real-estate.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Real-Estate-Client-Target"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The goal of this project is to help a real estate agency more effectively target potential clients by predicting which individuals earn more than $50K per year. Accurate income prediction will allow the agency to tailor its marketing strategies towards high-income clients, who are more likely to engage in high-value real estate transactions.\n",
    "\n",
    "To achieve this, the project will use the [UCI Adult Dataset](https://archive.ics.uci.edu/dataset/2/adult), which includes various demographic and employment related attributes such as age, education, occupation, and hours worked per week. These features will be used to train a machine learning model that can classify individuals as either earning above or below $50K annually."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Business Understanding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Selecting the proper clientele for their marketing campaigns is proving to be difficult for the real estate company. The agency can target its marketing techniques on high-earners who are more likely to engage in high-value transactions by properly forecasting which clients make above $50K annually. In order to increase marketing effectiveness and economic success, the project's goal is to develop a predictive model that would assist the agency in identifying possible clients with incomes over $50K."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Understanding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "UCI Adult Dataset.\n",
    "\n",
    "Predict whether income exceeds $50K/yr based on census data. Also known as \"Census Income\" dataset from UCI datasets. \n",
    "\n",
    "It comprises of:\n",
    "\n",
    "1. 14 features\n",
    "2. multivariate dataset  \n",
    "3. categorical and integer feature type"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Import Libraries to use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1972,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Perfom data manipulation and analysis.\n",
    "import pandas as pd\n",
    "\n",
    "# Perfoming mathematical calculations.\n",
    "import numpy as np\n",
    "\n",
    "# The two libraries below will aid in creating visualizations.\n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1973,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>39</th>\n",
       "      <th>State-gov</th>\n",
       "      <th>77516</th>\n",
       "      <th>Bachelors</th>\n",
       "      <th>13</th>\n",
       "      <th>Never-married</th>\n",
       "      <th>Adm-clerical</th>\n",
       "      <th>Not-in-family</th>\n",
       "      <th>White</th>\n",
       "      <th>Male</th>\n",
       "      <th>2174</th>\n",
       "      <th>0</th>\n",
       "      <th>40</th>\n",
       "      <th>United-States</th>\n",
       "      <th>&lt;=50K</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>37</td>\n",
       "      <td>Private</td>\n",
       "      <td>284582</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   39          State-gov   77516   Bachelors   13        Never-married  \\\n",
       "0  50   Self-emp-not-inc   83311   Bachelors   13   Married-civ-spouse   \n",
       "1  38            Private  215646     HS-grad    9             Divorced   \n",
       "2  53            Private  234721        11th    7   Married-civ-spouse   \n",
       "3  28            Private  338409   Bachelors   13   Married-civ-spouse   \n",
       "4  37            Private  284582     Masters   14   Married-civ-spouse   \n",
       "\n",
       "         Adm-clerical   Not-in-family   White     Male   2174   0   40  \\\n",
       "0     Exec-managerial         Husband   White     Male      0   0   13   \n",
       "1   Handlers-cleaners   Not-in-family   White     Male      0   0   40   \n",
       "2   Handlers-cleaners         Husband   Black     Male      0   0   40   \n",
       "3      Prof-specialty            Wife   Black   Female      0   0   40   \n",
       "4     Exec-managerial            Wife   White   Female      0   0   40   \n",
       "\n",
       "    United-States   <=50K  \n",
       "0   United-States   <=50K  \n",
       "1   United-States   <=50K  \n",
       "2   United-States   <=50K  \n",
       "3            Cuba   <=50K  \n",
       "4   United-States   <=50K  "
      ]
     },
     "execution_count": 1973,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"data/adult.csv\")\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observations\n",
    "\n",
    "The \"adult.csv\" data loaded above appears to have no column names. Therefore, I will have to come up with names that will be representing each column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1974,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a copy of my dataset.\n",
    "new_df = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1975,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Identify column names and assign them to a list\n",
    "columnNames = [\"age\",\"workclass\",\"fnlwgt\",\"education\",\"education_num\",\"marital_status\",\"occupation\",\"relationship\",\"race\",\"sex\",\"capital_gain\",\"capital_loss\",\n",
    "            \"hours_per_week\",\"native-country\",\"income\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1976,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education_num</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital_gain</th>\n",
       "      <th>capital_loss</th>\n",
       "      <th>hours_per_week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          workclass  fnlwgt   education  education_num  \\\n",
       "0   39          State-gov   77516   Bachelors             13   \n",
       "1   50   Self-emp-not-inc   83311   Bachelors             13   \n",
       "2   38            Private  215646     HS-grad              9   \n",
       "3   53            Private  234721        11th              7   \n",
       "4   28            Private  338409   Bachelors             13   \n",
       "\n",
       "        marital_status          occupation    relationship    race      sex  \\\n",
       "0        Never-married        Adm-clerical   Not-in-family   White     Male   \n",
       "1   Married-civ-spouse     Exec-managerial         Husband   White     Male   \n",
       "2             Divorced   Handlers-cleaners   Not-in-family   White     Male   \n",
       "3   Married-civ-spouse   Handlers-cleaners         Husband   Black     Male   \n",
       "4   Married-civ-spouse      Prof-specialty            Wife   Black   Female   \n",
       "\n",
       "   capital_gain  capital_loss  hours_per_week  native-country  income  \n",
       "0          2174             0              40   United-States   <=50K  \n",
       "1             0             0              13   United-States   <=50K  \n",
       "2             0             0              40   United-States   <=50K  \n",
       "3             0             0              40   United-States   <=50K  \n",
       "4             0             0              40            Cuba   <=50K  "
      ]
     },
     "execution_count": 1976,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# I introduced a parameter as I read my csv file in pandas that will help in naming my dataframe columns.\n",
    "new_df = pd.read_csv(\"data/adult.csv\", names=columnNames)\n",
    "new_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1977,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32561, 15)"
      ]
     },
     "execution_count": 1977,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the number of rows and columns in the dataset\n",
    "new_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Data-types used in my dataset**\n",
    "\n",
    "Model creation requires numerical data only. I will have to view the information of my dataset to use in this project."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1978,
   "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   education_num   32561 non-null  int64 \n",
      " 5   marital_status  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  capital_gain    32561 non-null  int64 \n",
      " 11  capital_loss    32561 non-null  int64 \n",
      " 12  hours_per_week  32561 non-null  int64 \n",
      " 13  native-country  32561 non-null  object\n",
      " 14  income          32561 non-null  object\n",
      "dtypes: int64(6), object(9)\n",
      "memory usage: 3.7+ MB\n"
     ]
    }
   ],
   "source": [
    "new_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1979,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([' <=50K', ' >50K'], dtype=object)"
      ]
     },
     "execution_count": 1979,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# target feature unique classifier\n",
    "new_df['income'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Binary Classifier\n",
    "Here is the binary classification for income column </br>\n",
    "0 : <=50K </br>\n",
    "1 : >50K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1980,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education_num</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital_gain</th>\n",
       "      <th>capital_loss</th>\n",
       "      <th>hours_per_week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</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>0</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>0</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>0</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32556</th>\n",
       "      <td>27</td>\n",
       "      <td>Private</td>\n",
       "      <td>257302</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Tech-support</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32557</th>\n",
       "      <td>40</td>\n",
       "      <td>Private</td>\n",
       "      <td>154374</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32558</th>\n",
       "      <td>58</td>\n",
       "      <td>Private</td>\n",
       "      <td>151910</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32559</th>\n",
       "      <td>22</td>\n",
       "      <td>Private</td>\n",
       "      <td>201490</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32560</th>\n",
       "      <td>52</td>\n",
       "      <td>Self-emp-inc</td>\n",
       "      <td>287927</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>15024</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32561 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age          workclass  fnlwgt    education  education_num  \\\n",
       "0       39          State-gov   77516    Bachelors             13   \n",
       "1       50   Self-emp-not-inc   83311    Bachelors             13   \n",
       "2       38            Private  215646      HS-grad              9   \n",
       "3       53            Private  234721         11th              7   \n",
       "4       28            Private  338409    Bachelors             13   \n",
       "...    ...                ...     ...          ...            ...   \n",
       "32556   27            Private  257302   Assoc-acdm             12   \n",
       "32557   40            Private  154374      HS-grad              9   \n",
       "32558   58            Private  151910      HS-grad              9   \n",
       "32559   22            Private  201490      HS-grad              9   \n",
       "32560   52       Self-emp-inc  287927      HS-grad              9   \n",
       "\n",
       "            marital_status          occupation    relationship    race  \\\n",
       "0            Never-married        Adm-clerical   Not-in-family   White   \n",
       "1       Married-civ-spouse     Exec-managerial         Husband   White   \n",
       "2                 Divorced   Handlers-cleaners   Not-in-family   White   \n",
       "3       Married-civ-spouse   Handlers-cleaners         Husband   Black   \n",
       "4       Married-civ-spouse      Prof-specialty            Wife   Black   \n",
       "...                    ...                 ...             ...     ...   \n",
       "32556   Married-civ-spouse        Tech-support            Wife   White   \n",
       "32557   Married-civ-spouse   Machine-op-inspct         Husband   White   \n",
       "32558              Widowed        Adm-clerical       Unmarried   White   \n",
       "32559        Never-married        Adm-clerical       Own-child   White   \n",
       "32560   Married-civ-spouse     Exec-managerial            Wife   White   \n",
       "\n",
       "           sex  capital_gain  capital_loss  hours_per_week  native-country  \\\n",
       "0         Male          2174             0              40   United-States   \n",
       "1         Male             0             0              13   United-States   \n",
       "2         Male             0             0              40   United-States   \n",
       "3         Male             0             0              40   United-States   \n",
       "4       Female             0             0              40            Cuba   \n",
       "...        ...           ...           ...             ...             ...   \n",
       "32556   Female             0             0              38   United-States   \n",
       "32557     Male             0             0              40   United-States   \n",
       "32558   Female             0             0              40   United-States   \n",
       "32559     Male             0             0              20   United-States   \n",
       "32560   Female         15024             0              40   United-States   \n",
       "\n",
       "       income  \n",
       "0           0  \n",
       "1           0  \n",
       "2           0  \n",
       "3           0  \n",
       "4           0  \n",
       "...       ...  \n",
       "32556       0  \n",
       "32557       1  \n",
       "32558       0  \n",
       "32559       0  \n",
       "32560       1  \n",
       "\n",
       "[32561 rows x 15 columns]"
      ]
     },
     "execution_count": 1980,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert our income column to binary classify\n",
    "new_df['income'] = new_df['income'].str.strip().apply(lambda x: 1 if x == '>50K' else 0)\n",
    "new_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the information of above cell, we can see we have a lot of object data-types that has to be converted to numeric so as to create our model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Replacing data**\n",
    "\n",
    "Identify any irrelevant data and replace it with **nan** value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1981,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df = new_df.replace(\" ?\", np.nan)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have missing values as shown below, because we replaced irrelevant data with nan values <br/>\n",
    "The missing values will be handled after we split our data into train and test values to avoid data leakage to our model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1982,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                  0\n",
       "workclass         1836\n",
       "fnlwgt               0\n",
       "education            0\n",
       "education_num        0\n",
       "marital_status       0\n",
       "occupation        1843\n",
       "relationship         0\n",
       "race                 0\n",
       "sex                  0\n",
       "capital_gain         0\n",
       "capital_loss         0\n",
       "hours_per_week       0\n",
       "native-country     583\n",
       "income               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 1982,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploratory Data Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "KEY NOTE: -> income column\n",
    "\n",
    "    0 : <=50K \n",
    "    1 : >50K"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The chart below shows the distribution of income based on ages in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1983,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 1983,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data=new_df, x=\"age\", binwidth=10, col=\"income\", kde=True)\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observation above:\n",
    "\n",
    "From the chart above, I will focus more on the graph positioned on the right side. This is because I am more focused on income 1(>50K) which will help <br/>\n",
    "the real-estate agency target potential clients.<br/>\n",
    "The age between 40-50 years tend to be having a promising income(>50K)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Gender Feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1984,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " Male      21790\n",
       " Female    10771\n",
       "Name: sex, dtype: int64"
      ]
     },
     "execution_count": 1984,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df[\"sex\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Gender is key while identifying potential clients before any approach is initiated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1985,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 1985,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 565.972x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data=new_df, x=\"sex\", binwidth=10, hue=\"income\")\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observation above\n",
    "\n",
    "Male tend to dominate in both income 1(>50K) and income 0(<=50K)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Occupation Feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1986,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " Prof-specialty       4140\n",
       " Craft-repair         4099\n",
       " Exec-managerial      4066\n",
       " Adm-clerical         3770\n",
       " Sales                3650\n",
       " Other-service        3295\n",
       " Machine-op-inspct    2002\n",
       " Transport-moving     1597\n",
       " Handlers-cleaners    1370\n",
       " Farming-fishing       994\n",
       " Tech-support          928\n",
       " Protective-serv       649\n",
       " Priv-house-serv       149\n",
       " Armed-Forces            9\n",
       "Name: occupation, dtype: int64"
      ]
     },
     "execution_count": 1986,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df[\"occupation\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The chart below compares income earned based on occupation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1987,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x14c44b09340>"
      ]
     },
     "execution_count": 1987,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 565.972x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(data=new_df, y=\"occupation\", kind=\"count\", hue=\"income\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below is a chart that displays occupation based on gender and whether their income is <=50K(0) or is >50K(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1988,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x14c44ae6940>"
      ]
     },
     "execution_count": 1988,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1065.97x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(data=new_df, y=\"occupation\", kind=\"count\", hue=\"income\", col=\"sex\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Education Feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1989,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " HS-grad         10501\n",
       " Some-college     7291\n",
       " Bachelors        5355\n",
       " Masters          1723\n",
       " Assoc-voc        1382\n",
       " 11th             1175\n",
       " Assoc-acdm       1067\n",
       " 10th              933\n",
       " 7th-8th           646\n",
       " Prof-school       576\n",
       " 9th               514\n",
       " 12th              433\n",
       " Doctorate         413\n",
       " 5th-6th           333\n",
       " 1st-4th           168\n",
       " Preschool          51\n",
       "Name: education, dtype: int64"
      ]
     },
     "execution_count": 1989,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df[\"education\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1990,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x14c3fd24910>"
      ]
     },
     "execution_count": 1990,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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",
      "text/plain": [
       "<Figure size 565.972x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(data=new_df, y=\"education\", kind=\"count\", hue=\"income\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Identify Predictors(X) and Target(y) features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1991,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = new_df.drop(columns=['income'])\n",
    "y = new_df['income']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predictor Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1992,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictors\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education_num</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital_gain</th>\n",
       "      <th>capital_loss</th>\n",
       "      <th>hours_per_week</th>\n",
       "      <th>native-country</th>\n",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          workclass  fnlwgt   education  education_num  \\\n",
       "0   39          State-gov   77516   Bachelors             13   \n",
       "1   50   Self-emp-not-inc   83311   Bachelors             13   \n",
       "2   38            Private  215646     HS-grad              9   \n",
       "3   53            Private  234721        11th              7   \n",
       "4   28            Private  338409   Bachelors             13   \n",
       "\n",
       "        marital_status          occupation    relationship    race      sex  \\\n",
       "0        Never-married        Adm-clerical   Not-in-family   White     Male   \n",
       "1   Married-civ-spouse     Exec-managerial         Husband   White     Male   \n",
       "2             Divorced   Handlers-cleaners   Not-in-family   White     Male   \n",
       "3   Married-civ-spouse   Handlers-cleaners         Husband   Black     Male   \n",
       "4   Married-civ-spouse      Prof-specialty            Wife   Black   Female   \n",
       "\n",
       "   capital_gain  capital_loss  hours_per_week  native-country  \n",
       "0          2174             0              40   United-States  \n",
       "1             0             0              13   United-States  \n",
       "2             0             0              40   United-States  \n",
       "3             0             0              40   United-States  \n",
       "4             0             0              40            Cuba  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# My predictor features\n",
    "print(\"Predictors\")\n",
    "display(X.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Drop unnecessary predictor features.\n",
    "Columns to drop: <br/>\n",
    "fnlwgt -> data does not give detailed information<br/>\n",
    "race -> this will promote biasness in disregard to modern society"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1993,
   "metadata": {},
   "outputs": [],
   "source": [
    "X.drop(columns=['fnlwgt', 'race', 'education_num'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1994,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>education</th>\n",
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       "      <th>3</th>\n",
       "      <td>53</td>\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>Male</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32556</th>\n",
       "      <td>27</td>\n",
       "      <td>Private</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Tech-support</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32557</th>\n",
       "      <td>40</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32558</th>\n",
       "      <td>58</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32559</th>\n",
       "      <td>22</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32560</th>\n",
       "      <td>52</td>\n",
       "      <td>Self-emp-inc</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Female</td>\n",
       "      <td>15024</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32561 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age          workclass    education       marital_status  \\\n",
       "0       39          State-gov    Bachelors        Never-married   \n",
       "1       50   Self-emp-not-inc    Bachelors   Married-civ-spouse   \n",
       "2       38            Private      HS-grad             Divorced   \n",
       "3       53            Private         11th   Married-civ-spouse   \n",
       "4       28            Private    Bachelors   Married-civ-spouse   \n",
       "...    ...                ...          ...                  ...   \n",
       "32556   27            Private   Assoc-acdm   Married-civ-spouse   \n",
       "32557   40            Private      HS-grad   Married-civ-spouse   \n",
       "32558   58            Private      HS-grad              Widowed   \n",
       "32559   22            Private      HS-grad        Never-married   \n",
       "32560   52       Self-emp-inc      HS-grad   Married-civ-spouse   \n",
       "\n",
       "               occupation    relationship      sex  capital_gain  \\\n",
       "0            Adm-clerical   Not-in-family     Male          2174   \n",
       "1         Exec-managerial         Husband     Male             0   \n",
       "2       Handlers-cleaners   Not-in-family     Male             0   \n",
       "3       Handlers-cleaners         Husband     Male             0   \n",
       "4          Prof-specialty            Wife   Female             0   \n",
       "...                   ...             ...      ...           ...   \n",
       "32556        Tech-support            Wife   Female             0   \n",
       "32557   Machine-op-inspct         Husband     Male             0   \n",
       "32558        Adm-clerical       Unmarried   Female             0   \n",
       "32559        Adm-clerical       Own-child     Male             0   \n",
       "32560     Exec-managerial            Wife   Female         15024   \n",
       "\n",
       "       capital_loss  hours_per_week  native-country  \n",
       "0                 0              40   United-States  \n",
       "1                 0              13   United-States  \n",
       "2                 0              40   United-States  \n",
       "3                 0              40   United-States  \n",
       "4                 0              40            Cuba  \n",
       "...             ...             ...             ...  \n",
       "32556             0              38   United-States  \n",
       "32557             0              40   United-States  \n",
       "32558             0              40   United-States  \n",
       "32559             0              20   United-States  \n",
       "32560             0              40   United-States  \n",
       "\n",
       "[32561 rows x 11 columns]"
      ]
     },
     "execution_count": 1994,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View our new predictor data after dropping unnecessary columns\n",
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Target Feature\n",
    "NOTE:\n",
    "\n",
    "    0 : <=50K \n",
    "    1 : >50K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1995,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Target\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0        0\n",
       "1        0\n",
       "2        0\n",
       "3        0\n",
       "4        0\n",
       "        ..\n",
       "32556    0\n",
       "32557    1\n",
       "32558    0\n",
       "32559    0\n",
       "32560    1\n",
       "Name: income, Length: 32561, dtype: int64"
      ]
     },
     "execution_count": 1995,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# My Target feature\n",
    "print(\"Target\")\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1996,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1], dtype=int64)"
      ]
     },
     "execution_count": 1996,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find out the unique binary classification of the target values\n",
    "y.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Perform train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1997,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the module utility to use\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1998,
   "metadata": {},
   "outputs": [],
   "source": [
    "# initialize our train_test_split class\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Handling missing values in predictors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Identify the null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1999,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                  0\n",
       "workclass         1210\n",
       "education            0\n",
       "marital_status       0\n",
       "occupation        1217\n",
       "relationship         0\n",
       "sex                  0\n",
       "capital_gain         0\n",
       "capital_loss         0\n",
       "hours_per_week       0\n",
       "native-country     392\n",
       "dtype: int64"
      ]
     },
     "execution_count": 1999,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Idetify the missing values in our training data\n",
    "X_train.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2000,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                 0\n",
       "workclass         626\n",
       "education           0\n",
       "marital_status      0\n",
       "occupation        626\n",
       "relationship        0\n",
       "sex                 0\n",
       "capital_gain        0\n",
       "capital_loss        0\n",
       "hours_per_week      0\n",
       "native-country    191\n",
       "dtype: int64"
      ]
     },
     "execution_count": 2000,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Idetify the missing values in our test data\n",
    "X_test.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Impute\n",
    "Missing value imputation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2001,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import simple imputer\n",
    "from sklearn.impute import SimpleImputer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The missing values will be imputed by the most frequent because the columns are categorecal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2002,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create copies of your train and test data\n",
    "X_train_final = X_train.copy()\n",
    "X_test_final = X_test.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2003,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>education</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital_gain</th>\n",
       "      <th>capital_loss</th>\n",
       "      <th>hours_per_week</th>\n",
       "      <th>native-country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27585</th>\n",
       "      <td>51</td>\n",
       "      <td>Private</td>\n",
       "      <td>Masters</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18010</th>\n",
       "      <td>46</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>Married-spouse-absent</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Philippines</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22472</th>\n",
       "      <td>25</td>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>55</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4585</th>\n",
       "      <td>27</td>\n",
       "      <td>Private</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11191</th>\n",
       "      <td>47</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>Masters</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Male</td>\n",
       "      <td>7688</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       age   workclass    education          marital_status        occupation  \\\n",
       "27585   51     Private      Masters      Married-civ-spouse   Exec-managerial   \n",
       "18010   46         NaN      HS-grad   Married-spouse-absent               NaN   \n",
       "22472   25     Private    Bachelors           Never-married    Prof-specialty   \n",
       "4585    27     Private   Assoc-acdm           Never-married     Other-service   \n",
       "11191   47   Local-gov      Masters      Married-civ-spouse    Prof-specialty   \n",
       "\n",
       "         relationship      sex  capital_gain  capital_loss  hours_per_week  \\\n",
       "27585         Husband     Male             0             0              50   \n",
       "18010   Not-in-family     Male             0             0              40   \n",
       "22472       Own-child   Female             0             0              55   \n",
       "4585    Not-in-family     Male             0             0              40   \n",
       "11191         Husband     Male          7688             0              45   \n",
       "\n",
       "       native-country  \n",
       "27585   United-States  \n",
       "18010     Philippines  \n",
       "22472   United-States  \n",
       "4585    United-States  \n",
       "11191   United-States  "
      ]
     },
     "execution_count": 2003,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2004,
   "metadata": {},
   "outputs": [],
   "source": [
    "imp_freq = SimpleImputer(strategy=\"most_frequent\")\n",
    "\n",
    "# For the train data I perform both the fit and transform method\n",
    "X_train_imputed = imp_freq.fit_transform(X_train_final)\n",
    "\n",
    "# For the test data I perform only the transform method\n",
    "X_test_imputed = imp_freq.transform(X_test_final)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below, Convert the imputed data back into dataframes, keeping original column names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2005,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_final_df = pd.DataFrame(X_train_imputed, columns=X_train.columns)\n",
    "X_test_final_df = pd.DataFrame(X_test_imputed, columns=X_train.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check whether the missing values were handled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2006,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age               0\n",
       "workclass         0\n",
       "education         0\n",
       "marital_status    0\n",
       "occupation        0\n",
       "relationship      0\n",
       "sex               0\n",
       "capital_gain      0\n",
       "capital_loss      0\n",
       "hours_per_week    0\n",
       "native-country    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 2006,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_final_df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2007,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age               0\n",
       "workclass         0\n",
       "education         0\n",
       "marital_status    0\n",
       "occupation        0\n",
       "relationship      0\n",
       "sex               0\n",
       "capital_gain      0\n",
       "capital_loss      0\n",
       "hours_per_week    0\n",
       "native-country    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 2007,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_final_df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Convert object dtype to numeric format\n",
    "OneHotEncoder will assist here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2008,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import relevant class\n",
    "from sklearn.preprocessing import OneHotEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2009,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize OneHotEncoder\n",
    "encoder = OneHotEncoder(sparse=False, handle_unknown='ignore')\n",
    "\n",
    "# Fit on the training data\n",
    "X_train_encoded = encoder.fit_transform(X_train_final_df)\n",
    "\n",
    "# Transform the test data\n",
    "X_test_encoded = encoder.transform(X_test_final_df)\n",
    "\n",
    "# Convert the encoded arrays back to DataFrames\n",
    "X_train_encoded = pd.DataFrame(X_train_encoded, index=(X_train_final_df.index), columns=encoder.get_feature_names(X_train_final_df.columns) )\n",
    "X_test_encoded = pd.DataFrame(X_test_encoded, index=(X_test_final_df.index), columns=encoder.get_feature_names(X_train_final_df.columns))\n",
    "\n",
    "\n",
    "# pd.DataFrame(ohe.fit_transform(X_train_cat), index=X_train.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2010,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21815 rows × 461 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age_17  age_18  age_19  age_20  age_21  age_22  age_23  age_24  age_25  \\\n",
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       "\n",
       "[21815 rows x 461 columns]"
      ]
     },
     "execution_count": 2010,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2011,
   "metadata": {},
   "outputs": [
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       "4                          0.0                         0.0  \n",
       "...                        ...                         ...  \n",
       "10741                      0.0                         0.0  \n",
       "10742                      0.0                         0.0  \n",
       "10743                      0.0                         0.0  \n",
       "10744                      0.0                         0.0  \n",
       "10745                      0.0                         0.0  \n",
       "\n",
       "[10746 rows x 461 columns]"
      ]
     },
     "execution_count": 2011,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_encoded"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generate descriptive statistics below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2012,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age_17</th>\n",
       "      <th>age_18</th>\n",
       "      <th>age_19</th>\n",
       "      <th>age_20</th>\n",
       "      <th>age_21</th>\n",
       "      <th>age_22</th>\n",
       "      <th>age_23</th>\n",
       "      <th>age_24</th>\n",
       "      <th>age_25</th>\n",
       "      <th>age_26</th>\n",
       "      <th>...</th>\n",
       "      <th>native-country_ Portugal</th>\n",
       "      <th>native-country_ Puerto-Rico</th>\n",
       "      <th>native-country_ Scotland</th>\n",
       "      <th>native-country_ South</th>\n",
       "      <th>native-country_ Taiwan</th>\n",
       "      <th>native-country_ Thailand</th>\n",
       "      <th>native-country_ Trinadad&amp;Tobago</th>\n",
       "      <th>native-country_ United-States</th>\n",
       "      <th>native-country_ Vietnam</th>\n",
       "      <th>native-country_ Yugoslavia</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>21815.000000</td>\n",
       "      <td>21815.000000</td>\n",
       "      <td>21815.000000</td>\n",
       "      <td>21815.000000</td>\n",
       "      <td>21815.000000</td>\n",
       "      <td>21815.000000</td>\n",
       "      <td>21815.000000</td>\n",
       "      <td>21815.000000</td>\n",
       "      <td>21815.000000</td>\n",
       "      <td>21815.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>21815.000000</td>\n",
       "      <td>21815.000000</td>\n",
       "      <td>21815.000000</td>\n",
       "      <td>21815.000000</td>\n",
       "      <td>21815.000000</td>\n",
       "      <td>21815.000000</td>\n",
       "      <td>21815.000000</td>\n",
       "      <td>21815.000000</td>\n",
       "      <td>21815.000000</td>\n",
       "      <td>21815.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.012514</td>\n",
       "      <td>0.016365</td>\n",
       "      <td>0.021499</td>\n",
       "      <td>0.023928</td>\n",
       "      <td>0.022645</td>\n",
       "      <td>0.023103</td>\n",
       "      <td>0.027275</td>\n",
       "      <td>0.024983</td>\n",
       "      <td>0.025120</td>\n",
       "      <td>0.023928</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001238</td>\n",
       "      <td>0.003759</td>\n",
       "      <td>0.000229</td>\n",
       "      <td>0.002705</td>\n",
       "      <td>0.001467</td>\n",
       "      <td>0.000458</td>\n",
       "      <td>0.000642</td>\n",
       "      <td>0.911529</td>\n",
       "      <td>0.002430</td>\n",
       "      <td>0.000458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.111168</td>\n",
       "      <td>0.126877</td>\n",
       "      <td>0.145044</td>\n",
       "      <td>0.152830</td>\n",
       "      <td>0.148772</td>\n",
       "      <td>0.150235</td>\n",
       "      <td>0.162887</td>\n",
       "      <td>0.156076</td>\n",
       "      <td>0.156494</td>\n",
       "      <td>0.152830</td>\n",
       "      <td>...</td>\n",
       "      <td>0.035160</td>\n",
       "      <td>0.061196</td>\n",
       "      <td>0.015138</td>\n",
       "      <td>0.051936</td>\n",
       "      <td>0.038273</td>\n",
       "      <td>0.021406</td>\n",
       "      <td>0.025325</td>\n",
       "      <td>0.283986</td>\n",
       "      <td>0.049231</td>\n",
       "      <td>0.021406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 461 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             age_17        age_18        age_19        age_20        age_21  \\\n",
       "count  21815.000000  21815.000000  21815.000000  21815.000000  21815.000000   \n",
       "mean       0.012514      0.016365      0.021499      0.023928      0.022645   \n",
       "std        0.111168      0.126877      0.145044      0.152830      0.148772   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "50%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "75%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "max        1.000000      1.000000      1.000000      1.000000      1.000000   \n",
       "\n",
       "             age_22        age_23        age_24        age_25        age_26  \\\n",
       "count  21815.000000  21815.000000  21815.000000  21815.000000  21815.000000   \n",
       "mean       0.023103      0.027275      0.024983      0.025120      0.023928   \n",
       "std        0.150235      0.162887      0.156076      0.156494      0.152830   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "50%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "75%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "max        1.000000      1.000000      1.000000      1.000000      1.000000   \n",
       "\n",
       "       ...  native-country_ Portugal  native-country_ Puerto-Rico  \\\n",
       "count  ...              21815.000000                 21815.000000   \n",
       "mean   ...                  0.001238                     0.003759   \n",
       "std    ...                  0.035160                     0.061196   \n",
       "min    ...                  0.000000                     0.000000   \n",
       "25%    ...                  0.000000                     0.000000   \n",
       "50%    ...                  0.000000                     0.000000   \n",
       "75%    ...                  0.000000                     0.000000   \n",
       "max    ...                  1.000000                     1.000000   \n",
       "\n",
       "       native-country_ Scotland  native-country_ South  \\\n",
       "count              21815.000000           21815.000000   \n",
       "mean                   0.000229               0.002705   \n",
       "std                    0.015138               0.051936   \n",
       "min                    0.000000               0.000000   \n",
       "25%                    0.000000               0.000000   \n",
       "50%                    0.000000               0.000000   \n",
       "75%                    0.000000               0.000000   \n",
       "max                    1.000000               1.000000   \n",
       "\n",
       "       native-country_ Taiwan  native-country_ Thailand  \\\n",
       "count            21815.000000              21815.000000   \n",
       "mean                 0.001467                  0.000458   \n",
       "std                  0.038273                  0.021406   \n",
       "min                  0.000000                  0.000000   \n",
       "25%                  0.000000                  0.000000   \n",
       "50%                  0.000000                  0.000000   \n",
       "75%                  0.000000                  0.000000   \n",
       "max                  1.000000                  1.000000   \n",
       "\n",
       "       native-country_ Trinadad&Tobago  native-country_ United-States  \\\n",
       "count                     21815.000000                   21815.000000   \n",
       "mean                          0.000642                       0.911529   \n",
       "std                           0.025325                       0.283986   \n",
       "min                           0.000000                       0.000000   \n",
       "25%                           0.000000                       1.000000   \n",
       "50%                           0.000000                       1.000000   \n",
       "75%                           0.000000                       1.000000   \n",
       "max                           1.000000                       1.000000   \n",
       "\n",
       "       native-country_ Vietnam  native-country_ Yugoslavia  \n",
       "count             21815.000000                21815.000000  \n",
       "mean                  0.002430                    0.000458  \n",
       "std                   0.049231                    0.021406  \n",
       "min                   0.000000                    0.000000  \n",
       "25%                   0.000000                    0.000000  \n",
       "50%                   0.000000                    0.000000  \n",
       "75%                   0.000000                    0.000000  \n",
       "max                   1.000000                    1.000000  \n",
       "\n",
       "[8 rows x 461 columns]"
      ]
     },
     "execution_count": 2012,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_encoded.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2013,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age_17</th>\n",
       "      <th>age_18</th>\n",
       "      <th>age_19</th>\n",
       "      <th>age_20</th>\n",
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       "      <th>age_26</th>\n",
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       "      <th>native-country_ Scotland</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
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       "      <td>10746.000000</td>\n",
       "      <td>10746.000000</td>\n",
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       "      <td>10746.000000</td>\n",
       "      <td>10746.000000</td>\n",
       "      <td>10746.000000</td>\n",
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       "      <td>10746.000000</td>\n",
       "      <td>10746.000000</td>\n",
       "      <td>10746.000000</td>\n",
       "      <td>10746.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.011353</td>\n",
       "      <td>0.017960</td>\n",
       "      <td>0.022613</td>\n",
       "      <td>0.021496</td>\n",
       "      <td>0.021031</td>\n",
       "      <td>0.024288</td>\n",
       "      <td>0.026242</td>\n",
       "      <td>0.023544</td>\n",
       "      <td>0.027266</td>\n",
       "      <td>0.024474</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000931</td>\n",
       "      <td>0.002978</td>\n",
       "      <td>0.000651</td>\n",
       "      <td>0.001954</td>\n",
       "      <td>0.001768</td>\n",
       "      <td>0.000744</td>\n",
       "      <td>0.000465</td>\n",
       "      <td>0.918295</td>\n",
       "      <td>0.001303</td>\n",
       "      <td>0.000558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.105949</td>\n",
       "      <td>0.132813</td>\n",
       "      <td>0.148673</td>\n",
       "      <td>0.145039</td>\n",
       "      <td>0.143495</td>\n",
       "      <td>0.153949</td>\n",
       "      <td>0.159863</td>\n",
       "      <td>0.151629</td>\n",
       "      <td>0.162865</td>\n",
       "      <td>0.154523</td>\n",
       "      <td>...</td>\n",
       "      <td>0.030493</td>\n",
       "      <td>0.054491</td>\n",
       "      <td>0.025516</td>\n",
       "      <td>0.044165</td>\n",
       "      <td>0.042014</td>\n",
       "      <td>0.027276</td>\n",
       "      <td>0.021567</td>\n",
       "      <td>0.273927</td>\n",
       "      <td>0.036073</td>\n",
       "      <td>0.023624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 461 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             age_17        age_18        age_19        age_20        age_21  \\\n",
       "count  10746.000000  10746.000000  10746.000000  10746.000000  10746.000000   \n",
       "mean       0.011353      0.017960      0.022613      0.021496      0.021031   \n",
       "std        0.105949      0.132813      0.148673      0.145039      0.143495   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "50%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "75%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "max        1.000000      1.000000      1.000000      1.000000      1.000000   \n",
       "\n",
       "             age_22        age_23        age_24        age_25        age_26  \\\n",
       "count  10746.000000  10746.000000  10746.000000  10746.000000  10746.000000   \n",
       "mean       0.024288      0.026242      0.023544      0.027266      0.024474   \n",
       "std        0.153949      0.159863      0.151629      0.162865      0.154523   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "50%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "75%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "max        1.000000      1.000000      1.000000      1.000000      1.000000   \n",
       "\n",
       "       ...  native-country_ Portugal  native-country_ Puerto-Rico  \\\n",
       "count  ...              10746.000000                 10746.000000   \n",
       "mean   ...                  0.000931                     0.002978   \n",
       "std    ...                  0.030493                     0.054491   \n",
       "min    ...                  0.000000                     0.000000   \n",
       "25%    ...                  0.000000                     0.000000   \n",
       "50%    ...                  0.000000                     0.000000   \n",
       "75%    ...                  0.000000                     0.000000   \n",
       "max    ...                  1.000000                     1.000000   \n",
       "\n",
       "       native-country_ Scotland  native-country_ South  \\\n",
       "count              10746.000000           10746.000000   \n",
       "mean                   0.000651               0.001954   \n",
       "std                    0.025516               0.044165   \n",
       "min                    0.000000               0.000000   \n",
       "25%                    0.000000               0.000000   \n",
       "50%                    0.000000               0.000000   \n",
       "75%                    0.000000               0.000000   \n",
       "max                    1.000000               1.000000   \n",
       "\n",
       "       native-country_ Taiwan  native-country_ Thailand  \\\n",
       "count            10746.000000              10746.000000   \n",
       "mean                 0.001768                  0.000744   \n",
       "std                  0.042014                  0.027276   \n",
       "min                  0.000000                  0.000000   \n",
       "25%                  0.000000                  0.000000   \n",
       "50%                  0.000000                  0.000000   \n",
       "75%                  0.000000                  0.000000   \n",
       "max                  1.000000                  1.000000   \n",
       "\n",
       "       native-country_ Trinadad&Tobago  native-country_ United-States  \\\n",
       "count                     10746.000000                   10746.000000   \n",
       "mean                          0.000465                       0.918295   \n",
       "std                           0.021567                       0.273927   \n",
       "min                           0.000000                       0.000000   \n",
       "25%                           0.000000                       1.000000   \n",
       "50%                           0.000000                       1.000000   \n",
       "75%                           0.000000                       1.000000   \n",
       "max                           1.000000                       1.000000   \n",
       "\n",
       "       native-country_ Vietnam  native-country_ Yugoslavia  \n",
       "count             10746.000000                10746.000000  \n",
       "mean                  0.001303                    0.000558  \n",
       "std                   0.036073                    0.023624  \n",
       "min                   0.000000                    0.000000  \n",
       "25%                   0.000000                    0.000000  \n",
       "50%                   0.000000                    0.000000  \n",
       "75%                   0.000000                    0.000000  \n",
       "max                   1.000000                    1.000000  \n",
       "\n",
       "[8 rows x 461 columns]"
      ]
     },
     "execution_count": 2013,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_encoded.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "observation above <br/>\n",
    "The encoded data falls between a scale of 0 to 1, which is good fo modelling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The below code cell, Checks if our target feature(y) is imbalanced."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2017,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Raw Counts\n",
      "0    24720\n",
      "1     7841\n",
      "Name: income, dtype: int64\n",
      "\n",
      "Percentages\n",
      "0    0.75919\n",
      "1    0.24081\n",
      "Name: income, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(\"Raw Counts\")\n",
    "print(y.value_counts())\n",
    "print()\n",
    "print(\"Percentages\")\n",
    "print(y.value_counts(normalize=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2024,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Raw Counts\n",
      "0    16524\n",
      "1     5291\n",
      "Name: income, dtype: int64\n",
      "\n",
      "Percentages\n",
      "0    0.75746\n",
      "1    0.24254\n",
      "Name: income, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# target features train_test_split\n",
    "print(\"Raw Counts\")\n",
    "print(y_train.value_counts())\n",
    "print()\n",
    "print(\"Percentages\")\n",
    "print(y_train.value_counts(normalize=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From output above, our target feature appears to be imbalanced."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modelling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Baseline Model - Logistic Regression\n",
    "Use scikit-learn's LogisticRegression model<br/>\n",
    "Then use cross_val_score with scoring=\"neg_log_loss\" to find the average cross-validated log loss for this model on X_train_encoded and y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2091,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2865160554275827"
      ]
     },
     "execution_count": 2091,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import relevant class\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "# Instantiate a LogisticRegression \n",
    "baseline_model = LogisticRegression(random_state=42, max_iter=1000)\n",
    "\n",
    "# Instantiate a cross_val_score with scoring=\"neg_log_loss\" to evaluate the model\n",
    "baseline_neg_log_loss_cv = cross_val_score(baseline_model, X_train_encoded, y_train, scoring=\"neg_log_loss\")\n",
    "\n",
    "baseline_log_loss = -(baseline_neg_log_loss_cv.mean())\n",
    "baseline_log_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2099,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2865160554275827"
      ]
     },
     "execution_count": 2099,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import make_scorer\n",
    "from sklearn.metrics import log_loss\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.base import clone\n",
    "\n",
    "def custom_cross_val_score(estimator, X, y):\n",
    "    # Create a list to hold the scores from each fold\n",
    "    kfold_train_scores = np.ndarray(5)\n",
    "    kfold_val_scores = np.ndarray(5)\n",
    "\n",
    "    # Instantiate a splitter object and loop over its result\n",
    "    kfold = StratifiedKFold(n_splits=5)\n",
    "    for fold, (train_index, val_index) in enumerate(kfold.split(X, y)):\n",
    "        # Extract train and validation subsets using the provided indices\n",
    "        X_t, X_val = X.iloc[train_index], X.iloc[val_index]\n",
    "        y_t, y_val = y.iloc[train_index], y.iloc[val_index]\n",
    "        \n",
    "        \n",
    "        # Clone the provided model and fit it on the train subset\n",
    "        temp_model = clone(estimator)\n",
    "        temp_model.fit(X_t, y_t)\n",
    "        \n",
    "        # Evaluate the provided model on the train and validation subsets\n",
    "        neg_log_loss_score_train = neg_log_loss(temp_model, X_t, y_t)\n",
    "        neg_log_loss_score_val = neg_log_loss(temp_model, X_val, y_val)\n",
    "        kfold_train_scores[fold] = neg_log_loss_score_train\n",
    "        kfold_val_scores[fold] = neg_log_loss_score_val\n",
    "        \n",
    "    return kfold_train_scores, kfold_val_scores\n",
    "        \n",
    "# using the defined function above        \n",
    "baseline_model = LogisticRegression(random_state=42, max_iter=1000)\n",
    "baseline_train_scores, baseline_neg_log_loss_cv = custom_cross_val_score(baseline_model, X_train_encoded, y_train)\n",
    "- (baseline_neg_log_loss_cv.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Explain output above<br/>\n",
    "log loss metric is not particularly interpretable but when lower, it is better."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Handling class imbalance\n",
    "Using SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2096,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.28805902864522304"
      ]
     },
     "execution_count": 2096,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import relevant sklearn and imblearn classes\n",
    "from imblearn.over_sampling import SMOTE\n",
    "\n",
    "def custom_cross_val_score(estimator, X, y):\n",
    "    # Create a list to hold the scores from each fold\n",
    "    kfold_train_scores = np.ndarray(5)\n",
    "    kfold_val_scores = np.ndarray(5)\n",
    "\n",
    "    # Instantiate a splitter object and loop over its result\n",
    "    kfold = StratifiedKFold(n_splits=5)\n",
    "    for fold, (train_index, val_index) in enumerate(kfold.split(X, y)):\n",
    "        # Extract train and validation subsets using the provided indices\n",
    "        X_t, X_val = X.iloc[train_index], X.iloc[val_index]\n",
    "        y_t, y_val = y.iloc[train_index], y.iloc[val_index]\n",
    "        \n",
    "        # # Instantiate StandardScaler\n",
    "        # scaler = StandardScaler()\n",
    "        # # Fit and transform X_t\n",
    "        # X_t_scaled = scaler.fit_transform(X_t)\n",
    "        # # Transform X_val\n",
    "        # X_val_scaled = scaler.transform(X_val)\n",
    "        \n",
    "        # Instantiate SMOTE with random_state=42 and sampling_strategy=0.28\n",
    "        sm = SMOTE(random_state=42, sampling_strategy=0.55)\n",
    "        # Fit and transform X_t_scaled and y_t using sm\n",
    "        X_t_oversampled, y_t_oversampled = sm.fit_resample(X_t, y_t)\n",
    "        \n",
    "        # Clone the provided model and fit it on the train subset\n",
    "        temp_model = clone(estimator)\n",
    "        temp_model.fit(X_t_oversampled, y_t_oversampled)\n",
    "        \n",
    "        # Evaluate the provided model on the train and validation subsets\n",
    "        neg_log_loss_score_train = neg_log_loss(temp_model, X_t_oversampled, y_t_oversampled)\n",
    "        neg_log_loss_score_val = neg_log_loss(temp_model, X_val, y_val)\n",
    "        kfold_train_scores[fold] = neg_log_loss_score_train\n",
    "        kfold_val_scores[fold] = neg_log_loss_score_val\n",
    "        \n",
    "    return kfold_train_scores, kfold_val_scores\n",
    "        \n",
    "model_with_preprocessing = LogisticRegression(random_state=42, class_weight={1: 0.55}, max_iter=1000)\n",
    "preprocessed_train_scores, preprocessed_neg_log_loss_cv = custom_cross_val_score(model_with_preprocessing, X_train_encoded, y_train)\n",
    "- (preprocessed_neg_log_loss_cv.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compare the log loss of the baseline model vs imbalance handled model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2088,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.2865160554275827\n",
      "0.28752615633657264\n"
     ]
    }
   ],
   "source": [
    "print(baseline_log_loss)\n",
    "print(-preprocessed_neg_log_loss_cv.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Explain results.\n",
    "\n",
    "The baseline model outperforms the imbalance handled model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check for **Overfitting or Underfitting** in our preferred model from above(baseline model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train:      [0.27220212 0.27453933 0.27481765 0.27790519 0.27530809]\n",
      "Validation: [0.2964799  0.28670631 0.28787852 0.27455354 0.28696201]\n"
     ]
    }
   ],
   "source": [
    "print(\"Train:     \", -baseline_train_scores)\n",
    "print(\"Validation:\", -baseline_neg_log_loss_cv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reducing Regularization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Previous Model\n",
      "Train:      0.2749544738970485\n",
      "Validation: 0.2865160554275827\n",
      "\n",
      "Current Model\n",
      "Train average:      0.263754278602938\n",
      "Validation average: 0.2790781440605517\n"
     ]
    }
   ],
   "source": [
    "model_less_regularization  = LogisticRegression(C=8, random_state=42, max_iter=1000)\n",
    "less_regularization_train_scores, less_regularization_val_scores = custom_cross_val_score(model_less_regularization , X_train_encoded, y_train)\n",
    "\n",
    "# compare previous model vs regularized model\n",
    "print(\"Previous Model\")\n",
    "print(\"Train:     \", -baseline_train_scores.mean())\n",
    "print(\"Validation:\", -baseline_neg_log_loss_cv.mean())\n",
    "print(\"\")\n",
    "print(\"Current Model\")\n",
    "print(\"Train average:     \", -less_regularization_train_scores.mean())\n",
    "print(\"Validation average:\", -less_regularization_val_scores.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Choose and Evaluate final model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2110,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_model = model_less_regularization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2139,
   "metadata": {},
   "outputs": [],
   "source": [
    "sm = SMOTE(random_state=42, sampling_strategy=0.33)\n",
    "# Fit and transform X_train_scaled and y_train using sm\n",
    "X_train_oversampled, y_train_oversampled = sm.fit_resample(X_train_encoded, y_train )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fitting the model to the full training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=8, max_iter=1000, random_state=42)"
      ]
     },
     "execution_count": 2140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_model.fit(X_train_oversampled, y_train_oversampled)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluate the model on the test data<br/>\n",
    "Log Loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2800766387821463"
      ]
     },
     "execution_count": 2141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_loss(y_test, final_model.predict_proba(X_test_encoded))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great! We are getting slightly better performance when we train the model with the full training set, compared to the average cross-validated performance. This is typical since models tend to perform better with more training data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Metrics\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2154,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_hat_test = final_model.predict(X_test_encoded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, 0, 0, 1, 1, 0, 0, 1], dtype=int64)"
      ]
     },
     "execution_count": 2155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_hat_test[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14160    0\n",
       "27048    0\n",
       "28868    1\n",
       "5667     0\n",
       "7827     0\n",
       "15382    1\n",
       "4641     1\n",
       "8943     0\n",
       "216      0\n",
       "5121     1\n",
       "Name: income, dtype: int64"
      ]
     },
     "execution_count": 2156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2157,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n",
      " [[7679  517]\n",
      " [ 876 1674]]\n"
     ]
    }
   ],
   "source": [
    "# Import confusion_matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "# Print confusion matrix\n",
    "cnf_matrix = confusion_matrix(y_test, y_hat_test)\n",
    "print('Confusion Matrix:\\n', cnf_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2158,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import plot_confusion_matrix\n",
    "from sklearn.metrics import ConfusionMatrixDisplay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x14c35d2e430>"
      ]
     },
     "execution_count": 2160,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sYPXq1UyaNAmoXZuQlJREamoqWVlZ5OTkMH36dMLCwhg5ciRAg2MY5dE1CkZWbTaaxRt8ol0NzXQ+3ejFsSM+3PRgK/v7V2U9c9SHuZk2AoK6ANB7IEyI8+b91VFMeOr/1pJ49wDgT6+1IO5aG136RDqM3zYUnl1nY8FD/jw0urbUFh5hY8LsGl54APyDwsHH8d+B1K/ngAhPh/CrZfGy8dIfW1FyzJ/Cg60A2P4F+L70A3clb+P6u1uyc9tPH/gXRHUGoE2H1vQc0LneMcfct5f/bulEQJsoeg5o+tdwvmvhe64+lixuWKPg2vTD5MmTCQgIsFfSIyMjWbJkCUOGDAFg5cqVzJ07l4SEBEJCQpg5c6Z9PQPA1KlTqa6uZvbs2ZSXlxMbG0tGRoZ9AWNwcHCDYxh6lTabKzmRa9577z1mzpzJrl278PrZSraZM2dSVFTEa6+91ugxbTYbFov2726slDEvcGhPASv+u9DeVnqilN+1u4urbr+cx9+a5tB/0oCHCe7UjnkfzrK37du+n/sGJPPE2hlcedOlTp/r6KFiyk9X0rlnGNv/sYPkEU+T+kkKMcP6uv11iRhhs5ZgKxqEJegPWIIcv21ZC3pDywfxajW17nlVu7AV34ilbRoW/9+cq3DFDYrKj5D8zaMujTG/33N09A9xU0TNl0crCkZWbTaaNR/rsfvdEZ5pVFdB9t9acMuDNViP3mhvDwDadmhBZcmnWI/+3fGcch98vXKxHpuOV7tFWI9NZ/Pa/fgFehM75DmsRx2fo+RH2PI3LwaPtNI+GPADWzF89y8vvLy8ibjg8TrnSP0euLZPw52kXm2Cy+g7qJCd2aEcL/7pb0zrduU8+ya8OTeLz/+2z95+QVRnHl0A/96whXfSZtYZ79pbv+PaW7x55KpNVFV+ck5ew/numfceoUOXc3OJqav7KJiFRxOFn6/a7Nq1q729qKiIqKiosxvUVgPVO90Rnmnk/jeAitN96HvJfqg+5XAsdnhXPvuoDScKv6NNcO2CmB/2+nFwbxTX3X649lpIgJrv+S67jJ4XeePXYi9UOz6HtcqbBQ9dxJTn8hk9tnZvhbJSL/7yRm/6XVZGUMt9dc6R+u39Sgs/z1ZY1wrmvZ7DW4tCeX1+mL094Z4j1NTAJ++WcXBfbp3zThwtYe9Xda/ESnroEHu2+7Nry4EmjdtMqirP3R8CV69cMAuPJgo/X7V5JlE4s2ozKSnJk6GZyv6c2mpO197ldY7dOb2Az//Shsdvj+TO6YVUV8Orz4UTEl7Jb+4oBn4qu+Xm+HPJ0Pp3WWwbXMPQG4+z+vlO+PpZaRtSzZq0UI4WtODR9Lx6zxFxt4IDfny8rh03Ty6iqsLCri8D6Tu4lNumFPHB6mAO7qv/ah5nIqLK2fbPoCaKVpqSu3ZmNAOPJgo/X7XZvn17OnfuzPz58x1WbUrTO3ak9uqFVm3qbi7TqVsli97fTcaccF6Y2hUvbxh45UkmpRwiMMjq0Pf4kRYE1TPGGQ+98AMZc8NZNS+c8jIvLhxYygvv7qVXvzL3viCRX/DizC4cyvXj6pt/5I5phRwtaMEbC0J5N71jo8dqG1LFqRPeDXcU+RXz6GJGgJqaGhYuXMiGDRvsqzaffPJJunTpclbj2ap/wHZ0uJujFKd8ovHq8F7t2gZN+ZwT14b393QIptFzQATLt73A/ZfMZO9XdackxP1e37uUTj2a/lLTwvIjTP3qCZfGSBvwDKFazNj0vL29SU5OJjk52dOhiIiIaVjcsJjRHGsctMuNiIiIOOXxioKIiIgn6KoHY5QoiIiI6eiqB+M09SAiIiJOqaIgIiKmpJ0ZjVGiICIi5mNzQ6JgkrkHTT2IiIiIU6ooiIiIKWnqwRglCiIiYjo2XL880iQzD0oURETEnFRRMEZrFERERMQpVRRERMSEdK8Ho5QoiIiI6WhnRuM09SAiIiJOqaIgIiKmpMWMxihREBERU7IpUTBEUw8iIiLilCoKIiJiSq5uuGQWShRERMR8dFMowzT1ICIiIk6poiAiIqZjw/XFjCYpKChREBERc9LlkcYoURAREROyuOHySHMkGlqjICIiIk6poiAiIqakqQdjlCiIiIgp2cyyGtFFmnoQERERp1RREBER07Hh+s6MZilIKFEQERFT0k2hjNHUg4iIiDilioKIiJiSrnowRomCiIiYj80NVz2YZJGCph5ERETEKVUURETElLSY0RhVFERExJRsNotLD1cdOnSIPn361HmsW7cOgF27dpGUlET//v0ZNmwYGRkZDudbrVbS0tKIj48nJiaGCRMmkJeX59CnoTGMUEVBRERMx4bF5cWMNhf3Yfjuu+/w8/Pj448/xmL5aaxWrVpx7Ngxxo8fz9VXX83TTz/N119/zdNPP03btm1JTEwEID09nTVr1vDss88SGhrK/PnzmThxIh988AG+vr6GxjBCiYKIiIgH7N69m4iICDp27Fjn2OrVq/H19SUlJQUfHx8iIyPJy8tjxYoVJCYmUllZyapVq0hOTmbo0KEALFq0iPj4eDZt2sTo0aNZu3btL45hlKYeRETElGw21x6u+u677+jZs2e9x7Kzs4mNjcXH56fv83FxceTm5lJcXExOTg6lpaXExcXZj7du3Zro6Gi2bt1qaAyjVFEQERFTcsc6g8OHDzN27Finx7Oyspwe2717NyEhIdxxxx3s37+fbt26MXnyZOLj4ykoKKB3794O/c9UHg4fPkxBQQEAnTp1qtMnPz8foMExgoODDb1GJQoiIiLnWGVlJfv37ycgIICZM2cSGBjI+++/z8SJE3n11VcpLy/H19fX4Rw/Pz8AKioqKCsrA6i3z4kTJwAaHMMoJQoiImJK7qgohIeH/2LVwBlfX1+2bt2Kj4+P/cP8oosuYt++fWRkZODv709lZaXDOWc+3AMDA/H39wdqE44zP5/pExAQANDgGEZpjYKIiJiSzcWHqwIDA+t84+/duzeFhYWEhYVRVFTkcOzM76GhofYph/r6hIWFATQ4hlFKFERERM6xnJwcBgwYQHZ2tkP7t99+S8+ePYmNjWXbtm3U1NTYj23evJmIiAiCg4OJiooiKCiILVu22I+XlJSwc+dOBg0aBNDgGEYpURAREVPy5IZLvXv3plevXjz99NNkZ2ezb98+nn32Wb7++mvuu+8+EhMTOXXqFLNmzWLv3r1s2LCB1atXM2nSJKB26iIpKYnU1FSysrLIyclh+vTphIWFMXLkSIAGxzBKaxRERMR83DF/4ML5Xl5evPTSS6SmpjJt2jRKSkqIjo7m1VdfpU+fPgCsXLmSuXPnkpCQQEhICDNnziQhIcE+xtSpU6murmb27NmUl5cTGxtLRkaGfTojODi4wTGMUKIgIiLiAe3bt2fevHlOj/fr14/MzEynx729vUlOTiY5OfmsxzBCiYKIiJiSbgpljBIFERExHRuu767ojisffg2UKIiIiCmpomCMrnoQERERp1RREBERc1JFwRBDicJjjz1meECLxfKLqzhFRESaA3fcAdIMDCUKP9/5qSEWizI0ERGR84WhROGTTz5p6jhERETOLVUUDDnrNQpWq5Xdu3dTVFTEwIEDqa6upm3btm4MTUREpInY3HDVg0kSjbNKFN577z0WLFhAUVERFouFd999lyVLltCiRQsWLFhQ525YIiIi8uvU6MsjP/zwQx555BHi4uJYtGgRtv9bDXLNNdfwr3/9i/T0dLcHKSIi4naevs/0r0SjKwovvfQSt912GykpKQ63rhwzZgzFxcWsXbuWadOmuTNGERERN3P9DpBgjsX7ja4o5Obm2m9h+b9iYmIoLCx0OSgRERFpHhqdKAQHB7Nv3756j+3bt4/g4GCXgxIREWlymnowpNGJwqhRo0hLS+Mvf/kLlZWVQO3eCd9++y3p6en85je/cXuQIiIi7mdx8WEOjV6jMG3aNHbv3s20adPw8qrNM8aOHcvp06cZNGgQDz30kNuDFBERcTsTVQVc0ehEwdfXl5UrV/LZZ5+xefNmTpw4QatWrRg8eDBDhw7VzowiIiLnkbPecOnyyy9n4MCBnDx5krZt22rvBBER+XVRRcGQs0oUPv/8c5YsWcL27dux2Wx4e3vTv39/pk2bxqBBg9wdo4iIiPvp7pGGnNWGSxMmTKCiooIHH3yQlJQU7rvvPo4fP85dd93Ff/7zn6aIU0RERDyg0RWF5cuXM3r0aBYsWODQ/sADDzB58mTmz5/P+vXr3RagiIhIU9Btpo1pdEUhLy+PhISEOu0Wi4U77riDPXv2uCUwERGRJuPqHgom2kuh0YlCZGQkO3furPdYfn4+Xbt2dTkoERERaR4MTT0cPnzY/vOECRN48skn8fLy4rrrriMkJIQTJ07w6aefsmTJEubOndtkwYqIiLiNFjMaYihRGD58uMP+CDabjdTU1DrrFGw2G5MmTWLXrl3ujVJERMTNLCaZOnCVoURh3rx52khJRETEhAwlCmPGjGnqOERERM4tVRQMOasNlwoKCvjyyy/tN4UCsFqtlJWVkZ2dzaJFi9wWoIiISJPQGgVDGp0ofPTRRyQnJ1NdXW2fjrDZbPafe/To4d4IRUREmoIqCoY0+vLIl19+mejoaDZs2MCYMWO44YYb+POf/0xycjI+Pj48/vjjTRGniIiIeECjKwq5ubmkpqYSHR3NpZdeysqVK4mMjCQyMpLi4mJeeuklLr/88qaIVURExH1UUTCk0RUFLy8v2rZtC0D37t35/vvvsVqtAMTHx7N37163BigiIuJ22pnRsEYnCj169GDbtm1AbaJQVVVl3zehpKTEYYGjiIiI/Lo1eurhtttu46mnnuL06dPMmDGDIUOG8Pjjj3PTTTfx5ptv0rdv36aIU0RExL101YMhja4o3HzzzcyaNYuqqioA/vjHP1JRUcHcuXOprq5m1qxZbg9SRETE3Sw21x5mcVb7KNx55532n7t27cpHH33EsWPHaN++vdsCExEREc9r9E2hjPQLDw8/+4hERETOBRNVBVxxVjeFaohuCiUiInJ+0E2hRERExKnz7qZQBQf9GX/JVZ4OwzR69uvK0r/D1Jtj2ftNqKfDMYfBXTwdgXn0Cav934t6Qosgz8ZiFn6+5+ypmtOCxNzcXMaMGcMTTzxh/8zdtWsXc+fO5dtvv6Vt27aMHTuWu+++236O1Wpl6dKlrFu3jpKSEi655BKeeuopunXrZu/T0BhGNPqqBxERkfOCzeLaw02qqqp4+OGHOX36tL3t2LFjjB8/nu7du7N+/XqmTJnC4sWLWb9+vb1Peno6a9asYc6cOWRmZmKxWJg4caJ9PyMjYxhxVlc9iIiI/Oo1k4rCkiVLaNmypUPb2rVr8fX1JSUlBR8fHyIjI8nLy2PFihUkJiZSWVnJqlWrSE5OZujQoQAsWrSI+Ph4Nm3axOjRoxscwyhVFERERDxk69atZGZm8vzzzzu0Z2dnExsbi4/PT9/n4+LiyM3Npbi4mJycHEpLS4mLi7Mfb926NdHR0WzdutXQGEYpURAREXPy8H0eSkpKmDlzJrNnz6ZTp04OxwoKCggLC3No69ixI1C7FUFBQQFAnfM6duxIfn6+oTGMcmnq4eTJkxQVFXHBBRfg7e2Nt7e3K8OJiIicG+7YXdFW+4E7duxYp12ysrKcHktJSaF///5cf/31dY6Vl5fj6+u4sNPPzw+AiooKysrKAOrtc+LECUNjGHVWicKWLVtITU3l22+/xWKxsG7dOlasWEFYWBiPPvro2QwpIiJiGhs3biQ7O5s//elP9R739/evc5PFMx/ugYGB+Pv7A1BZWWn/+UyfgIAAQ2MY1ehEYfPmzUycOJEBAwbw8MMPk5qaCkB0dDQvvvgioaGhjB8/vrHDioiInFtumD4IDw//xaqBM+vXr6e4uJhhw4Y5tD/11FNkZGQQHh5OUVGRw7Ezv4eGhlJdXW1v69q1q0OfqKgoAMLCwn5xDKManSi8+OKLjBgxgsWLF1NdXc38+fMBuPfeezl16hTr1q1ToiAiIs2fB696SE1Npby83KHtmmuuYerUqYwaNYo///nPrFmzhpqaGvu0/ubNm4mIiCA4OJhWrVoRFBTEli1b7IlCSUkJO3fuJCkpCYDY2NhfHMOoRi9m3LVrl/2yiv/drfHyyy/n0KFDjR1SRETEVEJDQ+nWrZvDAyA4OJjOnTuTmJjIqVOnmDVrFnv37mXDhg2sXr2aSZMmAbVrE5KSkkhNTSUrK4ucnBymT59OWFgYI0eOBGhwDKMaXVFo1aoVR44cqfdYfn4+rVq1auyQIiIi51xz2pnxfwUHB7Ny5Urmzp1LQkICISEhzJw5k4SEBHufqVOnUl1dzezZsykvLyc2NpaMjAz7AkYjYxjR6ERhxIgRLFq0iN69exMdHQ3UVhYKCgp46aWX6sy3iIiIND/u2F3RvfdA+u677xx+79evH5mZmU77e3t7k5ycTHJystM+DY1hRKMThT/84Q9s376dW265hQ4dOgAwY8YMCgoK6NSpEzNmzHApIBERkXOiGVcUmpNGJwpt2rRh3bp1bNy4kf/85z8cP36cVq1aMXbsWMaMGWO/LENERER+/c5qHwVfX19uueUWbrnlFnfHIyIi0uQsuL5Gwb0TD81XoxOFjRs3Ntjnd7/73VmEIiIico64Yxtmk0xdNDpRcLbzosVisW/jrERBRETk/NDoRKG+HahOnz7Ntm3beOWVV1i2bJlbAhMREWlKzfnyyOak0YlC586d623v1asXVVVVPPPMM7z99tsuByYiItKklCgY4tbbTPfu3ZsdO3a4c0gRERHxIJduM/1zlZWVrF27tlH7R4uIiHiMKgqGNDpRGD58eJ17PFitVo4dO0ZFRQWPPPKI24ITERFpKlqjYEyjE4UhQ4bU2x4UFMRVV13FZZdd5nJQIiIi0jw0OlG4/vrr6d+/P4GBgU0Rj4iIiDQjjV7MOHPmzHovkRQREflVsbn4MIlGVxR8fX3x8/NrilhERETOGa1RMKbRicKkSZN48sknycnJoVevXvY7SP5cbGysW4ITERERz2p0ovDUU08BkJ6eDuBwBYTNZsNisbBr1y43hSciItJEVFEwpNGJwuuvv94UcYiIiJw7uimUYYYShREjRrBs2TKioqIYPHhwU8ckIiIizYShROHQoUNUVlY2dSwiIiLnjBYzGuO2LZxFRER+VZQoGOLWm0KJiIjI+cVwReGBBx7A19e3wX4Wi4WPP/7YpaBERESamqYejDGcKERHR9O+ffumjEVEROTcUaJgSKMqCv369WvKWERERKSZ0WJGERExJ1UUDFGiICIipqQ1CsYYShQSEhJo165dU8ciIiJybmhnRsMMJQrPPvtsU8chIiIizZCmHkRExJxMUhFwlRIFERExJa1RMEY7M4qIiIhTqiiIiIg5qaJgiBIFERExHQuuTz1Y3BJJ86epBxEREXFKFQURETEnTT0YokRBRETMSYmCIZp6EBEREadUURAREVMyy2JEVylREBERc9LUgyFKFERExHxsbtiZ0SSJhtYoiIiIeEBxcTHJycnExcUxYMAA7r33Xvbu3Ws/vmvXLpKSkujfvz/Dhg0jIyPD4Xyr1UpaWhrx8fHExMQwYcIE8vLyHPo0NIYRShRERMScbC4+XHT//ffzww8/sGLFCt599138/f256667KCsr49ixY4wfP57u3buzfv16pkyZwuLFi1m/fr39/PT0dNasWcOcOXPIzMzEYrEwceJEKisrAQyNYYSmHkRExJw8OHVw7NgxunTpwv3330+vXr0AmDx5MjfeeCN79uxh8+bN+Pr6kpKSgo+PD5GRkeTl5bFixQoSExOprKxk1apVJCcnM3ToUAAWLVpEfHw8mzZtYvTo0axdu/YXxzBKFQUREZFzrF27dixcuNCeJBw9epSMjAzCwsLo2bMn2dnZxMbG4uPz0/f5uLg4cnNzKS4uJicnh9LSUuLi4uzHW7duTXR0NFu3bgVocAyjVFEQERFTcsdtpg8fPszYsWOdHs/KympwjCeeeML+7X/58uUEBgZSUFBA7969Hfp17NjR/pwFBQUAdOrUqU6f/Px8gAbHCA4ObjA2UEVBRETMysNrFM4YN24c69ev54YbbuCBBx5gx44dlJeX4+vr69DPz88PgIqKCsrKygDq7VNRUQHQ4BhGqaIgIiJylsLDww1VDX5Jz549AXjmmWf4+uuvefPNN/H397cvSjzjzId7YGAg/v7+AFRWVtp/PtMnICAAoMExjFJFQURETMlic+3hiuLiYj744ANqamrsbV5eXkRGRlJUVERYWBhFRUUO55z5PTQ01D7lUF+fsLAwgAbHMEqJgoiImJMHpx6Kior4wx/+wBdffGFvq6qqYufOnURGRhIbG8u2bdscEonNmzcTERFBcHAwUVFRBAUFsWXLFvvxkpISdu7cyaBBgwAaHMMoJQoiIiLnWFRUFFdccQVPP/002dnZ7N69m0ceeYSSkhLuuusuEhMTOXXqFLNmzWLv3r1s2LCB1atXM2nSJKB2bUJSUhKpqalkZWWRk5PD9OnTCQsLY+TIkQANjmGU1iiIiIgpueOqh7N+bouFF198kQULFjBt2jROnjzJoEGDeOuttwgPDwdg5cqVzJ07l4SEBEJCQpg5cyYJCQn2MaZOnUp1dTWzZ8+mvLyc2NhYMjIy7AsYg4ODGxzDCCUKIiJiPu64csHF81u1akVKSgopKSn1Hu/Xrx+ZmZlOz/f29iY5OZnk5GSnfRoawwglCiIiYk4muamTq7RGQURERJxSRUFEREzJk2sUfk2UKIiIiDkpUTBEUw8iIiLilCoKIiJiQjYsNg9f9vAroURBRETMyRyf8y7T1IOIiIg4pYqCiIiYjgXXr3qwuCWS5k+JgoiImE8z2Jnx10JTDyIiIuKUKgoiImJK2nDJGCUKIiJiTkoUDFGiICIipqSKgjFaoyAiIiJOqaIgIiLmpIqCIUoURETElDT1YIymHkRERMQpVRRERMScXL4plDkoURAREVPS1IMxmnoQERERp1RREBER89G9HgxToiB1XHvTYX439iCh4eUU5fvzwTud+eCdcMDChzv+Uae/teB10t6B7V+05bHx/QHwaWHlzsn7uer6Qtq0q+JQXgCZL3fj0792PKevReTnQjqU8tLiD3j62aF8822YvT24/WnuGfclgwYcxtvHxu49wax4bSBQ2+ehe9fQO/Kg03Gv/V1SnbYA/ypeTvuA7d+GsiDtMre/FnGdxerpCH4dlCiIg2sTD/PQ07t5783O/OeTDlwce5z7Ht+Dr18NG17ryvTbBzj0v6BnGDMWdobSlXy0tpO9Pfn5XVxy+Y+sWtiDw3kBDL+hkEdSd3K61Jtt/w4+1y9LhI4hp5j31CcEtaxyaA/wryJ13t+orvJi8fIhVFV5c8ct/+XZp7N4Lq0rAJkbr+ZoYb7DeZ3CTpH80Od89Lee9T7fpLuzCe1Y2jQvRuQcalaJQnp6Ops3b+aNN97wdCimNTKhgB1ftublZ3sBsH1LOzp3O81vbz/Mhte68t03bRz6tw4OgNOZ/OuvffjXR6EAXHTJceKvPcKT911M9qe1ScHX/2lHeNcyBl3xoxIFOacsFhsjr/qeiXdtq/f4mBt20aZVBfc8eD0/HgsEYPfeYJYu+JBePX4AoKCoA3t3V9vP8fKyMnniVr7f35blGYPqjBl7ySGuvPwAp0pbNMErErcxydSBq5rNYsbXXnuNtLQ0T4dhei18rZSedMwfS463oHXbqnr7J4zNBksAH2T+VGm4fOQR8g/425OEWhYeThrIy8/1aoqwRZyK6H6MKfdtYdPfe/DC4svrHL/i0gN8urmrPUkAOHY8gDvvTmTb9gvrHfO3v9lDzx7HWPLSEKqrvR2OBbWsYNrk/5CxegCnSn3d+2LErSw21x5m4fGKQmFhIbNmzWLbtm1ERER4OhzT2/h6F6bP+Y6rflvAln90ICqmhBE3FvLJ+6F1+l7Y/wT9hxzAEvQc5WU77e09ok6xf09Lho0u5LZJeXTudprDBwJ4Pa0Hn20KOZcvR4QjR1oy/v4bOVrckn4XFTgc8/a20vWCE2T9M4Lf3/E1v7l6L21aV7AzJ4RlK2I5s0bh5/z9qxh723ay/hnBd3s61Dk+eeJWfjjYhj//tRe3JO5oqpcl7qB9FAzxeKKwY8cO2rRpw/vvv8+yZcs4dOiQp0MytU//2pGYIcdJfj7H3pb973a8/FzdedjE8T9QXBREh9AbgJ8ShTbtqwjvVkbP6FOsTovg2BFfRt92mMcW7iBl8sX/U2kQaVonT/lx8pRfvceCgirx8bEx5oYc8guCeHFZHC1aWPn97duZ/8wmnlvSpc45v7l6Hy1bVrHm3YvqHLtsyAEuHXKQ+x76LWBx90sR8QiPJwrDhw9n+PDhbhvP28ebnv26um08s7n/0Y+J6H2UjW8N5MC+DoR3PcZ1iduZuzKXlQuHceaPX9v2pcQNL+bvf7mKkf186NLrp29eAS23EdzxNC88NpqD+2uTgsxV/eje5wPGz8jn+IkB9TyzGFXTVVeOnK3OF1T+3/+253RVGG3bnATA28eLVe/cTkVl7VTByrejeCo5g+uvq02YL+j2U3I75sY/899dkQS06k3PPj+NHdTyNNMfzOa9j4bRql1PWrWDFj7etG4dQM8+dSsTUr8WLbwb7uQGFlyfPjBLKmix2ZpP7eXRRx/l0KFDLi1mtNlsWCxm+c/nXrbKL7H9eBuW1nOwBN7yU3v537Edn4Sl7ctY/K+qbStdje3kc1hCPsXi7Vh+tR69EazFeHX8t2N7yVw4vQavsP82/YsRqYetYgu2Y2OxtHsDi98QbNaT2IouAf/f4tV2oUNf69EbwCsEr/YZP51ftQtb8Y1Y2qZh8f+NY/9jU8B2HEu7VZz5CLEdGQm+sVjazAO89bepGTlccJxbJq90aYy16fcQHtbWPQE1Yx6vKLjbkUPH+OPYdE+H8as08LJc7poCcyZ+QeHh3fZ2X78qUl+D99Jf5uM/fQbA5Mc34eUVwsbMjTz6yj08d+9KDu6pnf8d/9BpevU9weO3PsPPc+6bx29h4GU2Hrtqzrl8WecdVRTOXq8eB5g2CRY99yf2fL8dgGdnB7Lvv9+x8s1XHPrOnnGEklJvLrwM5j25nh/yirn2qv9w7XAfHnlgH1XVjv2XPf9XAGyFfR2ftPwQtvKNvPjyLez5XtXOhjwz/zY6dGzt6TDkZ867RKGmuoa93xzwdBi/StbK09w1BVq1yuGzb8Lt7QMv/xGAHdnl//fe2ujSrYg/vd3Fnhwc3FNgf9///id/BsRV0CpoO1993h6o3YCpZ1Qe33zRSv99XFRTpl1izlZgi9p/y4d++JG939VOM/xnaxiXD9lP0eH9lJz0B6BL+Ak6dviRz764mAsvgx/yitn7XQFJibns2duOXTuK64z94B+uq9P29Kx/sGdfe95c04+DhyyUlRfU6SOOqqpqztlzmenKBVecd4mCnL3vc1rx7791YOLMvQS1rua7b1rRredp7py8nz07gvg8q3aKoWOnCoJa13BgX2C94/z9g1Cuv/0QM5/fxWsvRnC00I8bkw7RIayCeTP61nuOiKe8lXkxlw35gXkpWby1th8+3lbGJ33NkaOBfP5FP26Z+FPfiG7H2fZ1p3rH2bOv7iLdqmovSk761XtMmoHmM/PerClREAcvzIzmtkl5jLrlMGMfrKAo359NG8N4e3k3aqprt91o26F2Qdipkvr/+dRUezFrYgzjHsrl91NzCWhZw75dQTx+dwz7drU6Z69FxIiCwlZMf+Ra7h73FTOnfYbVauHLrzvx8qpLaNvBcR+Etm3LOeXkCgqR85USBXFQXeXFm0sjeHOp8z0tdv+3NaP6DgOgZ7/6+5wqacGyZ3qz7JneTRClyNn55tuweu/LcOBgW56ae1Wd9rb/s03Cjbfe3qjnG3dvQqP6y7mlqQdjmlWi8Nxzz3k6BBERMQslCoY0my2cRUREpPlpVhUFERGRc0VTD8YoURAREXOyKlMwQomCiIiYjw3X1yiYJM/QGgUREREPOH78OE8++SRXXnklAwcO5Pbbbyc7O9t+fNeuXSQlJdG/f3+GDRtGRkaGw/lWq5W0tDTi4+OJiYlhwoQJ5OXlOfRpaAwjlCiIiIgpWWyuPVw1Y8YMtm/fzsKFC3n33Xfp27cvd999N/v27ePYsWOMHz+e7t27s379eqZMmcLixYtZv369/fz09HTWrFnDnDlzyMzMxGKxMHHiRCora/e6MTKGEZp6EBERc/Lgzox5eXl89tlnvPPOOwwcOBCAWbNm8a9//YsPPvgAf39/fH19SUlJwcfHh8jISPLy8lixYgWJiYlUVlayatUqkpOTGTp0KACLFi0iPj6eTZs2MXr0aNauXfuLYxilioKIiMg51q5dO1555RUuuugie5vFYsFms3HixAmys7OJjY3Fx+en7/NxcXHk5uZSXFxMTk4OpaWlxMXF2Y+3bt2a6Ohotm7dCtDgGEapoiAiIqbkjumDw4cPM3bsWKfHs7Ky6m1v3bq1vRJwxkcffcSBAwe44oorWLRoEb17O+5s27FjR/tzFhTU3mCsU6dOdfrk5+cDUFBQ8ItjBAcbuweJKgoiImJONhcfbrRt2zYef/xxRowYwfDhwykvL8fX1/FeI35+tfcZqaiooKysDKDePhUVFQANjmGUKgoiIiJnKTw83GnVwKiPP/6Yhx9+mJiYGBYuXAiAv7+/fVHiGWc+3AMDA/H3r70lemVlpf3nM30CAgIMjWGUKgoiImJKFpvNpYc7vPnmm0yZMoUrr7ySFStW2D/0w8LCKCoqcuh75vfQ0FD7lEN9fcLCwgyNYZQSBRERMR8bYHXx4WKu8Pbbb/PMM89w55138uKLLzpME8TGxrJt2zZqamrsbZs3byYiIoLg4GCioqIICgpiy5Yt9uMlJSXs3LmTQYMGGRrDKCUKIiIi51hubi7z5s1j5MiRTJo0ieLiYo4cOcKRI0c4efIkiYmJnDp1ilmzZrF37142bNjA6tWrmTRpElC7NiEpKYnU1FSysrLIyclh+vTphIWFMXLkSIAGxzBKaxRERMSE3DF9cPbn//Wvf6WqqopNmzaxadMmh2MJCQk899xzrFy5krlz55KQkEBISAgzZ84kISHB3m/q1KlUV1cze/ZsysvLiY2NJSMjw16ZCA4ObnAMI5QoiIiIOXnwXg333Xcf99133y/26devH5mZmU6Pe3t7k5ycTHJy8lmPYYQSBRERMScP7sz4a6I1CiIiIuKUKgoiImI6FlzfmdHilkiaPyUKIiJiTpp6MERTDyIiIuKUKgoiImJKFqunI/h1UKIgIiLmY8P1qQeTzFxo6kFEREScUkVBRETMySQVAVcpURAREVNy1x0gz3eaehARERGnVFEQERFzUkXBECUKIiJiTro80hAlCiIiYkpao2CM1iiIiIiIU6ooiIiI+WjDJcOUKIiIiAnZ3LCY0RyZgqYeRERExClVFERExJx01YMhShRERMSUdNWDMZp6EBEREadUURAREXNSRcEQJQoiImJOShQM0dSDiIiIOKWKgoiImJMqCoYoURAREfOx4frlkSbJM5QoiIiIKenySGO0RkFEREScUkVBRETMSRUFQ5QoiIiICdnAqptCGaGpBxEREXFKFQURETEnTT0YokRBRETMSYmCIZp6EBEREadUURAREfOx4XpFwSQFCSUKIiJiTi5f9WAOmnoQERERp1RREBERc7K5erMHc1CiICIi5qSrHgzR1IOIiJjQ/+3M6MrDjasZ09PTGTt2rEPbrl27SEpKon///gwbNoyMjAyH41arlbS0NOLj44mJiWHChAnk5eU1agwjlCiIiIh40GuvvUZaWppD27Fjxxg/fjzdu3dn/fr1TJkyhcWLF7N+/Xp7n/T0dNasWcOcOXPIzMzEYrEwceJEKisrDY9hhKYeRETEfJrB5ZGFhYXMmjWLbdu2ERER4XBs7dq1+Pr6kpKSgo+PD5GRkeTl5bFixQoSExOprKxk1apVJCcnM3ToUAAWLVpEfHw8mzZtYvTo0Q2OYZQqCiIiYk42m2sPF+3YsYM2bdrw/vvvExMT43AsOzub2NhYfHx++j4fFxdHbm4uxcXF5OTkUFpaSlxcnP1469atiY6OZuvWrYbGMEoVBRERkbN0+PDhOmsLfi4rK8vpseHDhzN8+PB6jxUUFNC7d2+Hto4dO9qfs6CgAIBOnTrV6ZOfn29ojODgYKex/ZwSBRERMadmfNVDeXk5vr6+Dm1+fn4AVFRUUFZWBlBvnxMnThgawyglCiIiYk5W1/dRCA8P/8Wqwdny9/e3L0o848yHe2BgIP7+/gBUVlbafz7TJyAgwNAYRmmNgoiISDMTFhZGUVGRQ9uZ30NDQ+1TDvX1CQsLMzSGUUoURETEnDy8mPGXxMbGsm3bNmpqauxtmzdvJiIiguDgYKKioggKCmLLli324yUlJezcuZNBgwYZGsMoJQoiImJOzThRSExM5NSpU8yaNYu9e/eyYcMGVq9ezaRJk4DatQlJSUmkpqaSlZVFTk4O06dPJywsjJEjRxoawyitURAREWlmgoODWblyJXPnziUhIYGQkBBmzpxJQkKCvc/UqVOprq5m9uzZlJeXExsbS0ZGhn0Bo5ExjFCiICIi5mOzuX6baTdWFZ577rk6bf369SMzM9PpOd7e3iQnJ5OcnOy0T0NjGKFEQURETMmmu0caokRBRETMydWKgkloMaOIiIg4pYqCiIiYUzPembE5UaIgIiLm5IadGc1AUw8iIiLilCoKIiJiPu7YNMkkUxdKFERExJRsmnowRFMPIiIi4pQqCiIiYk4mmTpwlRIFERExJ224ZIimHkRERMQpVRRERMScdK8HQ5QoiIiI+djA5vLdI90TSnOnREFEREzI5oaKgjkyBa1REBEREadUURAREVNyeerBJCw22/l1IWl1VQ1HDv3o6TBMo4WvDx3C23H08DGqKqs9HY45tFB+f660aOFNh46tOVpUQlVVjafDMYWQ0Nb4+Hifk+fK/77QpfM79Qh1UyTN23mXKIiIiIj7aI2CiIiIOKVEQURERJxSoiAiIiJOKVEQERERp5QoiIiIiFNKFERERMQpJQoiIiLilBIFERERcUqJgoiIiDilREFEREScUqIgIiIiTilREBEREaeUKIiIiIhTShTkrFitVtLS0oiPjycmJoYJEyaQl5fn6bBEmkR6ejpjx471dBgiHqFEQc5Keno6a9asYc6cOWRmZmKxWJg4cSKVlZWeDk3ErV577TXS0tI8HYaIxyhRkEarrKxk1apVTJkyhaFDhxIVFcWiRYsoLCxk06ZNng5PxC0KCwu55557WLx4MREREZ4OR8RjlChIo+Xk5FBaWkpcXJy9rXXr1kRHR7N161YPRibiPjt27KBNmza8//77xMTEeDocEY/x8XQA8utTUFAAQKdOnRzaO3bsSH5+vidCEnG74cOHM3z4cE+HIeJxqihIo5WVlQHg6+vr0O7n50dFRYUnQhIRkSaiREEazd/fH6DOwsWKigoCAgI8EZKIiDQRJQrSaGemHIqKihzai4qKCAsL80RIIiLSRJQoSKNFRUURFBTEli1b7G0lJSXs3LmTQYMGeTAyERFxNy1mlEbz9fUlKSmJ1NRU2rdvT+fOnZk/fz5hYWGMHDnS0+GJiIgbKVGQszJ16lSqq6uZPXs25eXlxMbGkpGRUWeBo4iI/LpZbDabzdNBiIiISPOkNQoiIiLilBIFERERcUqJgoiIiDilREFEREScUqIgIiIiTilREBEREaeUKIg0Y7p6WUQ8TYmCnLfGjh1Lnz59HB4XXXQRw4YN4+mnn+bEiRNN9twbNmygT58+HDx4EIAlS5bQp08fw+cXFBQwadIkDh065HIsBw8epE+fPmzYsMFpn0cffbTRt1Q+m3PqYyQ+EfEc7cwo57Xo6Gieeuop++9VVVXs2LGDhQsXsmvXLt555x0sFkuTx3HzzTcTHx9vuP/nn3/OP/7xD5544okmjEpEpGFKFOS8FhQURP/+/R3aYmNjKS0tJS0tje3bt9c53hTCwsJ0Z00R+VXS1IOY0kUXXQTA4cOHgdppiocffpipU6cycOBA7r33XgAqKip44YUXGDp0KBdddBHXX389H374ocNYVquV9PR0hg0bRkxMDJMnT64zrVHf1MOf//xnxowZQ0xMDMOGDWP+/PlUVlayYcMGHnvsMQBGjBjBo48+aj9n3bp1jB492j6FsmTJEqqrqx3G/dvf/sYNN9xAv379SEhIICcnp9HvT3l5OQsWLOCaa67hoosuYuDAgYwfP55du3bV6ZuZmcmwYcPo168f48aNY+fOnQ7HDx8+zIwZMxg8eDAxMTH19hGR5kuJgphSbm4uABdccIG97aOPPqJFixYsW7aM3//+99hsNh544AHWrFnD+PHjWb58OQMGDGD69Ols3LjRft78+fNZtmwZiYmJLF26lHbt2rFgwYJffP41a9YwY8YMLrzwQpYuXcqkSZN4++23SUlJYdiwYdx///0ALF26lMmTJwPw8ssv88QTT3DppZfy0ksvceedd7JixQqefPJJ+7iffPIJU6dOpVevXixdupTrrruO5OTkRr8/M2fO5N133+Xee+9l1apVPProo+zevZvp06c7LLAsKChgyZIlTJs2jYULF3LixAl+//vf8+OPPwLw448/ctttt7Fjxw6eeOIJFixYgNVq5c4772Tfvn2NjktEzj1NPch5zWazOXzjPnHiBF988QXLly+nf//+9soCgJeXF8888wyBgYEAfPbZZ3z66acsWrSIUaNGARAfH09ZWRmpqan89re/5fTp07zxxhv8/ve/Z8qUKfY+hYWFfPrpp/XGZLVaWbJkCSNHjmTu3Ln29oqKCv7f//t/BAUF0bVrVwAuvPBCunTpwsmTJ1m+fDm33nors2fPBuCKK66gbdu2zJ49m/Hjx9OrVy+WLVtG37597YnKlVdeCdBg4vJzlZWVlJaW8sQTT9hf9+DBgyktLeW5557jyJEjdOzYEYCamhqWLl1qn76JiYnh6quv5rXXXmPGjBmsXr2a48eP884779C5c2d7TKNGjWLx4sWkpaUZjktEPEMVBTmvbd26lb59+9ofl112GTNmzKBv374sXLjQYSFjly5d7EkCwObNm7FYLAwdOpTq6mr7Y/jw4Rw5coQ9e/bw9ddfU1VVxYgRIxye97rrrnMaU25uLkePHuXqq692aL/rrrt477336r1V91dffUVZWRnDhw+vEwvUJjXl5eXs2LGjUbHUx9fXl4yMDEaNGkVRURFbt24lMzOTv//970DtgtAzwsPDHdZ4hISE0L9/fz7//HOg9j288MILCQ0Ntcfs5eXFlVdeae8jIs2bKgpyXuvbty9PP/00ABaLBT8/Pzp16kRQUFCdvh06dHD4/fjx49hsNgYOHFjv2EVFRZSUlADQvn17h2MhISFOYzp+/DgAwcHBhl/HmXPOrJ2oL5YTJ05gs9nqxHLm239jfPrpp8ybN4/vv/+eli1b0qdPH1q2bAk47u3wv+8Z1L6u/Px8e9x5eXn07du33ucpKytrdGwicm4pUZDzWsuWLbn44ovP6txWrVoRGBjI66+/Xu/xbt268c033wBQXFxMjx497MfOfLDXp3Xr1gD2efyfn7Njx456r8I4c05qairdu3evc7xDhw60bdsWLy8vjh49Wmfcxjhw4AAPPPAAI0aM4OWXX7ZPg7z11lt1plPOJEo/d+TIEXuy0qpVKwYPHszMmTPrfa76qici0rxo6kHEicGDB3P69GlsNhsXX3yx/bFnzx6WLVtGdXU1AwYMwN/fn7/85S8O554p09enR48etGvXjqysLIf2P/3pT0ycOJGKigq8vBz/rxkTE0OLFi0oLCx0iKVFixYsWLCAgwcP4ufnx4ABA/jb3/7m8K3/k08+adTr/vbbb6moqGDSpEn2JAGwJwk/HzsvL4+8vDz77/n5+Xz11VcMGTIEqH0Pc3NziYiIcIj7/fffZ926dXh7ezcqNhE591RREHFi6NChxMbGMnnyZCZPnkxkZCTffPMNS5Ys4YorrrB/a548eTIvvvgiAQEBxMXF8c9//vMXEwVvb2+mTJnCH//4R1JSUhg5ciT79+/nxRdf5Pbbb6d9+/b2CsKmTZu48soriYyM5J577mHx4sWcOnWKIUOGUFhYyOLFi7FYLERFRQEwY8YMxo0bx4MPPsitt97K/v37Wb58eaNed9++ffHx8WH+/PlMmDDBfsnmP/7xDwBOnz5t7+vn58fkyZOZPn06NTU1LF68mLZt2zJu3Djgp3UXd911FxMmTKBdu3Z8+OGHrF271n4JqIg0b0oURJzw8vLilVdeYfHixbz88ssUFxcTGhrKXXfdxQMPPGDvN2nSJAIDA1m9ejWrV69mwIABPPLII6SkpDgd+8477yQwMJCMjAzeffddQkNDmTBhgn0NwpAhQ7jssstYsGABmzdv5pVXXmHatGmEhITw9ttvs3LlStq0acOll17KjBkzaNWqFQCDBg1ixYoVLFy4kAcffJAuXbowb9487rvvPsOvu1u3bixYsIClS5dy//3306ZNG/r3788bb7zB2LFjyc7Otu8J0adPH0aPHk1KSgonT57k0ksv5fHHH7cnUaGhoaxZs4YFCxaQkpJCRUUF3bt3Z+7cudx0002N/U8iIh5gsemuMyIiIuKE1iiIiIiIU0oURERExCklCiIiIuKUEgURERFxSomCiIiIOKVEQURERJxSoiAiIiJOKVEQERERp5QoiIiIiFNKFERERMQpJQoiIiLilBIFERERcer/A+KT8Z0ISp+qAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize your confusion matrix\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cnf_matrix)\n",
    "disp.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model's Performance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Accuracy**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8703703703703703"
      ]
     },
     "execution_count": 2142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "accuracy_score(y_test, final_model.predict(X_test_encoded))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Precision**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.764034687357371"
      ]
     },
     "execution_count": 2143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import precision_score\n",
    "\n",
    "precision_score(y_test, final_model.predict(X_test_encoded))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Recall**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6564705882352941"
      ]
     },
     "execution_count": 2144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import recall_score\n",
    "\n",
    "recall_score(y_test, final_model.predict(X_test_encoded))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ROC curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2162,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve, auc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2166,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_score = final_model.decision_function(X_test_encoded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-4.44352772,  0.35210908,  0.59113528, ..., -1.63022251,\n",
       "       -0.64017128, -2.93235613])"
      ]
     },
     "execution_count": 2167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2168,
   "metadata": {},
   "outputs": [],
   "source": [
    "fpr, tpr, thresholds = roc_curve(y_test, y_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2169,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC: 0.925657590024785\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('darkgrid', {'axes.facecolor': '0.9'})\n",
    "\n",
    "print('AUC: {}'.format(auc(fpr, tpr)))\n",
    "plt.figure(figsize=(10, 8))\n",
    "lw = 2\n",
    "plt.plot(fpr, tpr, color='darkorange',\n",
    "         lw=lw, label='ROC curve')\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.yticks([i/20.0 for i in range(21)])\n",
    "plt.xticks([i/20.0 for i in range(21)])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver operating characteristic (ROC) Curve')\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " The model is performing well because the ROC curve that hugs the upper left corner of the graph indicates a good model. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Random Forest Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2174,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.85\n",
      "\n",
      "Precision: 0.71\n",
      "\n",
      "Recall: 0.60\n",
      "\n",
      "AUC-ROC: 0.89\n"
     ]
    }
   ],
   "source": [
    "# Import the relevant class \n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "# Instantiate the classifier\n",
    "rf = RandomForestClassifier(random_state=42)\n",
    "\n",
    "# Train the classifier\n",
    "rf.fit(X_train_oversampled, y_train_oversampled)\n",
    "\n",
    "# Evaluate the model\n",
    "y_pred_rf = rf.predict(X_test_encoded)\n",
    "y_pred_rf_proba = rf.predict_proba(X_test_encoded)[:, 1]\n",
    "\n",
    "# Calculate the evaluation metrics\n",
    "accuracy = accuracy_score(y_test, y_pred_rf)\n",
    "precision = precision_score(y_test, y_pred_rf)\n",
    "recall = recall_score(y_test, y_pred_rf)\n",
    "roc_auc = roc_auc_score(y_test, y_pred_rf_proba)\n",
    "\n",
    "# Print the results\n",
    "print(f\"Accuracy: {accuracy:.2f}\")\n",
    "print(\"\")\n",
    "print(f\"Precision: {precision:.2f}\")\n",
    "print(\"\")\n",
    "print(f\"Recall: {recall:.2f}\")\n",
    "print(\"\")\n",
    "print(f\"AUC-ROC: {roc_auc:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Classification Modelling Recommendation "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Overall, the Logistic Regression Classification out-performed the Random Forest Classification."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Age Group**\n",
    "\n",
    "Age groups tend to have a vital role towards income earned. The early age groups have a low income since they are newbies to the indurstry while the middle age groups have experience and are energetic in the work-place. \n",
    "Real Estate  agencies should focus with people between 40-60 years of ages."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Gender**\n",
    "\n",
    "Men hold top positions in their indurstries of work thus make them attain high income. However, this does not mean women shold be ignored at any point."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Occupation**\n",
    "\n",
    "In matters towards income, occupation really matters. Top indurstrial positions make a higher income than other normal positions because they are the steers of respective indurstries."
   ]
  }
 ],
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