{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container { width:80% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import display, HTML\n",
    "display(HTML(\"<style>.container { width:80% !important; }</style>\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TU257 - Machine Learning - Grid Search"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Run this notebook and examine what is produced for each cell.\n",
    "\n",
    "#### Add your own comments to enrich the information given to make it more meaningful to you.\n",
    "\n",
    "#### In this notebook we will have a look at how to run a GridSearch\n",
    "\n",
    "#### This notebook is an extention of the previous examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": 2,
     "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": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Fnlwgt</th>\n",
       "      <th>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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Numerical feature of summary/description \n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking \"?\" values, how many are there in the whole dataset\n",
    "df_missing = (df=='?').sum()\n",
    "df_missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " <=50K    0.75919\n",
       " >50K     0.24081\n",
       "Name: Target, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Target'].value_counts()/len(df)   #calculate percentages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>WorkClass</th>\n",
       "      <th>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": 9,
     "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": 10,
   "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",
       "      <th>MaritalStatus</th>\n",
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       "      <th>Relationship</th>\n",
       "      <th>Race</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <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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       "    <tr>\n",
       "      <th>2</th>\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>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <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": 10,
     "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": 11,
   "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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       "      <td>40</td>\n",
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       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>83311</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>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": 11,
     "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": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>Fnlwgt</th>\n",
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       "      <td>-0.266288</td>\n",
       "      <td>-0.020947</td>\n",
       "      <td>-0.263698</td>\n",
       "      <td>0.028718</td>\n",
       "      <td>0.088832</td>\n",
       "      <td>-0.001151</td>\n",
       "      <td>0.234037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fnlwgt</th>\n",
       "      <td>-0.076646</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.043195</td>\n",
       "      <td>0.000432</td>\n",
       "      <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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix=df.corr()\n",
    "corr_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1700x1400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "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": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    24720\n",
       "1     7841\n",
       "Name: Target, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Target'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22792\n",
      "22792\n",
      "9769\n",
      "9769\n",
      "0.6999785018887626\n",
      "0.30002149811123735\n"
     ]
    }
   ],
   "source": [
    "y_train.dtypes\n",
    "print(len(X_train))\n",
    "print(len(y_train))\n",
    "print(len(X_test))\n",
    "print(len(y_test))\n",
    "print(len(X_train)/(len(X_train)+len(X_test)))\n",
    "print(len(X_test)/(len(X_train)+len(X_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Grid Search\n",
    "### for RandomForest()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV, RandomizedSearchCV\n",
    "\n",
    "#The following creates a RandomForest using default parameters with max_depth=5\n",
    "#rf_default = RandomForestClassifier(max_depth=5)\n",
    "#rf_default.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Challenge: What are the different parameters for RandomForest?\n",
    "# Open Google and search for the RandomForest documentation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = {\n",
    "    'n_estimators': [25, 50, 100, 150],\n",
    "    'max_features': ['sqrt', 'log2', None],\n",
    "#    'max_depth': [3, 6, 9],\n",
    "#    'max_leaf_nodes': [3, 6, 9],\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestClassifier(max_features='log2', n_estimators=50)\n"
     ]
    }
   ],
   "source": [
    "#RandomizedSearchCV will select a Random selection of values for each parameter.\n",
    "# This might not be suitable as it might miss important values\n",
    "\n",
    "## WARNING: This will take many minutes to run\n",
    "\n",
    "random_search = RandomizedSearchCV(RandomForestClassifier(),\n",
    "                                   param_grid)\n",
    "\n",
    "random_search.fit(X_train, y_train)\n",
    "print(random_search.best_estimator_)\n",
    "\n",
    "#Check out this webpage for more RandomizedSearchCV details\n",
    "# https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.RandomizedSearchCV.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best random search hyperparameters are: {'n_estimators': 50, 'max_features': 'log2'}\n",
      "Best random search score is: 0.854729887821523\n",
      "Best estimator is: RandomForestClassifier(max_features='log2', n_estimators=50)\n"
     ]
    }
   ],
   "source": [
    "# random random search results\n",
    "print('Best random search hyperparameters are: '+str(random_search.best_params_))\n",
    "print('Best random search score is: '+str(random_search.best_score_))\n",
    "print('Best estimator is: '+str(random_search.best_estimator_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now let's look at a Grid Search, where we can specify a range of values to try"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=10, estimator=RandomForestClassifier(),\n",
       "             param_grid=[{&#x27;max_depth&#x27;: [2, 3, 4, 5],\n",
       "                          &#x27;max_features&#x27;: [3, 4, 5, 6, 7]}],\n",
       "             scoring=&#x27;accuracy&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=10, estimator=RandomForestClassifier(),\n",
       "             param_grid=[{&#x27;max_depth&#x27;: [2, 3, 4, 5],\n",
       "                          &#x27;max_features&#x27;: [3, 4, 5, 6, 7]}],\n",
       "             scoring=&#x27;accuracy&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=10, estimator=RandomForestClassifier(),\n",
       "             param_grid=[{'max_depth': [2, 3, 4, 5],\n",
       "                          'max_features': [3, 4, 5, 6, 7]}],\n",
       "             scoring='accuracy')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc = RandomForestClassifier()\n",
    "\n",
    "#GridSearch can take a lot of time!   We will only use these 2 parameters as an example\n",
    "forest_params = [{'max_depth': list(range(2, 6)), \n",
    "                  'max_features': list(range(3, 8))}]\n",
    "\n",
    "## WARNING: This will take many minutes to run\n",
    "\n",
    "grid_search = GridSearchCV(rfc, forest_params, cv = 10, scoring='accuracy')\n",
    "\n",
    "#this next command will take some time!\n",
    "grid_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best hyperparameters are: {'max_depth': 5, 'max_features': 7}\n",
      "Best score is: 0.8528872119966436\n",
      "Best estimator is: RandomForestClassifier(max_depth=5, max_features=7)\n"
     ]
    }
   ],
   "source": [
    "print('Best hyperparameters are: '+str(grid_search.best_params_))\n",
    "print('Best score is: '+str(grid_search.best_score_))\n",
    "print('Best estimator is: '+str(grid_search.best_estimator_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Question: Add one or two more parameters to the above examples, and add list/range of values\n",
    "#          Rerun to see if there is a difference in outcome\n",
    "#\n",
    "#          Take note of the timing for adding each parameter\n",
    "#\n",
    "# Copy the above code, paste it below, and run\n",
    "#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Question: Now try using a different Algorithm. One we have looked at previously"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost import XGBClassifier\n"
   ]
  }
 ],
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