{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TU257-Lab6-Demo-3-KFold Cross Validation\n",
    "\n",
    "KFold Cross Validation is another way of testing the model and trys to avoid the issues associated with unfortunate splits.\n",
    "\n",
    "Same demo data set again, so scroll down to the testing part\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "from sklearn import datasets\n",
    "from sklearn.tree import DecisionTreeClassifier \n",
    "from sklearn import tree\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>WorkClass</th>\n",
       "      <th>Fnlwgt</th>\n",
       "      <th>Education</th>\n",
       "      <th>Edu_Num</th>\n",
       "      <th>MaritalStatus</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Relationship</th>\n",
       "      <th>Race</th>\n",
       "      <th>Sex</th>\n",
       "      <th>CapitalGain</th>\n",
       "      <th>CapitalLoss</th>\n",
       "      <th>HrPerWk</th>\n",
       "      <th>Native</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</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",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>49</td>\n",
       "      <td>Private</td>\n",
       "      <td>160187</td>\n",
       "      <td>9th</td>\n",
       "      <td>5</td>\n",
       "      <td>Married-spouse-absent</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>Jamaica</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>52</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>209642</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</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>45</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>31</td>\n",
       "      <td>Private</td>\n",
       "      <td>45781</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</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>14084</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>42</td>\n",
       "      <td>Private</td>\n",
       "      <td>159449</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>5178</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": [
       "   Age          WorkClass  Fnlwgt   Education  Edu_Num  \\\n",
       "0   39          State-gov   77516   Bachelors       13   \n",
       "1   50   Self-emp-not-inc   83311   Bachelors       13   \n",
       "2   38            Private  215646     HS-grad        9   \n",
       "3   53            Private  234721        11th        7   \n",
       "4   28            Private  338409   Bachelors       13   \n",
       "5   37            Private  284582     Masters       14   \n",
       "6   49            Private  160187         9th        5   \n",
       "7   52   Self-emp-not-inc  209642     HS-grad        9   \n",
       "8   31            Private   45781     Masters       14   \n",
       "9   42            Private  159449   Bachelors       13   \n",
       "\n",
       "            MaritalStatus          Occupation    Relationship    Race  \\\n",
       "0           Never-married        Adm-clerical   Not-in-family   White   \n",
       "1      Married-civ-spouse     Exec-managerial         Husband   White   \n",
       "2                Divorced   Handlers-cleaners   Not-in-family   White   \n",
       "3      Married-civ-spouse   Handlers-cleaners         Husband   Black   \n",
       "4      Married-civ-spouse      Prof-specialty            Wife   Black   \n",
       "5      Married-civ-spouse     Exec-managerial            Wife   White   \n",
       "6   Married-spouse-absent       Other-service   Not-in-family   Black   \n",
       "7      Married-civ-spouse     Exec-managerial         Husband   White   \n",
       "8           Never-married      Prof-specialty   Not-in-family   White   \n",
       "9      Married-civ-spouse     Exec-managerial         Husband   White   \n",
       "\n",
       "       Sex  CapitalGain  CapitalLoss  HrPerWk          Native  Target  \n",
       "0     Male         2174            0       40   United-States   <=50K  \n",
       "1     Male            0            0       13   United-States   <=50K  \n",
       "2     Male            0            0       40   United-States   <=50K  \n",
       "3     Male            0            0       40   United-States   <=50K  \n",
       "4   Female            0            0       40            Cuba   <=50K  \n",
       "5   Female            0            0       40   United-States   <=50K  \n",
       "6   Female            0            0       16         Jamaica   <=50K  \n",
       "7     Male            0            0       45   United-States    >50K  \n",
       "8   Female        14084            0       50   United-States    >50K  \n",
       "9     Male         5178            0       40   United-States    >50K  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "#Load in the dataset\n",
    "colnames=['Age', 'WorkClass', 'Fnlwgt', 'Education', 'Edu_Num', 'MaritalStatus', 'Occupation', 'Relationship', 'Race', 'Sex', 'CapitalGain', 'CapitalLoss', 'HrPerWk', 'Native', 'Target'] \n",
    "df = pd.read_csv('/Users/brendan.tierney/Dropbox/4-Datasets/adult.csv', names=colnames, header=None)\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows     :  32561\n",
      "Columns  :  15\n",
      "\n",
      "Features : \n",
      " ['Age', 'WorkClass', 'Fnlwgt', 'Education', 'Edu_Num', 'MaritalStatus', 'Occupation', 'Relationship', 'Race', 'Sex', 'CapitalGain', 'CapitalLoss', 'HrPerWk', 'Native', 'Target']\n",
      "\n",
      "Missing values :   0\n",
      "\n",
      "Unique values :  \n",
      " Age                 73\n",
      "WorkClass            9\n",
      "Fnlwgt           21648\n",
      "Education           16\n",
      "Edu_Num             16\n",
      "MaritalStatus        7\n",
      "Occupation          15\n",
      "Relationship         6\n",
      "Race                 5\n",
      "Sex                  2\n",
      "CapitalGain        119\n",
      "CapitalLoss         92\n",
      "HrPerWk             94\n",
      "Native              42\n",
      "Target               2\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print (\"Rows     : \" ,df.shape[0])\n",
    "print (\"Columns  : \" ,df.shape[1])\n",
    "print (\"\\nFeatures : \\n\" ,df.columns.tolist())\n",
    "print (\"\\nMissing values :  \", df.isnull().sum().values.sum())\n",
    "print (\"\\nUnique values :  \\n\",df.nunique())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 32561 entries, 0 to 32560\n",
      "Data columns (total 15 columns):\n",
      " #   Column         Non-Null Count  Dtype \n",
      "---  ------         --------------  ----- \n",
      " 0   Age            32561 non-null  int64 \n",
      " 1   WorkClass      32561 non-null  object\n",
      " 2   Fnlwgt         32561 non-null  int64 \n",
      " 3   Education      32561 non-null  object\n",
      " 4   Edu_Num        32561 non-null  int64 \n",
      " 5   MaritalStatus  32561 non-null  object\n",
      " 6   Occupation     32561 non-null  object\n",
      " 7   Relationship   32561 non-null  object\n",
      " 8   Race           32561 non-null  object\n",
      " 9   Sex            32561 non-null  object\n",
      " 10  CapitalGain    32561 non-null  int64 \n",
      " 11  CapitalLoss    32561 non-null  int64 \n",
      " 12  HrPerWk        32561 non-null  int64 \n",
      " 13  Native         32561 non-null  object\n",
      " 14  Target         32561 non-null  object\n",
      "dtypes: int64(6), object(9)\n",
      "memory usage: 3.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Fnlwgt</th>\n",
       "      <th>Edu_Num</th>\n",
       "      <th>CapitalGain</th>\n",
       "      <th>CapitalLoss</th>\n",
       "      <th>HrPerWk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>32561.000000</td>\n",
       "      <td>3.256100e+04</td>\n",
       "      <td>32561.000000</td>\n",
       "      <td>32561.000000</td>\n",
       "      <td>32561.000000</td>\n",
       "      <td>32561.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>38.581647</td>\n",
       "      <td>1.897784e+05</td>\n",
       "      <td>10.080679</td>\n",
       "      <td>1077.648844</td>\n",
       "      <td>87.303830</td>\n",
       "      <td>40.437456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13.640433</td>\n",
       "      <td>1.055500e+05</td>\n",
       "      <td>2.572720</td>\n",
       "      <td>7385.292085</td>\n",
       "      <td>402.960219</td>\n",
       "      <td>12.347429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>1.228500e+04</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>1.178270e+05</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>37.000000</td>\n",
       "      <td>1.783560e+05</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>48.000000</td>\n",
       "      <td>2.370510e+05</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>45.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>90.000000</td>\n",
       "      <td>1.484705e+06</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>99999.000000</td>\n",
       "      <td>4356.000000</td>\n",
       "      <td>99.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Age        Fnlwgt       Edu_Num   CapitalGain   CapitalLoss  \\\n",
       "count  32561.000000  3.256100e+04  32561.000000  32561.000000  32561.000000   \n",
       "mean      38.581647  1.897784e+05     10.080679   1077.648844     87.303830   \n",
       "std       13.640433  1.055500e+05      2.572720   7385.292085    402.960219   \n",
       "min       17.000000  1.228500e+04      1.000000      0.000000      0.000000   \n",
       "25%       28.000000  1.178270e+05      9.000000      0.000000      0.000000   \n",
       "50%       37.000000  1.783560e+05     10.000000      0.000000      0.000000   \n",
       "75%       48.000000  2.370510e+05     12.000000      0.000000      0.000000   \n",
       "max       90.000000  1.484705e+06     16.000000  99999.000000   4356.000000   \n",
       "\n",
       "            HrPerWk  \n",
       "count  32561.000000  \n",
       "mean      40.437456  \n",
       "std       12.347429  \n",
       "min        1.000000  \n",
       "25%       40.000000  \n",
       "50%       40.000000  \n",
       "75%       45.000000  \n",
       "max       99.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Numerical feature of summary/description \n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age              0\n",
       "WorkClass        0\n",
       "Fnlwgt           0\n",
       "Education        0\n",
       "Edu_Num          0\n",
       "MaritalStatus    0\n",
       "Occupation       0\n",
       "Relationship     0\n",
       "Race             0\n",
       "Sex              0\n",
       "CapitalGain      0\n",
       "CapitalLoss      0\n",
       "HrPerWk          0\n",
       "Native           0\n",
       "Target           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# checking \"?\" values, how many are there in the whole dataset\n",
    "df_missing = (df=='?').sum()\n",
    "df_missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>WorkClass</th>\n",
       "      <th>Education</th>\n",
       "      <th>MaritalStatus</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Relationship</th>\n",
       "      <th>Race</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Native</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>State-gov</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>Bachelors</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>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</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>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Private</td>\n",
       "      <td>11th</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>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</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>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           WorkClass   Education        MaritalStatus          Occupation  \\\n",
       "0          State-gov   Bachelors        Never-married        Adm-clerical   \n",
       "1   Self-emp-not-inc   Bachelors   Married-civ-spouse     Exec-managerial   \n",
       "2            Private     HS-grad             Divorced   Handlers-cleaners   \n",
       "3            Private        11th   Married-civ-spouse   Handlers-cleaners   \n",
       "4            Private   Bachelors   Married-civ-spouse      Prof-specialty   \n",
       "\n",
       "     Relationship    Race      Sex          Native  Target  \n",
       "0   Not-in-family   White     Male   United-States   <=50K  \n",
       "1         Husband   White     Male   United-States   <=50K  \n",
       "2   Not-in-family   White     Male   United-States   <=50K  \n",
       "3         Husband   Black     Male   United-States   <=50K  \n",
       "4            Wife   Black   Female            Cuba   <=50K  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import preprocessing\n",
    "\n",
    "# encode categorical variables using label Encoder\n",
    "\n",
    "# select all categorical variables\n",
    "df_categorical = df.select_dtypes(include=['object'])\n",
    "df_categorical.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>WorkClass</th>\n",
       "      <th>Education</th>\n",
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       "      <th>Relationship</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
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       "      <th>2</th>\n",
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       "      <td>11</td>\n",
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       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
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       "      <td>6</td>\n",
       "      <td>0</td>\n",
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       "      <td>39</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   WorkClass  Education  MaritalStatus  Occupation  Relationship  Race  Sex  \\\n",
       "0          7          9              4           1             1     4    1   \n",
       "1          6          9              2           4             0     4    1   \n",
       "2          4         11              0           6             1     4    1   \n",
       "3          4          1              2           6             0     2    1   \n",
       "4          4          9              2          10             5     2    0   \n",
       "\n",
       "   Native  Target  \n",
       "0      39       0  \n",
       "1      39       0  \n",
       "2      39       0  \n",
       "3      39       0  \n",
       "4       5       0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# apply label encoder to df_categorical\n",
    "le = preprocessing.LabelEncoder()\n",
    "df_categorical = df_categorical.apply(le.fit_transform)\n",
    "df_categorical.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Fnlwgt</th>\n",
       "      <th>Edu_Num</th>\n",
       "      <th>CapitalGain</th>\n",
       "      <th>CapitalLoss</th>\n",
       "      <th>HrPerWk</th>\n",
       "      <th>WorkClass</th>\n",
       "      <th>Education</th>\n",
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       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>77516</td>\n",
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       "      <td>2174</td>\n",
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       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>83311</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
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       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>215646</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>234721</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
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       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>338409</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Fnlwgt  Edu_Num  CapitalGain  CapitalLoss  HrPerWk  WorkClass  \\\n",
       "0   39   77516       13         2174            0       40          7   \n",
       "1   50   83311       13            0            0       13          6   \n",
       "2   38  215646        9            0            0       40          4   \n",
       "3   53  234721        7            0            0       40          4   \n",
       "4   28  338409       13            0            0       40          4   \n",
       "\n",
       "   Education  MaritalStatus  Occupation  Relationship  Race  Sex  Native  \\\n",
       "0          9              4           1             1     4    1      39   \n",
       "1          9              2           4             0     4    1      39   \n",
       "2         11              0           6             1     4    1      39   \n",
       "3          1              2           6             0     2    1      39   \n",
       "4          9              2          10             5     2    0       5   \n",
       "\n",
       "   Target  \n",
       "0       0  \n",
       "1       0  \n",
       "2       0  \n",
       "3       0  \n",
       "4       0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Next, Concatenate df_categorical dataframe with original df (dataframe)\n",
    "\n",
    "# first, Drop earlier duplicate columns which had categorical values\n",
    "df = df.drop(df_categorical.columns,axis=1)\n",
    "df = pd.concat([df,df_categorical],axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>-0.266288</td>\n",
       "      <td>-0.020947</td>\n",
       "      <td>-0.263698</td>\n",
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       "      <td>0.088832</td>\n",
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       "    <tr>\n",
       "      <th>Fnlwgt</th>\n",
       "      <td>-0.076646</td>\n",
       "      <td>1.000000</td>\n",
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       "      <td>-0.010252</td>\n",
       "      <td>-0.018768</td>\n",
       "      <td>-0.016656</td>\n",
       "      <td>-0.028145</td>\n",
       "      <td>0.028153</td>\n",
       "      <td>0.001597</td>\n",
       "      <td>0.008931</td>\n",
       "      <td>-0.021291</td>\n",
       "      <td>0.026858</td>\n",
       "      <td>-0.051966</td>\n",
       "      <td>-0.009463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Edu_Num</th>\n",
       "      <td>0.036527</td>\n",
       "      <td>-0.043195</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.122630</td>\n",
       "      <td>0.079923</td>\n",
       "      <td>0.148123</td>\n",
       "      <td>0.052085</td>\n",
       "      <td>0.359153</td>\n",
       "      <td>-0.069304</td>\n",
       "      <td>0.109697</td>\n",
       "      <td>-0.094153</td>\n",
       "      <td>0.031838</td>\n",
       "      <td>0.012280</td>\n",
       "      <td>0.050840</td>\n",
       "      <td>0.335154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CapitalGain</th>\n",
       "      <td>0.077674</td>\n",
       "      <td>0.000432</td>\n",
       "      <td>0.122630</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.031615</td>\n",
       "      <td>0.078409</td>\n",
       "      <td>0.033835</td>\n",
       "      <td>0.030046</td>\n",
       "      <td>-0.043393</td>\n",
       "      <td>0.025505</td>\n",
       "      <td>-0.057919</td>\n",
       "      <td>0.011145</td>\n",
       "      <td>0.048480</td>\n",
       "      <td>-0.001982</td>\n",
       "      <td>0.223329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CapitalLoss</th>\n",
       "      <td>0.057775</td>\n",
       "      <td>-0.010252</td>\n",
       "      <td>0.079923</td>\n",
       "      <td>-0.031615</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.054256</td>\n",
       "      <td>0.012216</td>\n",
       "      <td>0.016746</td>\n",
       "      <td>-0.034187</td>\n",
       "      <td>0.017987</td>\n",
       "      <td>-0.061062</td>\n",
       "      <td>0.018899</td>\n",
       "      <td>0.045567</td>\n",
       "      <td>0.000419</td>\n",
       "      <td>0.150526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HrPerWk</th>\n",
       "      <td>0.068756</td>\n",
       "      <td>-0.018768</td>\n",
       "      <td>0.148123</td>\n",
       "      <td>0.078409</td>\n",
       "      <td>0.054256</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.138962</td>\n",
       "      <td>0.055510</td>\n",
       "      <td>-0.190519</td>\n",
       "      <td>0.080383</td>\n",
       "      <td>-0.248974</td>\n",
       "      <td>0.041910</td>\n",
       "      <td>0.229309</td>\n",
       "      <td>-0.002671</td>\n",
       "      <td>0.229689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WorkClass</th>\n",
       "      <td>0.003787</td>\n",
       "      <td>-0.016656</td>\n",
       "      <td>0.052085</td>\n",
       "      <td>0.033835</td>\n",
       "      <td>0.012216</td>\n",
       "      <td>0.138962</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.023513</td>\n",
       "      <td>-0.064731</td>\n",
       "      <td>0.254892</td>\n",
       "      <td>-0.090461</td>\n",
       "      <td>0.049742</td>\n",
       "      <td>0.095981</td>\n",
       "      <td>-0.007690</td>\n",
       "      <td>0.051604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Education</th>\n",
       "      <td>-0.010508</td>\n",
       "      <td>-0.028145</td>\n",
       "      <td>0.359153</td>\n",
       "      <td>0.030046</td>\n",
       "      <td>0.016746</td>\n",
       "      <td>0.055510</td>\n",
       "      <td>0.023513</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.038407</td>\n",
       "      <td>-0.021260</td>\n",
       "      <td>-0.010876</td>\n",
       "      <td>0.014131</td>\n",
       "      <td>-0.027356</td>\n",
       "      <td>0.064288</td>\n",
       "      <td>0.079317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MaritalStatus</th>\n",
       "      <td>-0.266288</td>\n",
       "      <td>0.028153</td>\n",
       "      <td>-0.069304</td>\n",
       "      <td>-0.043393</td>\n",
       "      <td>-0.034187</td>\n",
       "      <td>-0.190519</td>\n",
       "      <td>-0.064731</td>\n",
       "      <td>-0.038407</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.009654</td>\n",
       "      <td>0.185451</td>\n",
       "      <td>-0.068013</td>\n",
       "      <td>-0.129314</td>\n",
       "      <td>-0.023819</td>\n",
       "      <td>-0.199307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Occupation</th>\n",
       "      <td>-0.020947</td>\n",
       "      <td>0.001597</td>\n",
       "      <td>0.109697</td>\n",
       "      <td>0.025505</td>\n",
       "      <td>0.017987</td>\n",
       "      <td>0.080383</td>\n",
       "      <td>0.254892</td>\n",
       "      <td>-0.021260</td>\n",
       "      <td>-0.009654</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.075607</td>\n",
       "      <td>0.006763</td>\n",
       "      <td>0.080296</td>\n",
       "      <td>-0.012543</td>\n",
       "      <td>0.075468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Relationship</th>\n",
       "      <td>-0.263698</td>\n",
       "      <td>0.008931</td>\n",
       "      <td>-0.094153</td>\n",
       "      <td>-0.057919</td>\n",
       "      <td>-0.061062</td>\n",
       "      <td>-0.248974</td>\n",
       "      <td>-0.090461</td>\n",
       "      <td>-0.010876</td>\n",
       "      <td>0.185451</td>\n",
       "      <td>-0.075607</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.116055</td>\n",
       "      <td>-0.582454</td>\n",
       "      <td>-0.005507</td>\n",
       "      <td>-0.250918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Race</th>\n",
       "      <td>0.028718</td>\n",
       "      <td>-0.021291</td>\n",
       "      <td>0.031838</td>\n",
       "      <td>0.011145</td>\n",
       "      <td>0.018899</td>\n",
       "      <td>0.041910</td>\n",
       "      <td>0.049742</td>\n",
       "      <td>0.014131</td>\n",
       "      <td>-0.068013</td>\n",
       "      <td>0.006763</td>\n",
       "      <td>-0.116055</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.087204</td>\n",
       "      <td>0.137852</td>\n",
       "      <td>0.071846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <td>0.088832</td>\n",
       "      <td>0.026858</td>\n",
       "      <td>0.012280</td>\n",
       "      <td>0.048480</td>\n",
       "      <td>0.045567</td>\n",
       "      <td>0.229309</td>\n",
       "      <td>0.095981</td>\n",
       "      <td>-0.027356</td>\n",
       "      <td>-0.129314</td>\n",
       "      <td>0.080296</td>\n",
       "      <td>-0.582454</td>\n",
       "      <td>0.087204</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.008119</td>\n",
       "      <td>0.215980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Native</th>\n",
       "      <td>-0.001151</td>\n",
       "      <td>-0.051966</td>\n",
       "      <td>0.050840</td>\n",
       "      <td>-0.001982</td>\n",
       "      <td>0.000419</td>\n",
       "      <td>-0.002671</td>\n",
       "      <td>-0.007690</td>\n",
       "      <td>0.064288</td>\n",
       "      <td>-0.023819</td>\n",
       "      <td>-0.012543</td>\n",
       "      <td>-0.005507</td>\n",
       "      <td>0.137852</td>\n",
       "      <td>-0.008119</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.015840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Target</th>\n",
       "      <td>0.234037</td>\n",
       "      <td>-0.009463</td>\n",
       "      <td>0.335154</td>\n",
       "      <td>0.223329</td>\n",
       "      <td>0.150526</td>\n",
       "      <td>0.229689</td>\n",
       "      <td>0.051604</td>\n",
       "      <td>0.079317</td>\n",
       "      <td>-0.199307</td>\n",
       "      <td>0.075468</td>\n",
       "      <td>-0.250918</td>\n",
       "      <td>0.071846</td>\n",
       "      <td>0.215980</td>\n",
       "      <td>0.015840</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Age    Fnlwgt   Edu_Num  CapitalGain  CapitalLoss  \\\n",
       "Age            1.000000 -0.076646  0.036527     0.077674     0.057775   \n",
       "Fnlwgt        -0.076646  1.000000 -0.043195     0.000432    -0.010252   \n",
       "Edu_Num        0.036527 -0.043195  1.000000     0.122630     0.079923   \n",
       "CapitalGain    0.077674  0.000432  0.122630     1.000000    -0.031615   \n",
       "CapitalLoss    0.057775 -0.010252  0.079923    -0.031615     1.000000   \n",
       "HrPerWk        0.068756 -0.018768  0.148123     0.078409     0.054256   \n",
       "WorkClass      0.003787 -0.016656  0.052085     0.033835     0.012216   \n",
       "Education     -0.010508 -0.028145  0.359153     0.030046     0.016746   \n",
       "MaritalStatus -0.266288  0.028153 -0.069304    -0.043393    -0.034187   \n",
       "Occupation    -0.020947  0.001597  0.109697     0.025505     0.017987   \n",
       "Relationship  -0.263698  0.008931 -0.094153    -0.057919    -0.061062   \n",
       "Race           0.028718 -0.021291  0.031838     0.011145     0.018899   \n",
       "Sex            0.088832  0.026858  0.012280     0.048480     0.045567   \n",
       "Native        -0.001151 -0.051966  0.050840    -0.001982     0.000419   \n",
       "Target         0.234037 -0.009463  0.335154     0.223329     0.150526   \n",
       "\n",
       "                HrPerWk  WorkClass  Education  MaritalStatus  Occupation  \\\n",
       "Age            0.068756   0.003787  -0.010508      -0.266288   -0.020947   \n",
       "Fnlwgt        -0.018768  -0.016656  -0.028145       0.028153    0.001597   \n",
       "Edu_Num        0.148123   0.052085   0.359153      -0.069304    0.109697   \n",
       "CapitalGain    0.078409   0.033835   0.030046      -0.043393    0.025505   \n",
       "CapitalLoss    0.054256   0.012216   0.016746      -0.034187    0.017987   \n",
       "HrPerWk        1.000000   0.138962   0.055510      -0.190519    0.080383   \n",
       "WorkClass      0.138962   1.000000   0.023513      -0.064731    0.254892   \n",
       "Education      0.055510   0.023513   1.000000      -0.038407   -0.021260   \n",
       "MaritalStatus -0.190519  -0.064731  -0.038407       1.000000   -0.009654   \n",
       "Occupation     0.080383   0.254892  -0.021260      -0.009654    1.000000   \n",
       "Relationship  -0.248974  -0.090461  -0.010876       0.185451   -0.075607   \n",
       "Race           0.041910   0.049742   0.014131      -0.068013    0.006763   \n",
       "Sex            0.229309   0.095981  -0.027356      -0.129314    0.080296   \n",
       "Native        -0.002671  -0.007690   0.064288      -0.023819   -0.012543   \n",
       "Target         0.229689   0.051604   0.079317      -0.199307    0.075468   \n",
       "\n",
       "               Relationship      Race       Sex    Native    Target  \n",
       "Age               -0.263698  0.028718  0.088832 -0.001151  0.234037  \n",
       "Fnlwgt             0.008931 -0.021291  0.026858 -0.051966 -0.009463  \n",
       "Edu_Num           -0.094153  0.031838  0.012280  0.050840  0.335154  \n",
       "CapitalGain       -0.057919  0.011145  0.048480 -0.001982  0.223329  \n",
       "CapitalLoss       -0.061062  0.018899  0.045567  0.000419  0.150526  \n",
       "HrPerWk           -0.248974  0.041910  0.229309 -0.002671  0.229689  \n",
       "WorkClass         -0.090461  0.049742  0.095981 -0.007690  0.051604  \n",
       "Education         -0.010876  0.014131 -0.027356  0.064288  0.079317  \n",
       "MaritalStatus      0.185451 -0.068013 -0.129314 -0.023819 -0.199307  \n",
       "Occupation        -0.075607  0.006763  0.080296 -0.012543  0.075468  \n",
       "Relationship       1.000000 -0.116055 -0.582454 -0.005507 -0.250918  \n",
       "Race              -0.116055  1.000000  0.087204  0.137852  0.071846  \n",
       "Sex               -0.582454  0.087204  1.000000 -0.008119  0.215980  \n",
       "Native            -0.005507  0.137852 -0.008119  1.000000  0.015840  \n",
       "Target            -0.250918  0.071846  0.215980  0.015840  1.000000  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix=df.corr()\n",
    "corr_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x1008 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sn\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig = plt.subplots(figsize=(17,14))\n",
    "sn.heatmap(corr_matrix, annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    24720\n",
       "1     7841\n",
       "Name: Target, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Target'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Putting independent variables/features to X\n",
    "X = df.drop('Target',axis=1)\n",
    "\n",
    "# Putting response/dependent variable/feature to y\n",
    "y = df['Target']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting the data into train and test\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.30,random_state=99)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "################\n",
    "#  KNN Model\n",
    "################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=3)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importing library\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "#set k=3\n",
    "knn_model = KNeighborsClassifier(n_neighbors=3)\n",
    "#fit the model\n",
    "knn_model.fit(X_train,y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.88      0.85      7436\n",
      "           1       0.50      0.38      0.43      2333\n",
      "\n",
      "    accuracy                           0.76      9769\n",
      "   macro avg       0.66      0.63      0.64      9769\n",
      "weighted avg       0.74      0.76      0.75      9769\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Importing classification report and confusion matrix from sklearn metrics\n",
    "from sklearn.metrics import classification_report,confusion_matrix,accuracy_score\n",
    "\n",
    "# making predictions\n",
    "y_pred_default = knn_model.predict(X_test)\n",
    "\n",
    "# Printing classifier report after prediction\n",
    "print(classification_report(y_test,y_pred_default))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[6529  907]\n",
      " [1438  895]]\n",
      "0.759954959565974\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,y_pred_default))\n",
    "print(accuracy_score(y_test,y_pred_default))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted     0    1   All\n",
      "Actual                    \n",
      "0          1854  402  2256\n",
      "1           563  147   710\n",
      "All        2417  549  2966\n"
     ]
    }
   ],
   "source": [
    "print(pd.crosstab(pd.Series(y_test), pd.Series(y_pred_default), rownames=['Actual'], colnames=['Predicted'], margins=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC =  0.6706631839590393\n",
      "tpr =  [0.         0.20145735 0.38362623 0.69095585 1.        ]\n",
      "fpt =  [0.         0.01721356 0.12197418 0.45965573 1.        ]\n"
     ]
    }
   ],
   "source": [
    "#Let's print the ROC chart\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "\n",
    "# calculate roc curve\n",
    "Y_predict_prob = knn_model.predict_proba(X_test)[:,1]\n",
    "fpr, tpr, thresholds = roc_curve(y_test, Y_predict_prob)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "print('AUC = ', roc_auc)\n",
    "print('tpr = ', tpr)\n",
    "print('fpt = ', fpr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x1008 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.subplots(figsize=(17,14))\n",
    "plt.plot(fpr, tpr, label='%s ROC (area = %0.2f)' % ('KNN', roc_auc))\n",
    "\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.0])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC Curve')\n",
    "plt.legend(loc=0, fontsize='small')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "###########################\n",
    "# KFold Cross Validation\n",
    "###########################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "###########################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.754 (0.006)\n"
     ]
    }
   ],
   "source": [
    "#load scikit-learn k-fold cross-validation\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from numpy import mean\n",
    "from numpy import std\n",
    "\n",
    "#n_splits = number of k-folds\n",
    "#shuffle = shuffles data set prior to split\n",
    "#radnom_state = seed for (pseydo)random number generator\n",
    "cv = KFold(n_splits=10, shuffle=True, random_state=42)\n",
    "\n",
    "#create model, perform cross validation and evaluate model\n",
    "scores = cross_val_score(knn_model, X_train, y_train, scoring='accuracy', cv=cv, n_jobs=-1)\n",
    "#performance result\n",
    "print('Accuracy: %.3f (%.3f)' % (mean(scores), std(scores)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Excerise: Investigate if changing the Number of Splits gives a very different outcome. \n",
    "#Change the number and investigate?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Exercise: Repeat the KFold Cross Validation for the SVM and NNet models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Excercise: Can you also create the ROC chart for the other algorithms/model you created last week"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Exercise: Compare the ROC charts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.9"
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 },
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