      <td>0.078409</td>\n",
       "      <td>0.054256</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.229690</td>\n",
       "      <td>-0.033115</td>\n",
       "      <td>-0.050027</td>\n",
       "      <td>-0.081628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>High-Income</th>\n",
       "      <td>0.234039</td>\n",
       "      <td>-0.009481</td>\n",
       "      <td>0.335182</td>\n",
       "      <td>0.223333</td>\n",
       "      <td>0.150523</td>\n",
       "      <td>0.229690</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.067720</td>\n",
       "      <td>-0.046237</td>\n",
       "      <td>-0.161575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Color</th>\n",
       "      <td>-0.039999</td>\n",
       "      <td>0.000488</td>\n",
       "      <td>-0.039748</td>\n",
       "      <td>-0.007990</td>\n",
       "      <td>-0.017192</td>\n",
       "      <td>-0.033115</td>\n",
       "      <td>-0.067720</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.029628</td>\n",
       "      <td>0.023677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Degree</th>\n",
       "      <td>0.007768</td>\n",
       "      <td>0.024157</td>\n",
       "      <td>-0.279589</td>\n",
       "      <td>0.024159</td>\n",
       "      <td>-0.003199</td>\n",
       "      <td>-0.050027</td>\n",
       "      <td>-0.046237</td>\n",
       "      <td>0.029628</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.065647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Job</th>\n",
       "      <td>-0.000245</td>\n",
       "      <td>0.013120</td>\n",
       "      <td>-0.284144</td>\n",
       "      <td>-0.066277</td>\n",
       "      <td>-0.037941</td>\n",
       "      <td>-0.081628</td>\n",
       "      <td>-0.161575</td>\n",
       "      <td>0.023677</td>\n",
       "      <td>0.065647</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   39     77516        13      2174         0        40  \\\n",
       "39           1.000000 -0.076646  0.036527  0.077674  0.057775  0.068756   \n",
       "77516       -0.076646  1.000000 -0.043159  0.000437 -0.010259 -0.018770   \n",
       "13           0.036527 -0.043159  1.000000  0.122627  0.079932  0.148127   \n",
       "2174         0.077674  0.000437  0.122627  1.000000 -0.031614  0.078409   \n",
       "0            0.057775 -0.010259  0.079932 -0.031614  1.000000  0.054256   \n",
       "40           0.068756 -0.018770  0.148127  0.078409  0.054256  1.000000   \n",
       "High-Income  0.234039 -0.009481  0.335182  0.223333  0.150523  0.229690   \n",
       "Color       -0.039999  0.000488 -0.039748 -0.007990 -0.017192 -0.033115   \n",
       "Degree       0.007768  0.024157 -0.279589  0.024159 -0.003199 -0.050027   \n",
       "Job         -0.000245  0.013120 -0.284144 -0.066277 -0.037941 -0.081628   \n",
       "\n",
       "             High-Income     Color    Degree       Job  \n",
       "39              0.234039 -0.039999  0.007768 -0.000245  \n",
       "77516          -0.009481  0.000488  0.024157  0.013120  \n",
       "13              0.335182 -0.039748 -0.279589 -0.284144  \n",
       "2174            0.223333 -0.007990  0.024159 -0.066277  \n",
       "0               0.150523 -0.017192 -0.003199 -0.037941  \n",
       "40              0.229690 -0.033115 -0.050027 -0.081628  \n",
       "High-Income     1.000000 -0.067720 -0.046237 -0.161575  \n",
       "Color          -0.067720  1.000000  0.029628  0.023677  \n",
       "Degree         -0.046237  0.029628  1.000000  0.065647  \n",
       "Job            -0.161575  0.023677  0.065647  1.000000  "
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb64a975908>"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['Job'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb64a81dfd0>"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['Color'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb649d63e80>"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['Degree'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fb649c6d588>"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data['Job'], data['Degree'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fb649bceba8>"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data['Bachelors'], data['High-Income'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Color\n",
       "0    AxesSubplot(0.125,0.125;0.775x0.755)\n",
       "1    AxesSubplot(0.125,0.125;0.775x0.755)\n",
       "2    AxesSubplot(0.125,0.125;0.775x0.755)\n",
       "3    AxesSubplot(0.125,0.125;0.775x0.755)\n",
       "4    AxesSubplot(0.125,0.125;0.775x0.755)\n",
       "Name: High-Income, dtype: object"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.groupby('Color')['High-Income'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Job\n",
       "0     AxesSubplot(0.125,0.125;0.775x0.755)\n",
       "1     AxesSubplot(0.125,0.125;0.775x0.755)\n",
       "2     AxesSubplot(0.125,0.125;0.775x0.755)\n",
       "3     AxesSubplot(0.125,0.125;0.775x0.755)\n",
       "4     AxesSubplot(0.125,0.125;0.775x0.755)\n",
       "5     AxesSubplot(0.125,0.125;0.775x0.755)\n",
       "6     AxesSubplot(0.125,0.125;0.775x0.755)\n",
       "7     AxesSubplot(0.125,0.125;0.775x0.755)\n",
       "8     AxesSubplot(0.125,0.125;0.775x0.755)\n",
       "9     AxesSubplot(0.125,0.125;0.775x0.755)\n",
       "10    AxesSubplot(0.125,0.125;0.775x0.755)\n",
       "11    AxesSubplot(0.125,0.125;0.775x0.755)\n",
       "12    AxesSubplot(0.125,0.125;0.775x0.755)\n",
       "13    AxesSubplot(0.125,0.125;0.775x0.755)\n",
       "14    AxesSubplot(0.125,0.125;0.775x0.755)\n",
       "Name: High-Income, dtype: object"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.groupby('Job')['High-Income'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Degree\n",
       "0     AxesSubplot(0.125,0.125;0.775x0.755)\n",
       "1     AxesSubplot(0.125,0.125;0.775x0.755)\n",
       "2     AxesSubplot(0.125,0.125;0.775x0.755)\n",
       "3     AxesSubplot(0.125,0.125;0.775x0.755)\n",
       "4     AxesSubplot(0.125,0.125;0.775x0.755)\n",
       "5     AxesSubplot(0.125,0.125;0.775x0.755)\n",
       "6     AxesSubplot(0.125,0.125;0.775x0.755)\n",
       "7     AxesSubplot(0.125,0.125;0.775x0.755)\n",
       "8     AxesSubplot(0.125,0.125;0.775x0.755)\n",
       "9     AxesSubplot(0.125,0.125;0.775x0.755)\n",
       "10    AxesSubplot(0.125,0.125;0.775x0.755)\n",
       "11    AxesSubplot(0.125,0.125;0.775x0.755)\n",
       "12    AxesSubplot(0.125,0.125;0.775x0.755)\n",
       "13    AxesSubplot(0.125,0.125;0.775x0.755)\n",
       "14    AxesSubplot(0.125,0.125;0.775x0.755)\n",
       "15    AxesSubplot(0.125,0.125;0.775x0.755)\n",
       "Name: High-Income, dtype: object"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.groupby('Degree')['High-Income'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "High-Income\n",
       "0    AxesSubplot(0.125,0.125;0.775x0.755)\n",
       "1    AxesSubplot(0.125,0.125;0.775x0.755)\n",
       "Name: White, dtype: object"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.groupby('High-Income')['White'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "High-Income\n",
       "0    AxesSubplot(0.125,0.125;0.775x0.755)\n",
       "1    AxesSubplot(0.125,0.125;0.775x0.755)\n",
       "Name: Adm-clerical, dtype: object"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.groupby('High-Income')['Adm-clerical'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "High-Income\n",
       "0    AxesSubplot(0.125,0.125;0.775x0.755)\n",
       "1    AxesSubplot(0.125,0.125;0.775x0.755)\n",
       "Name: Bachelors, dtype: object"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.groupby('High-Income')['Bachelors'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Bachelors\n",
       " 10th            AxesSubplot(0.125,0.125;0.775x0.755)\n",
       " 11th            AxesSubplot(0.125,0.125;0.775x0.755)\n",
       " 12th            AxesSubplot(0.125,0.125;0.775x0.755)\n",
       " 1st-4th         AxesSubplot(0.125,0.125;0.775x0.755)\n",
       " 5th-6th         AxesSubplot(0.125,0.125;0.775x0.755)\n",
       " 7th-8th         AxesSubplot(0.125,0.125;0.775x0.755)\n",
       " 9th             AxesSubplot(0.125,0.125;0.775x0.755)\n",
       " Assoc-acdm      AxesSubplot(0.125,0.125;0.775x0.755)\n",
       " Assoc-voc       AxesSubplot(0.125,0.125;0.775x0.755)\n",
       " Bachelors       AxesSubplot(0.125,0.125;0.775x0.755)\n",
       " Doctorate       AxesSubplot(0.125,0.125;0.775x0.755)\n",
       " HS-grad         AxesSubplot(0.125,0.125;0.775x0.755)\n",
       " Masters         AxesSubplot(0.125,0.125;0.775x0.755)\n",
       " Preschool       AxesSubplot(0.125,0.125;0.775x0.755)\n",
       " Prof-school     AxesSubplot(0.125,0.125;0.775x0.755)\n",
       " Some-college    AxesSubplot(0.125,0.125;0.775x0.755)\n",
       "Name: White, dtype: object"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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o/0VJlo0MW72e8/x4+xu1zUqShUk+kWRlkh+07oVt2OIkfzQy7kuT/O2Gq+1d9Zjz8Znk2CR7TWi5U0kuT/LdJN9M8ogJzHOdx/0k23x9PmNmskmEDvBN4A9H+h8NLGyv1aEN+9d1zaCq3lZV00H1OuBeCR3ghcA32u+JSzLxhz+q6qCqumWCs9yvqvZuP69Zj/qMbqsNYb+q2hs4BJgxNMVxwJVVtUdVPRy4Cji2DVsM/NFME45r5Fifrzkfn1X1sgm/outFVfVo4ATgvROc70ZnXZ9Tm0roXAD8dpLt2pnUba3sd9vwP2QIJoCtknwsySVJ/inJdnD32WaS1wAPBc5McmYb9rQk30pyXpLPJtlhfSqZJMChwEuBpybZtpUvTvK9VofvJ/lUkqe0M54rkuzTxts+yfFJvpPk/Omz7HbGsiLJGcDps9RhKsm72zy+n+SJrXy7JCcmuSzJF4HtRqa5OsnOrfvkJOe29jtiZJzVSY5uZ2rfTrJozLZ5TZJLk1yY4fVHaw7/70n+X6vnXVcGrW5/1a48zknymCSntjPrl0+3e5L3Jrm4XZ08f5y6rcMDgJvXUtcdkpze9pc1r4Ze0tbxu0k+uZZp39HWb1IfohtEkj2AxwLvGCk+CliS5OHAu4Antu32+jb8oUm+2vb594zMa63HX9v2705yHsNxNd86z3R8bp/ky22bXTy9/+SedwM+0va/S5K8fWSeVyd5+8i+8Mg5VOVfgD3a9G9LcnZb7jGtjiTZI8nXWp3Oa226rnWb7fh6VpKz2ufK16aP3wxXMMe3db0yw+fj9DRvaZ8h32B4O8x0+cPbdjw3yden17nt1x9NchbwnjXrcJeq2iR+gDOBJwEHMOzQhwN/AuwC/KiNsxi4A9i79X8G+G+t++PAIa37amDn1r1z2wm2b/1/DrxtPev4BOD01v1/gOeuUa/fZQj6c4HjgTC8e+7kNt5fjtR3R+D7wPYMB8kqYKcZlrsc+NPWPQW8r3UfBHytdf9PhkfUAX6v1WfJWtpjp/Z7O+Bi4EGtv4Bnte73AG+doS5XAxcxnBRcALy+lf8bsM30uo3WG3gV8KWR4Wtuq1e07vcDFwL3Bx4MXN/KnwucxvAY/iLgR8BD1nMbTtf/YuAXwDNHhq1uvxcADxjZf1a2bfmots3WbMuPM1w1vRf4KO2p0U35B3g28MW1lH+xDVsKnDJS/lLgSmAhsC3wQ4Y/FJ/x+Gvb4o0TrPNMx+dzgY+NjLdw5FiaPkamt+VWrfz3Rur46tb9Jwxvzl/bskfn9WfASaPzbd2f5O5j7CzgOa17W+B+a5nncu4+7td2fL0U+NvW/cDp/Q54GXd/RixnuEu0TdsWPwXuy3BCcRHDHaEHtH18elmnA3u27scBZ4zs56cAW61rO2x0r8FZh39luKLZDvgWcAXwF8CN3PPW2lVVNf0/Zs9l+MBfl32BvYBvtpOMrdv818cLgemzjBOBlwCfH6nXRQBJLmHY+SvJRSN1fBrw7Nx973Rb4Lda92lVddMc6/GF9nt0/Z9Eu1VUVRcmuXCGaV+T5DmtezdgT4Yd8T8Ydqjp+T51Hcvfr6rWfGfUhcCnkpwMnDxS/hLgGuDgqvrVDPOb/gPii4AdqupnwM+S3J7hu6j/Any6qu4Erk/yz8AfsP5/eLxfVf2knV2enmSqqka/zwnwl0meBPya4cRnEfBk4LPT677G9vpfwFlVdQRbrtOr6laAJJcCD2M4uVrX8XfSBJc/0/F5EfC+JO9mCMqvr2Xa52W48l8APKTVefoYGj3e/us6lv+pJLfRgqqV7ZfkjQwf7jsBlySZAnapqi8CVNUv57BuMx1f03YFTkryEIY2vmpk2Jer6nbg9iQ3MOzLT2Q4qfgFQJIV7fcODJ/Dn23bC4bAmvbZdhzOaFMKnW8CL2f4IP47hrDZi98MndtHuu9k5DbSDMLwgT6v72Da7ZLnAsuSvKXN90FJ7r+Wev16pP/X3L0dwnD2dY+XnCZ5HPDzkf6jgWcA1PDdw5qm530nY2zjJEuBpwCPr6pftJ1/2zb4V9VOZ6bn29b53Fa2oqreto7ZP4Mh+J4FvCXJ9K3Ri4C9GQ6Kq2aYdrSt1mzHe20frqofJLmeYT/7zsigFzFcaT22qn6V5GrubqeZnA08NslOY5w8bMwuBfZOcp+q+jVAkvswbMtLGbbnmtY8Nhcw+/H38xnKx7Ku47Oqvp/kMQx3Bt6Z5PSqOmpk2t0Zrsj/oKpuTvJx7rm9f+N4S3Iqw4f3OdVecMzwnc45I/PdFvgwwxXQNUmWs479aJbjfqbja9rfAH9dVSvacb58LfW/xzrM4D7ALTN87sActtem8p0ODGc/+wIPrqob2gfgjQy3p765zil/088YbtEAfBt4QrtHPX1/97fXo377AxdW1W5VtbiqHsZwFvWcWaYbdSrw6pH7ur+/tpGq6i3VvqgfY97/QvtiN8nvMNxiW9NC4OYWOI9kaO8ZVdWddfcDAzMGTvsw2q2qzmS4fbIQmP7e7HzgfwArkjx0jPUZ9XXg+Um2SvJghoPvO7NMM6sk/wnYneFW0KiFwA0tcPZjOGMHOAM4NMmD2vQ7jUzzVYbbwl8eORHZZFXVSoZtN/r/nN8KnNeGjR5j6zKp4282Mx6fbb/7RVX9I8Mt0MesMe0DGD5Mb23fhTx9toVV1QHtuHjZOkabDpiftCuIQ9q0PwNWJTkYIMk2Se4303E/y/E1bSF3v+fysNnqz/B5cXCG71jvzxBmVNW/A1clObQtO0kePYf53WWTudJpZxg3AqOvzfkWw33a7445u2OAryb5t6raL8lLgU8nmb5MfCvDvflxvJDhfvaozwOvYNiAc/EO4APAhW1Hugp45pj1mMlHgH9IchlwGXdfoYz6KvDyNs7lDB8I6+PMJNOX2BcyfP/2jxkeAgnwoaq6ZfryvKq+0W4pfjnJum7bzeSLwOMZ9oNi+B7gx+tZ99H63xd4U1Vdv8bwTwH/t90aPQf4HkBVXdLORv+5TX8+w3112vDPtgN4RZKDquq2edRxY3A48DdJftD6v9XKYNjudyb5LsO9/t94IAOgqm6c0PE3m3Udn9cD703ya+BXrWy0jt9Ncj7Ddr6G8U9y16odAx9j+P7wxwxXw9NeDPx9kqNanQ5l+E5sbbZiHcdXs5zhltjNDCdHu89St/OSnMRwTN2wRt1eBHwkyVsZjpETGeMz2NfgSJK62ZRur0mSNnGGjiSpG0NHktSNoSNJ6sbQkSR1Y+hIkroxdCRJ3fx/HZhw3wOcplcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.groupby('Bachelors')['White'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Degree\n",
       "0     AxesSubplot(0.125,0.125;0.775x0.755)\n",
       "1     AxesSubplot(0.125,0.125;0.775x0.755)\n",
       "2     AxesSubplot(0.125,0.125;0.775x0.755)\n",
       "3     AxesSubplot(0.125,0.125;0.775x0.755)\n",
       "4     AxesSubplot(0.125,0.125;0.775x0.755)\n",
       "5     AxesSubplot(0.125,0.125;0.775x0.755)\n",
       "6     AxesSubplot(0.125,0.125;0.775x0.755)\n",
       "7     AxesSubplot(0.125,0.125;0.775x0.755)\n",
       "8     AxesSubplot(0.125,0.125;0.775x0.755)\n",
       "9     AxesSubplot(0.125,0.125;0.775x0.755)\n",
       "10    AxesSubplot(0.125,0.125;0.775x0.755)\n",
       "11    AxesSubplot(0.125,0.125;0.775x0.755)\n",
       "12    AxesSubplot(0.125,0.125;0.775x0.755)\n",
       "13    AxesSubplot(0.125,0.125;0.775x0.755)\n",
       "14    AxesSubplot(0.125,0.125;0.775x0.755)\n",
       "15    AxesSubplot(0.125,0.125;0.775x0.755)\n",
       "Name: White, dtype: object"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.groupby('Degree')['White'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = split_data(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = X_test['White'].map(lambda x: 1 if x == ' White' else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.17      0.28      7455\n",
      "           1       0.25      0.92      0.40      2313\n",
      "\n",
      "    accuracy                           0.34      9768\n",
      "   macro avg       0.56      0.54      0.34      9768\n",
      "weighted avg       0.72      0.34      0.31      9768\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "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": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}