From feb00df21062c82a03cb7d9c840ceb1667bdbc9e Mon Sep 17 00:00:00 2001
From: "Mishra, Ritwik (PG/T - Comp Sci & Elec Eng)" <rm02120@surrey.ac.uk>
Date: Fri, 18 Apr 2025 01:04:50 +0000
Subject: [PATCH] Delete preprocessing_arpit.ipynb
---
.../preprocessing_arpit.ipynb | 1552 -----------------
1 file changed, 1552 deletions(-)
delete mode 100644 notebooks/induction logic programming (ILP)/preprocessing_arpit.ipynb
diff --git a/notebooks/induction logic programming (ILP)/preprocessing_arpit.ipynb b/notebooks/induction logic programming (ILP)/preprocessing_arpit.ipynb
deleted file mode 100644
index e435fab..0000000
--- a/notebooks/induction logic programming (ILP)/preprocessing_arpit.ipynb
+++ /dev/null
@@ -1,1552 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "75cdca4d",
- "metadata": {},
- "source": [
- "# Inductive Logic programming using Pygol \n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "bfa59e0b",
- "metadata": {},
- "source": [
- "Problem statement - \"Apply ILP to learn rules predicting income >$50K per year. The goal is to generate interpretable, rule-based models for socio-economic decision-making. \""
- ]
- },
- {
- "cell_type": "markdown",
- "id": "693861f6",
- "metadata": {},
- "source": [
- "## Exploratory Data Analysis (EDA) and Preprocessing the dataset"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "7ce4daec",
- "metadata": {},
- "source": [
- "### Loading the dataset "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 24,
- "id": "0e4c74ab",
- "metadata": {
- "scrolled": true
- },
- "outputs": [],
- "source": [
- "import pandas as pd\n",
- "\n",
- "# Load the raw dataset\n",
- "file_path = \"C:/Users/Arpit Mahapatra/Desktop/MLDM Coursework 2025/mlmavericks_coursework/data/raw/ILP_census_income_rawdata.csv\"\n",
- "df = pd.read_csv(file_path)\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 25,
- "id": "d4182c53",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>age</th>\n",
- " <th>workclass</th>\n",
- " <th>fnlwgt</th>\n",
- " <th>education</th>\n",
- " <th>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><=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><=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><=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><=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><=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": 25,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "df.head()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "0c24e28c",
- "metadata": {},
- "source": [
- "### Displaying each column's data type "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 26,
- "id": "62119ad4",
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "age int64\n",
- "workclass object\n",
- "fnlwgt int64\n",
- "education object\n",
- "education-num int64\n",
- "marital-status object\n",
- "occupation object\n",
- "relationship object\n",
- "race object\n",
- "sex object\n",
- "capital-gain int64\n",
- "capital-loss int64\n",
- "hours-per-week int64\n",
- "native-country object\n",
- "income object\n",
- "dtype: object\n"
- ]
- }
- ],
- "source": [
- "print(df.dtypes)\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 27,
- "id": "d486feaf",
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "age 0\n",
- "workclass 963\n",
- "fnlwgt 0\n",
- "education 0\n",
- "education-num 0\n",
- "marital-status 0\n",
- "occupation 966\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 274\n",
- "income 0\n",
- "dtype: int64"
- ]
- },
- "execution_count": 27,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "df.isnull().sum()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "508a0eed",
- "metadata": {},
- "source": [
- "### Checking for duplicate records in the dataset and cleaning the dataset"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 28,
- "id": "4ce9b352",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " age workclass fnlwgt education education-num \\\n",
- "4881 25 Private 308144 Bachelors 13 \n",
- "5104 90 Private 52386 Some-college 10 \n",
- "9171 21 Private 250051 Some-college 10 \n",
- "11631 20 Private 107658 Some-college 10 \n",
- "13084 25 Private 195994 1st-4th 2 \n",
- "15059 21 Private 243368 Preschool 1 \n",
- "17040 46 Private 173243 HS-grad 9 \n",
- "18555 30 Private 144593 HS-grad 9 \n",
- "18698 19 Private 97261 HS-grad 9 \n",
- "21318 19 Private 138153 Some-college 10 \n",
- "21490 19 Private 146679 Some-college 10 \n",
- "21875 49 Private 31267 7th-8th 4 \n",
- "22300 25 Private 195994 1st-4th 2 \n",
- "22367 44 Private 367749 Bachelors 13 \n",
- "22494 49 Self-emp-not-inc 43479 Some-college 10 \n",
- "25872 23 Private 240137 5th-6th 3 \n",
- "26313 28 Private 274679 Masters 14 \n",
- "28230 27 Private 255582 HS-grad 9 \n",
- "28522 42 Private 204235 Some-college 10 \n",
- "28846 39 Private 30916 HS-grad 9 \n",
- "29157 38 Private 207202 HS-grad 9 \n",
- "30845 46 Private 133616 Some-college 10 \n",
- "31993 19 Private 251579 Some-college 10 \n",
- "32404 35 Private 379959 HS-grad 9 \n",
- "33425 24 Private 194630 Bachelors 13 \n",
- "43750 37 Private 52870 Bachelors 13 \n",
- "43773 29 Private 36440 Bachelors 13 \n",
- "46409 30 Private 180317 Assoc-voc 11 \n",
- "48521 18 Self-emp-inc 378036 12th 8 \n",
- "\n",
- " marital-status occupation relationship \\\n",
- "4881 Never-married Craft-repair Not-in-family \n",
- "5104 Never-married Other-service Not-in-family \n",
- "9171 Never-married Prof-specialty Own-child \n",
- "11631 Never-married Tech-support Not-in-family \n",
- "13084 Never-married Priv-house-serv Not-in-family \n",
- "15059 Never-married Farming-fishing Not-in-family \n",
- "17040 Married-civ-spouse Craft-repair Husband \n",
- "18555 Never-married Other-service Not-in-family \n",
- "18698 Never-married Farming-fishing Not-in-family \n",
- "21318 Never-married Adm-clerical Own-child \n",
- "21490 Never-married Exec-managerial Own-child \n",
- "21875 Married-civ-spouse Craft-repair Husband \n",
- "22300 Never-married Priv-house-serv Not-in-family \n",
- "22367 Never-married Prof-specialty Not-in-family \n",
- "22494 Married-civ-spouse Craft-repair Husband \n",
- "25872 Never-married Handlers-cleaners Not-in-family \n",
- "26313 Never-married Prof-specialty Not-in-family \n",
- "28230 Never-married Machine-op-inspct Not-in-family \n",
- "28522 Married-civ-spouse Prof-specialty Husband \n",
- "28846 Married-civ-spouse Craft-repair Husband \n",
- "29157 Married-civ-spouse Machine-op-inspct Husband \n",
- "30845 Divorced Adm-clerical Unmarried \n",
- "31993 Never-married Other-service Own-child \n",
- "32404 Divorced Other-service Not-in-family \n",
- "33425 Never-married Prof-specialty Not-in-family \n",
- "43750 Married-civ-spouse Exec-managerial Husband \n",
- "43773 Never-married Adm-clerical Not-in-family \n",
- "46409 Divorced Machine-op-inspct Not-in-family \n",
- "48521 Never-married Farming-fishing Own-child \n",
- "\n",
- " race sex capital-gain capital-loss hours-per-week \\\n",
- "4881 White Male 0 0 40 \n",
- "5104 Asian-Pac-Islander Male 0 0 35 \n",
- "9171 White Female 0 0 10 \n",
- "11631 White Female 0 0 10 \n",
- "13084 White Female 0 0 40 \n",
- "15059 White Male 0 0 50 \n",
- "17040 White Male 0 0 40 \n",
- "18555 Black Male 0 0 40 \n",
- "18698 White Male 0 0 40 \n",
- "21318 White Female 0 0 10 \n",
- "21490 Black Male 0 0 30 \n",
- "21875 White Male 0 0 40 \n",
- "22300 White Female 0 0 40 \n",
- "22367 White Female 0 0 45 \n",
- "22494 White Male 0 0 40 \n",
- "25872 White Male 0 0 55 \n",
- "26313 White Male 0 0 50 \n",
- "28230 White Female 0 0 40 \n",
- "28522 White Male 0 0 40 \n",
- "28846 White Male 0 0 40 \n",
- "29157 White Male 0 0 48 \n",
- "30845 White Female 0 0 40 \n",
- "31993 White Male 0 0 14 \n",
- "32404 White Female 0 0 40 \n",
- "33425 White Male 0 0 35 \n",
- "43750 White Male 0 0 40 \n",
- "43773 White Female 0 0 40 \n",
- "46409 White Male 0 0 40 \n",
- "48521 White Male 0 0 10 \n",
- "\n",
- " native-country income \n",
- "4881 Mexico <=50K \n",
- "5104 United-States <=50K \n",
- "9171 United-States <=50K \n",
- "11631 United-States <=50K \n",
- "13084 Guatemala <=50K \n",
- "15059 Mexico <=50K \n",
- "17040 United-States <=50K \n",
- "18555 ? <=50K \n",
- "18698 United-States <=50K \n",
- "21318 United-States <=50K \n",
- "21490 United-States <=50K \n",
- "21875 United-States <=50K \n",
- "22300 Guatemala <=50K \n",
- "22367 Mexico <=50K \n",
- "22494 United-States <=50K \n",
- "25872 Mexico <=50K \n",
- "26313 United-States <=50K \n",
- "28230 United-States <=50K \n",
- "28522 United-States >50K \n",
- "28846 United-States <=50K \n",
- "29157 United-States >50K \n",
- "30845 United-States <=50K \n",
- "31993 United-States <=50K \n",
- "32404 United-States <=50K \n",
- "33425 United-States <=50K. \n",
- "43750 United-States <=50K. \n",
- "43773 United-States <=50K. \n",
- "46409 United-States <=50K. \n",
- "48521 United-States <=50K. \n"
- ]
- }
- ],
- "source": [
- "duplicates = df[df.duplicated()]\n",
- "\n",
- "print(duplicates)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 29,
- "id": "59ce8da8",
- "metadata": {},
- "outputs": [],
- "source": [
- "# Removing the duplicates\n",
- "\n",
- "df_no_duplicates = df.drop_duplicates()\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "48eb4505",
- "metadata": {},
- "source": [
- "### Checking for null values"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 30,
- "id": "817af8f1",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "age 0\n",
- "workclass 963\n",
- "fnlwgt 0\n",
- "education 0\n",
- "education-num 0\n",
- "marital-status 0\n",
- "occupation 966\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 274\n",
- "income 0\n",
- "dtype: int64"
- ]
- },
- "execution_count": 30,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "df.isnull().sum()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "369da037",
- "metadata": {},
- "source": [
- "### Handling the null values and replacing them with \"Unknown\""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 31,
- "id": "1a5df3f2",
- "metadata": {},
- "outputs": [],
- "source": [
- "df.fillna(\"unknown\", inplace=True)\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "f609b3ad",
- "metadata": {},
- "source": [
- "### Displaying all the unique values and their frequency in the target variable "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 32,
- "id": "97daa78c",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<=50K 24720\n",
- "<=50K. 12435\n",
- ">50K 7841\n",
- ">50K. 3846\n",
- "Name: income, dtype: int64"
- ]
- },
- "execution_count": 32,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "df[\"income\"].value_counts()\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "c8eec534",
- "metadata": {},
- "source": [
- "### Removing \".\" from the values in target variable to resolve string formatting error and clean the dirty data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 33,
- "id": "c51a5c4b",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<=50K 37155\n",
- ">50K 11687\n",
- "Name: income, dtype: int64"
- ]
- },
- "execution_count": 33,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "df[\"income\"] = df[\"income\"].str.strip().str.rstrip(\".\")\n",
- "\n",
- "df[\"income\"].value_counts()\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "53c1d058",
- "metadata": {},
- "source": [
- "### Encoding the Target variable "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 34,
- "id": "4aef7008",
- "metadata": {},
- "outputs": [],
- "source": [
- "df[\"income\"] = df[\"income\"].map({\"<=50K\": 0, \">50K\": 1})"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "7fc1772b",
- "metadata": {},
- "source": [
- "### Checking the correlation between the target variable and other numerical columns "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 35,
- "id": "5b9f181d",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " age fnlwgt education-num capital-gain capital-loss \\\n",
- "age 1.000000 -0.076628 0.030940 0.077229 0.056944 \n",
- "fnlwgt -0.076628 1.000000 -0.038761 -0.003706 -0.004366 \n",
- "education-num 0.030940 -0.038761 1.000000 0.125146 0.080972 \n",
- "capital-gain 0.077229 -0.003706 0.125146 1.000000 -0.031441 \n",
- "capital-loss 0.056944 -0.004366 0.080972 -0.031441 1.000000 \n",
- "hours-per-week 0.071558 -0.013519 0.143689 0.082157 0.054467 \n",
- "income 0.230369 -0.006339 0.332613 0.223013 0.147554 \n",
- "\n",
- " hours-per-week income \n",
- "age 0.071558 0.230369 \n",
- "fnlwgt -0.013519 -0.006339 \n",
- "education-num 0.143689 0.332613 \n",
- "capital-gain 0.082157 0.223013 \n",
- "capital-loss 0.054467 0.147554 \n",
- "hours-per-week 1.000000 0.227687 \n",
- "income 0.227687 1.000000 \n"
- ]
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 720x576 with 2 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "import pandas as pd\n",
- "import matplotlib.pyplot as plt\n",
- "import seaborn as sns\n",
- "\n",
- "# Select numeric columns, including 'income'\n",
- "numeric_df = df.select_dtypes(include=['int64', 'float64'])\n",
- "\n",
- "# Compute correlation matrix\n",
- "correlation_matrix = df.corr()\n",
- "print (correlation_matrix)\n",
- "# Create the heatmap using seaborn for better visuals\n",
- "plt.figure(figsize=(10, 8))\n",
- "sns.heatmap(correlation_matrix, annot=True, fmt=\".2f\", cmap=\"coolwarm\", square=True,\n",
- " cbar_kws={\"label\": \"Correlation Coefficient\"})\n",
- "plt.title(\"Correlation Matrix Heatmap\")\n",
- "plt.xticks(rotation=45)\n",
- "plt.tight_layout()\n",
- "plt.show()\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "066427e2",
- "metadata": {},
- "source": [
- "As it can be clearly seen in the correlation matrix and heatmap, we can come up with the following conclusions:\n",
- "\n",
- "1) Columns like fnlwgt, capital gain and capital loss have less or no importance while tackling the problem statement hence we should drop these columns.\n",
- "\n",
- "2) The education_num column altough having a positive correlation with income have less value while defining rules for ILP using Pygol hence can be dropped as they add retunduncy because they are the numeric representations of education column.\n",
- "\n",
- "3) While dealing with features like age and hours per week, it is better for us to convert these numeric features into categorial columns so that it is better suitable to help define rules in ILP.\n",
- "\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "a423ebe7",
- "metadata": {},
- "source": [
- "### Removing unecessary columns "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 36,
- "id": "964bcbdd",
- "metadata": {},
- "outputs": [],
- "source": [
- "columns_to_keep = [\n",
- " \"age\", \"workclass\", \"education\", \"marital-status\",\n",
- " \"occupation\", \"relationship\", \"race\", \"sex\",\n",
- " \"hours-per-week\", \"native-country\", \"income\" \n",
- "]\n",
- "\n",
- "df = df[columns_to_keep]\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "b6d6e110",
- "metadata": {},
- "source": [
- "### Discretizing 'age' into age groups "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 37,
- "id": "2bf01c3d",
- "metadata": {},
- "outputs": [],
- "source": [
- "def age_group(age):\n",
- " if age < 30:\n",
- " return \"young\"\n",
- " elif 30 <= age <= 55:\n",
- " return \"middle\"\n",
- " else:\n",
- " return \"senior\"\n",
- "df[\"age\"] = df[\"age\"].apply(age_group)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "f381014c",
- "metadata": {},
- "source": [
- "### Discritizing 'hours-per-week' into hour groups"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 38,
- "id": "18153522",
- "metadata": {},
- "outputs": [],
- "source": [
- "def hours_group(hours):\n",
- " if hours < 25:\n",
- " return \"low\"\n",
- " elif 25 <= hours <= 40:\n",
- " return \"average\"\n",
- " else:\n",
- " return \"high\"\n",
- "df[\"hours-per-week\"] = df[\"hours-per-week\"].apply(hours_group)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "78ffed21",
- "metadata": {},
- "source": [
- "### Checking for dirty data in categorical features "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 39,
- "id": "0b83ab5d",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Column: age\n",
- "Unique Values (3):\n",
- "['middle', 'young', 'senior']\n",
- "----------------------------------------\n",
- "Column: workclass\n",
- "Unique Values (10):\n",
- "['State-gov', 'Self-emp-not-inc', 'Private', 'Federal-gov', 'Local-gov', '?', 'Self-emp-inc', 'Without-pay', 'Never-worked', 'unknown']\n",
- "----------------------------------------\n",
- "Column: education\n",
- "Unique Values (16):\n",
- "['Bachelors', 'HS-grad', '11th', 'Masters', '9th', 'Some-college', 'Assoc-acdm', 'Assoc-voc', '7th-8th', 'Doctorate', 'Prof-school', '5th-6th', '10th', '1st-4th', 'Preschool', '12th']\n",
- "----------------------------------------\n",
- "Column: marital-status\n",
- "Unique Values (7):\n",
- "['Never-married', 'Married-civ-spouse', 'Divorced', 'Married-spouse-absent', 'Separated', 'Married-AF-spouse', 'Widowed']\n",
- "----------------------------------------\n",
- "Column: occupation\n",
- "Unique Values (16):\n",
- "['Adm-clerical', 'Exec-managerial', 'Handlers-cleaners', 'Prof-specialty', 'Other-service', 'Sales', 'Craft-repair', 'Transport-moving', 'Farming-fishing', 'Machine-op-inspct', 'Tech-support', '?', 'Protective-serv', 'Armed-Forces', 'Priv-house-serv', 'unknown']\n",
- "----------------------------------------\n",
- "Column: relationship\n",
- "Unique Values (6):\n",
- "['Not-in-family', 'Husband', 'Wife', 'Own-child', 'Unmarried', 'Other-relative']\n",
- "----------------------------------------\n",
- "Column: race\n",
- "Unique Values (5):\n",
- "['White', 'Black', 'Asian-Pac-Islander', 'Amer-Indian-Eskimo', 'Other']\n",
- "----------------------------------------\n",
- "Column: sex\n",
- "Unique Values (2):\n",
- "['Male', 'Female']\n",
- "----------------------------------------\n",
- "Column: hours-per-week\n",
- "Unique Values (3):\n",
- "['average', 'low', 'high']\n",
- "----------------------------------------\n",
- "Column: native-country\n",
- "Unique Values (43):\n",
- "['United-States', 'Cuba', 'Jamaica', 'India', '?', 'Mexico', 'South', 'Puerto-Rico', 'Honduras', 'England', 'Canada', 'Germany', 'Iran', 'Philippines', 'Italy', 'Poland', 'Columbia', 'Cambodia', 'Thailand', 'Ecuador', 'Laos', 'Taiwan', 'Haiti', 'Portugal', 'Dominican-Republic', 'El-Salvador', 'France', 'Guatemala', 'China', 'Japan', 'Yugoslavia', 'Peru', 'Outlying-US(Guam-USVI-etc)', 'Scotland', 'Trinadad&Tobago', 'Greece', 'Nicaragua', 'Vietnam', 'Hong', 'Ireland', 'Hungary', 'Holand-Netherlands', 'unknown']\n",
- "----------------------------------------\n"
- ]
- }
- ],
- "source": [
- "categorical_cols = df.select_dtypes(include=['object', 'category'])\n",
- "\n",
- "\n",
- "for col in categorical_cols.columns:\n",
- " print(f\"Column: {col}\")\n",
- " print(f\"Unique Values ({len(categorical_cols[col].unique())}):\")\n",
- " print(categorical_cols[col].unique().tolist())\n",
- " print(\"-\" * 40)\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "a2f34b03",
- "metadata": {},
- "source": [
- "As it can be noted that quite a few categorical columns have dirty data like '?', we should remove them and replace them with 'unkown' to have a uniform and clean dataset "
- ]
- },
- {
- "cell_type": "markdown",
- "id": "85bc07ff",
- "metadata": {},
- "source": [
- "### Replacing '?' with 'unknown' "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 40,
- "id": "ab8e0293",
- "metadata": {},
- "outputs": [],
- "source": [
- "df.replace('?', 'unknown', inplace=True)\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "405a956d",
- "metadata": {},
- "source": [
- "Lets see how this affected our dataset "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 41,
- "id": "e8e99f23",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Column: age\n",
- "Unique Values (3):\n",
- "['middle', 'young', 'senior']\n",
- "----------------------------------------\n",
- "Column: workclass\n",
- "Unique Values (9):\n",
- "['State-gov', 'Self-emp-not-inc', 'Private', 'Federal-gov', 'Local-gov', 'unknown', 'Self-emp-inc', 'Without-pay', 'Never-worked']\n",
- "----------------------------------------\n",
- "Column: education\n",
- "Unique Values (16):\n",
- "['Bachelors', 'HS-grad', '11th', 'Masters', '9th', 'Some-college', 'Assoc-acdm', 'Assoc-voc', '7th-8th', 'Doctorate', 'Prof-school', '5th-6th', '10th', '1st-4th', 'Preschool', '12th']\n",
- "----------------------------------------\n",
- "Column: marital-status\n",
- "Unique Values (7):\n",
- "['Never-married', 'Married-civ-spouse', 'Divorced', 'Married-spouse-absent', 'Separated', 'Married-AF-spouse', 'Widowed']\n",
- "----------------------------------------\n",
- "Column: occupation\n",
- "Unique Values (15):\n",
- "['Adm-clerical', 'Exec-managerial', 'Handlers-cleaners', 'Prof-specialty', 'Other-service', 'Sales', 'Craft-repair', 'Transport-moving', 'Farming-fishing', 'Machine-op-inspct', 'Tech-support', 'unknown', 'Protective-serv', 'Armed-Forces', 'Priv-house-serv']\n",
- "----------------------------------------\n",
- "Column: relationship\n",
- "Unique Values (6):\n",
- "['Not-in-family', 'Husband', 'Wife', 'Own-child', 'Unmarried', 'Other-relative']\n",
- "----------------------------------------\n",
- "Column: race\n",
- "Unique Values (5):\n",
- "['White', 'Black', 'Asian-Pac-Islander', 'Amer-Indian-Eskimo', 'Other']\n",
- "----------------------------------------\n",
- "Column: sex\n",
- "Unique Values (2):\n",
- "['Male', 'Female']\n",
- "----------------------------------------\n",
- "Column: hours-per-week\n",
- "Unique Values (3):\n",
- "['average', 'low', 'high']\n",
- "----------------------------------------\n",
- "Column: native-country\n",
- "Unique Values (42):\n",
- "['United-States', 'Cuba', 'Jamaica', 'India', 'unknown', 'Mexico', 'South', 'Puerto-Rico', 'Honduras', 'England', 'Canada', 'Germany', 'Iran', 'Philippines', 'Italy', 'Poland', 'Columbia', 'Cambodia', 'Thailand', 'Ecuador', 'Laos', 'Taiwan', 'Haiti', 'Portugal', 'Dominican-Republic', 'El-Salvador', 'France', 'Guatemala', 'China', 'Japan', 'Yugoslavia', 'Peru', 'Outlying-US(Guam-USVI-etc)', 'Scotland', 'Trinadad&Tobago', 'Greece', 'Nicaragua', 'Vietnam', 'Hong', 'Ireland', 'Hungary', 'Holand-Netherlands']\n",
- "----------------------------------------\n"
- ]
- }
- ],
- "source": [
- "categorical_cols = df.select_dtypes(include=['object', 'category'])\n",
- "\n",
- "\n",
- "for col in categorical_cols.columns:\n",
- " print(f\"Column: {col}\")\n",
- " print(f\"Unique Values ({len(categorical_cols[col].unique())}):\")\n",
- " print(categorical_cols[col].unique().tolist())\n",
- " print(\"-\" * 40)\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "370797d2",
- "metadata": {},
- "source": [
- "Now the categorical features data is much more cleaner and ideal for training ILP models."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "47614495",
- "metadata": {},
- "source": [
- "Finally lets normalise all the strings across the dataset so that there are no errors while identifying same values"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "00c14391",
- "metadata": {},
- "source": [
- "### Normalising the strings across the dataframe for a cleaner data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 42,
- "id": "19a7e465",
- "metadata": {},
- "outputs": [],
- "source": [
- "df = df.applymap(lambda x: x.strip().lower() if isinstance(x, str) else x)\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 43,
- "id": "8e19c8eb",
- "metadata": {},
- "outputs": [],
- "source": [
- "# Saving the changes in a new file \n",
- "df.to_csv('C:/Users/Arpit Mahapatra/Desktop/MLDM Coursework 2025/mlmavericks_coursework/data/processed/ILP_preprocessed_cencus_income_data.csv', index=False)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "04da5c6b",
- "metadata": {},
- "source": [
- "### Distribution of categorical columns in comparison to target variable "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 44,
- "id": "8aa10cbd",
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<Figure size 720x288 with 0 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 1080x576 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- },
- {
- "data": {
- "text/plain": [
- "<Figure size 720x288 with 0 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
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\n",
- "text/plain": [
- "<Figure size 1080x576 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- },
- {
- "data": {
- "text/plain": [
- "<Figure size 720x288 with 0 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 1080x576 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- },
- {
- "data": {
- "text/plain": [
- "<Figure size 720x288 with 0 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
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yy8D4VkkuaW/7cpK048slOa4dPzfJpLH6XSRJkiRJWhxUVdcljNj81jqWMzCOAHadfTDJBsBLgRsHxjYD9gU2b+/ztSRLtzd/HTgA2KT9GXrM/YG7q2pj4EvA58bkt5AkSZIkaTGw/PLLc+eddy4WIUZVceedd7L88suP+D5j1sSzqn49h1kRXwI+BpwwMLY7cGxVPQhcl2QqsE2S64FVquocgCRHAXsAJ7f3+VR7/x8AX02SWhz+l5IkSZIkaZRNnDiRadOmMX369K5LGZHll1+eiRMnjvj4RboLSZJXATdX1UXtSpAh6wO/G7g+rR17uL08+/jQfW4CqKqZSe4F1gTuGOZ5D6CZxcGGG244Kr+LJEmSJEl9sswyyzB58uSuyxgzi6yJZ5IVgX8C/nW4m4cZq7mMz+0+jx+sOqyqplTVlLXXXnsk5UqSJEmSpB5ZlLuQPAWYDFzULg2ZCPwhyRNpZlZsMHDsROCWdnziMOMM3ifJBGBV4K4xrF+SJEmSJHVkkQUYVXVJVa1TVZOqahJNAPGcqvozcCKwb7uzyGSaZp3nVdWtwP1Jtmt3H3kzj/bOOBHYr728F3CG/S8kSZIkSVoyjeU2qscA5wBPSzItyf5zOraqLgOOBy4HTgHeU1Wz2pvfBXwLmApcQ9PAE+BwYM224eeHgQPH5BeRJEmSJEmdG8tdSF43j9snzXb9IOCgYY67ANhimPEZwN4LV6UkSZIkSVocLMoeGJIkSZIkSQvEAEOSJEmSJPWeAYYkSZIkSeo9AwxJkiRJktR7BhiSJEmSJKn3DDAkSZIkSVLvGWBIkiRJkqTeM8CQJEmSJEm9Z4AhSZIkSZJ6zwBDkiRJkiT1ngGGJEmSJEnqPQMMSZIkSZLUewYYkiRJkiSp9wwwJEmSJElS7xlgSJIkSZKk3jPAkCRJkiRJvWeAIUmSJEmSes8AQ5IkSZIk9Z4BhiRJkiRJ6j0DDEmSJEmS1HsGGJIkSZIkqfcMMCRJkiRJUu8ZYEiSJEmSpN4zwJAkSZIkSb1ngCFJkiRJknrPAEOSJEmSJPWeAYYkSZIkSeo9AwxJkiRJktR7BhiSJEmSJKn3DDAkSZIkSVLvGWBIkiRJkqTeM8CQJEmSJEm9Z4AhSZIkSZJ6zwBDkiRJkiT13oSuC5Akja5JB/6s6xIW2vUH79Z1CZIkSeoZZ2BIkiRJkqTeM8CQJEmSJEm9Z4AhSZIkSZJ6zwBDkiRJkiT1ngGGJEmSJEnqPQMMSZIkSZLUe2MWYCT5dpLbk1w6MPafSa5McnGSHydZbeC2TySZmuSqJLsMjG+V5JL2ti8nSTu+XJLj2vFzk0waq99FkiRJkiR1ayxnYBwB7Drb2GnAFlX1TOBPwCcAkmwG7Ats3t7na0mWbu/zdeAAYJP2Z+gx9wfurqqNgS8Bnxuz30SSJEmSJHVqzAKMqvo1cNdsY6dW1cz26u+Aie3l3YFjq+rBqroOmApsk2Q9YJWqOqeqCjgK2GPgPke2l38AvHhodoYkSZIkSVqydNkD423Aye3l9YGbBm6b1o6t316effwx92lDkXuBNYd7oiQHJLkgyQXTp08ftV9AkiRJkiQtGp0EGEn+CZgJHD00NMxhNZfxud3n8YNVh1XVlKqasvbaa89vuZIkSZIkqWOLPMBIsh/wCuAN7bIQaGZWbDBw2ETglnZ84jDjj7lPkgnAqsy2ZEWSJEmSJC0ZFmmAkWRX4OPAq6rqbwM3nQjs2+4sMpmmWed5VXUrcH+S7dr+Fm8GThi4z37t5b2AMwYCEUmSJEmStASZMFYPnOQY4IXAWkmmAZ+k2XVkOeC0tt/m76rqnVV1WZLjgctplpa8p6pmtQ/1LpodTVag6Zkx1DfjcOA7SabSzLzYd6x+F0mSJEmS1K0xCzCq6nXDDB8+l+MPAg4aZvwCYIthxmcAey9MjZIkSZIkafHQ5S4kkiRJkiRJI2KAIUmSJEmSes8AQ5IkSZIk9Z4BhiRJkiRJ6j0DDEmSJEmS1HsGGJIkSZIkqfcMMCRJkiRJUu8ZYEiSJEmSpN4zwJAkSZIkSb1ngCFJkiRJknrPAEOSJEmSJPWeAYYkSZIkSeo9AwxJkiRJktR7BhiSJEmSJKn3DDAkSZIkSVLvGWBIkiRJkqTeM8CQJEmSJEm9Z4AhSZIkSZJ6zwBDkiRJkiT1ngGGJEmSJEnqPQMMSZIkSZLUewYYkiRJkiSp9wwwJEmSJElS7xlgSJIkSZKk3jPAkCRJkiRJvWeAIUmSJEmSes8AQ5IkSZIk9Z4BhiRJkiRJ6j0DDEmSJEmS1HsGGJIkSZIkqfcMMCRJkiRJUu8ZYEiSJEmSpN4zwJAkSZIkSb1ngCFJkiRJknrPAEOSJEmSJPWeAYYkSZIkSeo9AwxJkiRJktR7BhiSJEmSJKn3DDAkSZIkSVLvGWBIkiRJkqTeM8CQJEmSJEm9N2YBRpJvJ7k9yaUDY2skOS3J1e2/qw/c9okkU5NclWSXgfGtklzS3vblJGnHl0tyXDt+bpJJY/W7SJIkSZKkbo3lDIwjgF1nGzsQOL2qNgFOb6+TZDNgX2Dz9j5fS7J0e5+vAwcAm7Q/Q4+5P3B3VW0MfAn43Jj9JpIkSZIkqVNjFmBU1a+Bu2Yb3h04sr18JLDHwPixVfVgVV0HTAW2SbIesEpVnVNVBRw1232GHusHwIuHZmdIkiRJkqQly4RF/HzrVtWtAFV1a5J12vH1gd8NHDetHXu4vTz7+NB9bmofa2aSe4E1gTtmf9IkB9DM4mDDDTcctV9GkjRGPrVq1xUsvE/d23UFkiRJS5S+NPEcbuZEzWV8bvd5/GDVYVU1paqmrL322gtYoiRJkiRJ6sqiDjBua5eF0P57ezs+Ddhg4LiJwC3t+MRhxh9znyQTgFV5/JIVSZIkSZK0BFjUAcaJwH7t5f2AEwbG9213FplM06zzvHa5yf1Jtmv7W7x5tvsMPdZewBltnwxJkiRJkrSEGbMeGEmOAV4IrJVkGvBJ4GDg+CT7AzcCewNU1WVJjgcuB2YC76mqWe1DvYtmR5MVgJPbH4DDge8kmUoz82LfsfpdJEmSJElSt8YswKiq183hphfP4fiDgIOGGb8A2GKY8Rm0AYgkSZIkSVqy9aWJpyRJkiRJ0hwZYEiSJEmSpN4zwJAkSZIkSb1ngCFJkiRJknrPAEOSJEmSJPWeAYYkSZIkSeo9AwxJkiRJktR7BhiSJEmSJKn3DDAkSZIkSVLvGWBIkiRJkqTeM8CQJEmSJEm9Z4AhSZIkSZJ6zwBDkiRJkiT1ngGGJEmSJEnqPQMMSZIkSZLUewYYkiRJkiSp9wwwJEmSJElS7xlgSJIkSZKk3jPAkCRJkiRJvWeAIUmSJEmSes8AQ5IkSZIk9Z4BhiRJkiRJ6j0DDEmSJEmS1HsGGJIkSZIkqfcMMCRJkiRJUu8ZYEiSJEmSpN4zwJAkSZIkSb1ngCFJkiRJknrPAEOSJEmSJPXeiAKMJDuMZEySJEmSJGksjHQGxldGOCZJkiRJkjTqJsztxiTPBbYH1k7y4YGbVgGWHsvCJEmSJEmShsw1wACWBVZqj1t5YPw+YK+xKkqSJEmSJGnQXAOMqjoTODPJEVV1wyKqSZIkSZIk6THmNQNjyHJJDgMmDd6nqnYai6IkSZIkSZIGjTTA+D7wDeBbwKyxK0eSJEmSJOnxRhpgzKyqr49pJZIkSZIkSXMw0m1Uf5rk3UnWS7LG0M+YViZJkiRJktQa6QyM/dp/PzowVsBGo1uOJEmSJEnS440owKiqyWNdiCRJkiRJ0pyMKMBI8ubhxqvqqNEtR5IkSZIk6fFG2gNj64Gf5wOfAl61oE+a5ENJLktyaZJjkizf9tU4LcnV7b+rDxz/iSRTk1yVZJeB8a2SXNLe9uUkWdCaJEmSJElSf40owKiq9w38vAN4NrDsgjxhkvWB9wNTqmoLYGlgX+BA4PSq2gQ4vb1Oks3a2zcHdgW+lmTp9uG+DhwAbNL+7LogNUmSJEmSpH4b6QyM2f2NJjBYUBOAFZJMAFYEbgF2B45sbz8S2KO9vDtwbFU9WFXXAVOBbZKsB6xSVedUVQFHDdxHkiRJkiQtQUbaA+OnNLuOQDNj4unA8QvyhFV1c5IvADcCDwCnVtWpSdatqlvbY25Nsk57l/WB3w08xLR27OH28uzjw9V/AM1MDTbccMMFKVuSJEmSJHVopNuofmHg8kzghqqaNqeD56btbbE7MBm4B/h+kjfO7S7DjNVcxh8/WHUYcBjAlClThj1GkiRJkiT110h7YJwJXAmsDKwOPLQQz/kS4Lqqml5VDwM/ArYHbmuXhdD+e3t7/DRgg4H7T6RZcjKtvTz7uCRJkiRJWsKMKMBI8lrgPGBv4LXAuUn2WsDnvBHYLsmK7a4hLwauAE4E9muP2Q84ob18IrBvkuWSTKbpvXFeu9zk/iTbtY/z5oH7SJIkSZKkJchIl5D8E7B1Vd0OkGRt4P+AH8zvE1bVuUl+APyBZjnKhTTLO1YCjk+yP03IsXd7/GVJjgcub49/T1XNah/uXcARwArAye2PJEmSJElawow0wFhqKLxo3cmC72BCVX0S+ORsww/SzMYY7viDgIOGGb8A2GJB65AkSZIkSYuHkQYYpyT5BXBMe30f4OdjU5IkSZIkSdJjzTXASLIxsG5VfTTJq4Hn0ez+cQ5w9CKoT5IkSZIkaZ7LQA4B7geoqh9V1Yer6kM0sy8OGdvSJEmSJEmSGvMKMCZV1cWzD7a9JyaNSUWSJEmSJEmzmVeAsfxcblthNAuRJEmSJEmak3kFGOcnecfsg+1Wp78fm5IkSZIkSZIea167kHwQ+HGSN/BoYDEFWBbYcwzrkiRJkiRJ+ru5BhhVdRuwfZIXAVu0wz+rqjPGvDJJkiRJkqTWvGZgAFBVvwR+Oca1SJIkSZIkDWtePTAkSZIkSZI6Z4AhSZIkSZJ6zwBDkiRJkiT1ngGGJEmSJEnqPQMMSZIkSZLUewYYkiRJkiSp9wwwJEmSJElS7xlgSJIkSZKk3jPAkCRJkiRJvWeAIUmSJEmSes8AQ5IkSZIk9Z4BhiRJkiRJ6j0DDEmSJEmS1HsGGJIkSZIkqfcMMCRJkiRJUu8ZYEiSJEmSpN4zwJAkSZIkSb1ngCFJkiRJknrPAEOSJEmSJPWeAYYkSZIkSeo9AwxJkiRJktR7BhiSJEmSJKn3DDAkSZIkSVLvGWBIkiRJkqTeM8CQJEmSJEm9Z4AhSZIkSZJ6zwBDkiRJkiT1ngGGJEmSJEnqPQMMSZIkSZLUewYYkiRJkiSp9wwwJEmSJElS7xlgSJIkSZKk3uskwEiyWpIfJLkyyRVJnptkjSSnJbm6/Xf1geM/kWRqkquS7DIwvlWSS9rbvpwkXfw+kiRJkiRpbHU1A+NQ4JSq2hTYErgCOBA4vao2AU5vr5NkM2BfYHNgV+BrSZZuH+frwAHAJu3Provyl5AkSZIkSYvGIg8wkqwC7AgcDlBVD1XVPcDuwJHtYUcCe7SXdweOraoHq+o6YCqwTZL1gFWq6pyqKuCogftIkiRJkqQlSBczMDYCpgP/m+TCJN9K8gRg3aq6FaD9d532+PWBmwbuP60dW7+9PPv44yQ5IMkFSS6YPn366P42kiRJkiRpzE3o6DmfA7yvqs5NcijtcpE5GK6vRc1l/PGDVYcBhwFMmTJl2GO6MOnAn3VdwkK7/uDdui5BkiRJkjQOdDEDYxowrarOba//gCbQuK1dFkL77+0Dx28wcP+JwC3t+MRhxiVJkiRJ0hJmkQcYVfVn4KYkT2uHXgxcDpwI7NeO7Qec0F4+Edg3yXJJJtM06zyvXWZyf5Lt2t1H3jxwH0mSJEmStATpYgkJwPuAo5MsC1wLvJUmTDk+yf7AjcDeAFV1WZLjaUKOmcB7qmpW+zjvAo4AVgBObn8kSZIkSdISppMAo6r+CEwZ5qYXz+H4g4CDhhm/ANhiVIuTJEmSJEm900UPDEmSJEmSpPligCFJkiRJknrPAEOSJEmSJPWeAYYkSZIkSeo9AwxJkiRJktR7BhiSJEmSJKn3DDAkSZIkSVLvGWBIkiRJkqTeM8CQJEmSJEm9Z4AhSZIkSZJ6zwBDkiRJkiT1ngGGJEmSJEnqPQMMSZIkSZLUewYYkiRJkiSp9wwwJEmSJElS7xlgSJIkSZKk3jPAkCRJkiRJvWeAIUmSJEmSes8AQ5IkSZIk9Z4BhiRJkiRJ6j0DDEmSJEmS1HsGGJIkSZIkqfcMMCRJkiRJUu8ZYEiSJEmSpN4zwJAkSZIkSb1ngCFJkiRJknrPAEOSJEmSJPWeAYYkSZIkSeo9AwxJkiRJktR7BhiSJEmSJKn3DDAkSZIkSVLvGWBIkiRJkqTeM8CQJEmSJEm9Z4AhSZIkSZJ6zwBDkiRJkiT1ngGGJEmSJEnqPQMMSZIkSZLUewYYkiRJkiSp9wwwJEmSJElS7xlgSJIkSZKk3usswEiydJILk5zUXl8jyWlJrm7/XX3g2E8kmZrkqiS7DIxvleSS9rYvJ0kXv4skSZIkSRpbXc7A+ABwxcD1A4HTq2oT4PT2Okk2A/YFNgd2Bb6WZOn2Pl8HDgA2aX92XTSlS5IkSZKkRamTACPJRGA34FsDw7sDR7aXjwT2GBg/tqoerKrrgKnANknWA1apqnOqqoCjBu4jSZIkSZKWIF3NwDgE+BjwyMDYulV1K0D77zrt+PrATQPHTWvH1m8vzz4uSZIkSZKWMIs8wEjyCuD2qvr9SO8yzFjNZXy45zwgyQVJLpg+ffoIn1aSJEmSJPVFFzMwdgBeleR64FhgpyTfBW5rl4XQ/nt7e/w0YIOB+08EbmnHJw4z/jhVdVhVTamqKWuvvfZo/i6SJEmSJGkRWOQBRlV9oqomVtUkmuacZ1TVG4ETgf3aw/YDTmgvnwjsm2S5JJNpmnWe1y4zuT/Jdu3uI28euI8kSZIkSVqCTOi6gAEHA8cn2R+4EdgboKouS3I8cDkwE3hPVc1q7/Mu4AhgBeDk9keSJEmSJC1hOg0wqupXwK/ay3cCL57DcQcBBw0zfgGwxdhVKEmSJEmS+qCrXUgkSZIkSZJGzABDkiRJkiT1ngGGJEmSJEnqPQMMSZIkSZLUewYYkiRJkiSp9wwwJEmSJElS7xlgSJIkSZKk3jPAkCRJkiRJvWeAIUmSJEmSes8AQ5IkSZIk9Z4BhiRJkiRJ6j0DDEmSJEmS1HsGGJIkSZIkqfcMMCRJkiRJUu8ZYEiSJEmSpN4zwJAkSZIkSb1ngCFJkiRJknrPAEOSJEmSJPWeAYYkSZIkSeo9AwxJkiRJktR7BhiSJEmSJKn3DDAkSZIkSVLvGWBIkiRJkqTeM8CQJEmSJEm9Z4AhSZIkSZJ6zwBDkiRJkiT1ngGGJEmSJEnqPQMMSZIkSZLUewYYkiRJkiSp9wwwJEmSJElS7xlgSJIkSZKk3jPAkCRJkiRJvWeAIUmSJEmSes8AQ5IkSZIk9Z4BhiRJkiRJ6j0DDEmSJEmS1HsGGJIkSZIkqfcMMCRJkiRJUu8ZYEiSJEmSpN4zwJAkSZIkSb1ngCFJkiRJknpvkQcYSTZI8sskVyS5LMkH2vE1kpyW5Or239UH7vOJJFOTXJVkl4HxrZJc0t725SRZ1L+PJEmSJEkae13MwJgJfKSqng5sB7wnyWbAgcDpVbUJcHp7nfa2fYHNgV2BryVZun2srwMHAJu0P7suyl9EkiRJkiQtGos8wKiqW6vqD+3l+4ErgPWB3YEj28OOBPZoL+8OHFtVD1bVdcBUYJsk6wGrVNU5VVXAUQP3kSRJkiRJS5BOe2AkmQQ8GzgXWLeqboUm5ADWaQ9bH7hp4G7T2rH128uzj0uSJEmSpCVMZwFGkpWAHwIfrKr75nboMGM1l/HhnuuAJBckuWD69OnzX6wkSZIkSepUJwFGkmVowoujq+pH7fBt7bIQ2n9vb8enARsM3H0icEs7PnGY8cepqsOqakpVTVl77bVH7xeRJEmSJEmLRBe7kAQ4HLiiqr44cNOJwH7t5f2AEwbG902yXJLJNM06z2uXmdyfZLv2Md88cB9JkiRJkrQEmdDBc+4AvAm4JMkf27F/BA4Gjk+yP3AjsDdAVV2W5HjgcpodTN5TVbPa+70LOAJYATi5/ZEkSZIkSUuYRR5gVNVvGb5/BcCL53Cfg4CDhhm/ANhi9KqTJEmSJEl91OkuJJIkSZIkSSNhgCFJkiRJknrPAEOSJEmSJPWeAYYkSZIkSeo9AwxJkiRJktR7BhiSJEmSJKn3DDAkSZIkSVLvGWBIkiRJkqTeM8CQJEmSJEm9N6HrAiRJkiQtWSYd+LOuS1ho1x+8W9clSJqNAYYkSZIkqTMGXhopl5BIkiRJkqTeM8CQJEmSJEm9Z4AhSZIkSZJ6zwBDkiRJkiT1ngGGJEmSJEnqPQMMSZIkSZLUewYYkiRJkiSp9wwwJEmSJElS7xlgSJIkSZKk3jPAkCRJkiRJvWeAIUmSJEmSes8AQ5IkSZIk9Z4BhiRJkiRJ6j0DDEmSJEmS1HsGGJIkSZIkqfcmdF2AJEmStEh8atWuK1h4n7q36wokqTPOwJAkSZIkSb3nDAwtHM9kSJIkSZIWAWdgSJIkSZKk3nMGhiRJ49ikA3/WdQkL7fqDd+u6BEmStAg4A0OSJEmSJPWeAYYkSZIkSeo9AwxJkiRJktR7BhiSJEmSJKn3DDAkSZIkSVLvGWBIkiRJkqTeM8CQJEmSJEm9Z4AhSZIkSZJ6zwBDkiRJkiT1ngGGJEmSJEnqvQldF7CwkuwKHAosDXyrqg7uuCRJkiRJ0njyqVW7rmDhfereriuYp8U6wEiyNPDfwEuBacD5SU6sqsu7rUySJEnSYs0vpFLvLNYBBrANMLWqrgVIciywO2CAIUnSeOGXDEmSxoVUVdc1LLAkewG7VtXb2+tvAratqvfOdtwBwAHt1acBVy3SQpdsawF3dF2EFhu+XjRSvlY0P3y9aKR8rWh++HrR/PD1MrqeXFVrzz64uM/AyDBjj0tkquow4LCxL2f8SXJBVU3pug4tHny9aKR8rWh++HrRSPla0fzw9aL54etl0VjcdyGZBmwwcH0icEtHtUiSJEmSpDGyuAcY5wObJJmcZFlgX+DEjmuSJEmSJEmjbLFeQlJVM5O8F/gFzTaq366qyzoua7xxaY7mh68XjZSvFc0PXy8aKV8rmh++XjQ/fL0sAot1E09JkiRJkjQ+LO5LSCRJkiRJ0jhggCFJkiRJknrPAEOSJEmSJPWeAYYWWpJ0XYOk8cn3H0kLK8nSvpdoTpIs3XUNWjz4PrJo2MRTo2ro/7jlC0tSB5LE9x/NS5J1q+q2rutQ/yQZOrlXvpcoySrA+4Fr2p+rq+rubqvS4sDvRGPHGRhaYEmemmRqki8l2TnJatXqujYtPobe4JMsm2SDJM9LsnrXdamfhr5cJNk0yeeT/CrJ84Zu9/1HczLwXrMK8MH28tJJlvKs2fiV5MYkP0/yuiSrV9Uj7Y/vJQJYF1gZeA7wAeDgJB9I8or2c/Aq3Zanvkjy2yT/27421vE70dgxwNACaT/sTQX2A1YHjgVuT3Juks8m2THJcp0WqcXCwJv7Z4D/Ar4GrJpkmSQbdFeZemro9XI4cCqwAbA8QJKDkmzWVWHqvY2TvJfmtfNkgKqaVVWPAJOSvKHT6tSVnYBzgEOBm5Ncl+RbSV6cZELHtaljVXV1VX0c+BuwTPuzOk2Y8WXghd1Vp575EHAr8A3g1iQXJTkkyUuTrNRxbUsU35i1QKqqklBVZyXZAfgN8FvgqcCHgY8D/wB8scMytZhIshHwfOBlwDlVdX2StYDDk+xeVQ90W6H6on3vWaW9/H9JpgNntTfvRvPBQRrOX4DbgOcBNyU5DrgB+AWwd3uZJEu1oYaWcEmWrqqpSc6gCUd/C8wA3gucBpxN83rRODXwfvBK4HlV9bf2y+jZNJ93h943XL44jrWvk/OTLNMO/ZAm6HoHzd+Yk4BXdVXfksYAQwts4I16P2C3qroeuAr4aZLPAT/vqjYtHgb+4D8T+CXN2fTL25snA8tW1QN+MNBs1gXOTfIO4Pb2NbIpMKuqbuq4NvVUVd2a5ERgHZr3m42ArYADgDVpzqbCo7N8tOQb+t/6n4DvVtWv2uu/S3I+8IdOqlJvVNUjSZ4ATAP2THJiVd0PnJrk07QBhp9Rxr2hZYjvB06vqt+31/8vydk0MzM0SlxCooWSZHmaZPGNSVZIYxNgV+DqbqtT3w38wb+C5v3ou8AlSdYFXg+c297ue5WAv58xvRo4HXgLsFKS9wCfp1nKZsd4zVFVPQh8B7i7qk6qqk9X1T5V9ZKqurw9xi8i48TATJvTgWcONPCEZlaOs/9EVf2VZnbfa4F3J/lMkqNpAvR77KGjqprVXjwb2CrJkwDa5fS7YYAxqtyFRAstyRNpzlxN5tEvordV1Uc6LUyLlSTPAd4NPAI8EbgS+HJVTXMGhgYl2bCqbkwyhWb98crAj4ErqupBXy+aXRt8zUryNmBPYAfgrzQzMX5BM933IZeOjE/tkoDDgF2Ai2h2m1izql7daWHqlSQ7A1sCM4H7gf9rl7y67Ex/l+SLwAtovhPNANYA9h4IObSQDDC0UNoznZvQTK2bRPPGfnZVXddlXeq/oS+ZbaPOp1bV6UnWpFlOctvQ2VBpUPt6OQx4efv62Rm4saqu7Lg09djA+815wEer6sw2NN0TeBvwyar6ll9ExqckGwJ3AsvSnC29o6pO6bYq9UXb4HdLmiVH/wYs1S4jkR4jyZNp3kvWpemfc11V/brbqpY8TsvWAmunWh5MM61uWlVdWlVH06SN0rwMvf+8DdirvbwDzdKRbeDRbQ+lgWUhewJ/ar+Mfgj4FnBckl26q059175elqHpsTO9HftDVf1LVa1fVd9qxwwvxok8uiXzR2majt8NbFBV3wUumm05icaZoc8fSbYE3gjcDryyXU6ydpIPd1mf+mPgveRDNDvq3QesWlVHAn9qb/Pz7CjyzVnzbeD/hC8EnkKzJvDS9rbnAod0UpgWN0PTv14A/G+SbYBXA3cBr02yi8sANGDoi+UGwDlJ9qB5/3kyTYixLTz6QUIaxmY0Sx0/n2SPJNsm2cjt7cantjnjCsCbgE/RLB25qR37HLBWh+Wpe0OfdV8K/Kz9GWrM+ByaXm/+zdHQe8nSwDuq6k3ABTTbqC4PfL1d9urn2VHkLiRaEKH58rk9cArNm/hF7W3rATbQ0zy1b/ihOSO6Kc1WU1+oqhOS/IpmWZJbkwl4TGPFo4Dj28v7tWfWXwn8RzeVaTHyZ+ALNMse96RZx/4wTf+L0zqsS4vYwN+V59HsNLIc8Nequrvdwvs5VXV7p0WqUwOzsW6hCcu/BHy/HXsucGZ72TPr49jAe8m2NDO3NgBmtrteLUezRPrGbqtc8pgaar4NvKn/DFgJeBdwRDs99000ex1L89S+6X8GeD7wvTa8eCawUlVdNnCMNNRob3pVPR3YtqrOSzIRWIb2w6RLADQnVXUbzTKB79IEpl8B/gjcBk7xHU8G/q5cSrNj2iHAGe3YO4HfdVCWeqiqvkfzvrEWsE6SE2i2YB4K0v2bM44NvJdcSzND599pTu5CszTNrZjHgDMwtMCq6sIkL6QJMX4CnA9cDJzQXVVaHAzsCLAHMAv4YLuulPb6l9vjbKinwTMczwXelGQqcEP776XASwy6NDdJ1qFpvrc2MKWqnpzkRuCGqrobDEvHkyRrVtWd7VnSi2h2H9kpySuA82iWpWmcS/J04EU0oec9NEsWvwOcWlX3ge8balTVn5OcA7wc2LNdUn8V8PVuK1syuQuJ5svQF8ok2wLrVdVP2vGNgZWr6sJOC9RiJckBwCtotsG8D/gtcGZVnddpYeqlJJsDW9F8CV2TpqfBDOCLvmY0nIG/WW+l2UXgB8CHquo1SV4G7F5V7+y2Si1KSdal+bvzvzQ9vM6k2TVgFWBSVV3QYXnqgTa4WJmmeefVVfWVgdueRZNbXDSHu2ucSbIycCzwX1V1Rvse80zgN1XlxgZjwBkYml9DU2z3o9nfeOhs+tQkmyWZWFXTuitPi5kjaNaUPhF4Fs1ypDck2amq7uqwLvVQu6zosrZZ1pOB3WnOjt3QaWHqs6GzNNsB36PpeXBWO7Yl8CA8Oits0ZenDixHM9V7A+DFNDO77geuB65O8pSquqa78tQDKwIfAPYArkzyRODsqvoZzc5p59P0O7BH1zjXLj18EDgV2DfJ84Gjq8q+SmPIGRhaIEkuBl5aVbclmVBVM5McC3yrqv6v6/q0eEryKuCZVfWZrmtRPwycQd+CpmHwCVV19cDtZ1TVTt1VqMVBkmcDrwP2AV5GE3r9HPhUVf3S5WrjT5K9aGZwPQI8jWZm1/rAT6rqx13Wpu61O0h8DfgxzW5pW9HsYnQn8IqqurXD8tQDswdYSV5Es8HB04E7gEOr6rqu6luSOQND861t1nky8L4kX6+qm9ubnkOzdlSao4EvpJ+haYT1A5plI3cCz6b5EGn/CwGPacq5KvASYK/2jMdNwN94dLcaz6BrjtqeTTsAN9LsOHI9cBw2fx13Br50vKOqdmmHf55kFZovHtd3Vpx6o536/7b26k+h6Z0CPMHwQvBo/5Mkl9M08byIZrbfRsAKNP0BDTDGgDMwNGJDf/STrE9ztuJtwDXARGBD4KKq+ocua9Tio+2jshuPvtlPA24HvlpVvzbA0HCSrM2jU79nAse2jfh8vegxBsLS5wGPVNXZ7fiKwLJVdU+nBaozSdagaep6IvBr16lryECT8RcA+9OEWqfS7Ezzu6qa7tIRDUmyFPAhms+yP6mqo9vPKesCV3hiZWwYYGjEBj4MHkHToftPwKtptpe6neaN/YEOS9RiJslawF+Bh2gaHt1cVbd3W5X6pu15sSOwDc0uNTdU1fe7rUp9N/A366fAf1fVKUk+SrOU5Is065T9EDSODJyIeQ3NFt630jTfu5gmRL/DMEMASS6jafL6Y5rXx47AssALbOCpYZaP7AB8nKZ/ymHAz6vqL13Vt6RbqusCtPgYOLt5NfDXqrq9qr4BfL+qfml4oXlpk2qSbJ/k68B/0ywhORSYbnihQW1wAc1MnY8Am9CEXbsl+WqSlTorTr3XhhcBJrfhxV40Z8k+RtOcb60u61OnVgU+SNNIegea2RjfB57fXUnqiyQbAXcBlwP3VdVeNL0NfkZz8k4CIMkXkpwBbE2z1GhpmlB0904LW8I5A0PzJclmwKU064i/CPzIXUc0UgNTM48D/gCcQtP9/e3AE2ga6t3dZY3qj4Ez6McAP66q49tQYyLwBeDIqjqp2yrVZ0kmAt+kObu+CfB+mjXJ51TVFl3Wpu4k+WVVvWjg+gSaIOPKqrqtu8rUB0m2olmm+GOas+lvBSYB76+qV7uEREOSTAY2pgnEHwL+DDyVZmmauxmNEZt4asTaL5+Xt8n0S4C9gH9sz4J+tqoO6rZC9d3AWsDNgdcNzOr5xyS/Bp5EsyRJGpz1dSWwbZLfVtUtwA1JVgCWARu+anjt62Jako/TbLd7UlVdnOTtNDMJbf46jgwsH9mAZmvM19LsRPNAVc2kbeiq8WsgmHgA+CrN9pg/BT5L8wV16DWyFM1yRul+YA2a3WnOrap7eXSrbo0RZ2BovrXNadYBbqqq+5JsD0yoql93XJoWA+2U7gNp+hl8maYx1vLA76tqoy5rUz+1Zzj+nWZ3gIdpArAnAHtX1d86LE09155Z34Rm1s609mdr4G9V9TvDr/Enyb7Ap4DpwOk0swGvpvlM45p1keT3wIvaz7jr0yw9+yMwraoedAbG+DYwm3g/mqUitwNPpAkyvlhVP+myvvHAAEMj1n4Q/E+arS4vBZYDflVVR3damBY7bfPOd9BMu9seuIWmod63/UKh4STZAngZzdmOWTTTM6/qtir1WZKVgf+h2Zr5Upqg9JyqOqrTwtSZgWVpKwFbAjvRfKZ5KvCBqjq90wLVmYEZOpNplki/tqoe7rou9c/A+8gvgK8MLWVN8iqaJtH/WlVXd1rkEs4lJJqngS+UOwOb0vyfc2WaXSPek+Thqjq+yxq12HkSzRmvC4FPAMtU1c3wmGUDGscGPiC8lOY95zbgPmBqVf1ft9VpMbEDTdC+F82swWcA70vySFV9t9PK1In2PWV1msDiHuDLVXVvO7PU2Rfj2MCMig1otnb/bZLjgSuAy6rqhs6KU6+07yPLACvQNHodGj8xySeAdFbcOGGAoZEYelPfAji/qm6l2XrsT+0HgR0AAwzNU5Jlge+1V28B1gO2rKrPdVeV+mggyPovmiaM99PsHHBQkifiFpiagyTvpVm7vhFwZrsm+V7g6iRPplm+9l1ne40fA4HoC4D30ZyEmQBcm+Q+mm3g3ZpZ0Cwzey/wNODJNLtg7Z/kUJdKa0hVPZzkq8BPk/wQOI1mCckTqsqdasaYAYbmaeBLwvE0/0d9Ak3jq6tpmnme0VVtWjwMrBfdkWYa9+tpzoxuBPxDkndV1de7rFH9MTCVd3OaXQG+MnDbhcC/eAZdc/EAMAVYHXhvku2Ak4BrgMntZY0vQ2dE3wIcTTODdCJwMfBOmtmAElV1bZK/VNVvANoeGM8GLum2MvVB+/ckwDXtzmi3A6+i6en2e2D/LusbLwwwNCLtmfO/AXsD+wD7Ai8EvgX4RULzMtSxe2Oavin3tePTk/yK5ozo190RQLPZGNgyyT/SbKN6BfAIcBe4+4jm6DvAT2h2DfgeTa+DPWmWQV4BfBxcrjaeDPxdWZtmJ4k3A/9WVRcmeTZwc2fFqXMDM3Q2B/YAXtb2fbsc+BHwm3Yml7Qb7a4jSW6lCUH/q6o+3G1Z44tNPDUiSbYFPgl8pKquSLJaVd3jFwjNjyTH0ARgJwLHADfR/DH4SVWdb2dvDUqyC/ByYE2as+rb0myzexLwPwNBmPQ4SVaoqgcGrm8KTKyq//O9ZvxpT8TsTfP354M0vZiOAo4Etququ7qrTl0a2FXiEOBGmuWK6wF/Bf4fcFRVvdP3DbXLEJ8F7EczM+cMmiWLDwBTgcOr6qHOChwnDDA0IklWBN4AvBU4GzjMNV6aX0lWo2mktxNN75QdaUKMnwKnAKf54UCDkqwAbAisS7MmeQOa9evrAR+rqhs7LE89M3AmdXea7e2eTzOt96fAz6rqni7rUzdm/+LZ7oR1KM37yY+q6pCualP3BpYtnktzUuXrwCFVdVaS/wCOqapLPGk3vs3292VX4KM0M0V3AN4FfL+qPt1ljeOFS0g0IlX1N+Cb7XT/PYE9kxzjlwfNy8Ab/qbAUu260t+0odiqNIHGrsC7qurULmtV9wZeLy8D3lhVb0gyjWbK5i9ppnpvCjzJ9x/NbuDLxedoloz8HJhO0wz2O0m2qir7HYwjA+8pWwKTaP7u3EDTqPGvni1VG14sC/yAZrn0I8DE9nPKHjRhl8vONGR74Maq+gvwR+CPSWYCq3Ra1ThigKF5avdLfybN1Ki1gGWBVwKfTLJ6VT3YZX3qvaHmaQfRfJm4PMnbaNaln1JVJwOnJlmqqwLVK0Ovl5cDZ7WXP03TfO93wD+0X0D9EqrHGDiL+hzgeprlRn+tqvclOQp4leHF+DPwpfNI4CqaEHRrmsZ7s5J8sar+3FV96o2HaXoZPJLkS8C/0fwduriqbnP5iAbeS34GHNp+bj2HZkvm1wD/3VFp444BhuZo4M36GcDngSfQrPWaRbPN1PGGF5qXgeZpk4Eft9vYvRY4H3h/kqlVdbVnNgSPeb1sDHw5yYHAA1W1VpLv0My+uNKpvJrdwJeLJwDH0Zxtv79ds7wRsAk8fjmBlnxJngLcUFX7JFmTR18PGwC3dVqcOjX0ftCGnzsneZhm2dnbaWb+Xd5theqDJMsOzdaqql8neTvwapqlis8BTqU5SadFwABDc/O8JOcB9wO7VdW9SVYZapznGXONVJI1aLYwfAXwPuDdbdPOP9A0zJJmdxjN1s230Uz1BticZs2pU3k1R1X1myR/qKq/JjkF+BTN7MGT20OGdkXS+PFXmuBzm6o6D7iTJkTXODcUZib5LrAi8BRgJZoQ470DX1oNPce39yS5BbgIeDrNbkZfAJavqls7rWwcMsDQsJIsA+zUfhDcDfhbkqnAtCR3A3e1fTGkeaqqu5IcRpNUH9qGFzsD91TVg55N1+yq6sdJLgEerKqb2tfLtKr6s2fQNS9V9df24uk0MwZ/RxuWulXz+DHwt2V/4LnAR5NcC5xGs4TxBN9PlGQjYLOqek57fQ2asPztwH90WZu6lyQ0TV0ryTdpdtP7M00T+jOT/I7mb80jvpcsGu5ComElmQS8kWbq3C40DY2WBf5Cc+biuqr6TmcFarEy+AExySo0W069EphRVScZYGhQ+2Fh6aqamWQn4Oo2xFi7qqb7hUNzM9C0cRPg/1XVP3Rdk7rTzha9Cnh2Vf2lfU95NbAv8IKquqzTAtWZgfeK7YHX0/Rb+ktVPZBkB+CLVbWtn1E0JMlHaBq9ngcsR9NLZ19g96q6qMvaxhOXAGhOnkIz5XZrmr2Np9F8AJhOs3f6Ot2VpsVNm1oPvd8cBCxXVT+gXS/oBwMNasOJobPkXwEmtuPTB26XHqcNt4beT14FPLEdX7q7qtSFNgiFptfFhTTbL1NVZ1TVe6tqLcOLcW/ob8lraJa4fg7YNsnraRp4/k9Xham3jgRWAz5Ls+XuIVU1yfBi0XIGhuYqyeHAusDtNJ27n0kzDffwqvpjh6Wp55K8FNgC+CrwxPYM+kbA6VU12bPoGk6S1Wi2SL08yarAMVX18o7L0mIkyWpVdU+SPYFrq+oiz6COPwO70rwS+DhwB/AL4Faaqd9XDiw10jiWZF2aoOulNEuN1gTuA35Cc6b9wqqa0VmB6lSSpatqVpIdaWajXwG8hGaZ0SrAgVX1vS5rHG8MMDRH7Z7Yv6+qZ7TXnwTsDewMvLmq7uyyPvVbki/T7I99D7BCVR2dZBtgSlV9begPQpc1qn+SvIim0es/tyHGZu2/Bl6aqyTL0/yNejrNDNNP0jTlu7+qZnZZm7qTZCLwNOCpNLuOLA+sCnzFEzECSLIWzRfSU9ueXRsCO9AsoZ4MvHSomafGryT/DbyVZseR79P0knwz8M2qOrbL2sYbm3hqbiYB05NsCVxeVbfQ7Hu8p+GFRuB04B9otr387yQr0QRi54GN9DRH59JsoXpMknOAQ8FlI5qzgdkVr6T5wnEn8OS2QfBzaL60Ht9ljepOVU2jWQZ7enumfW2a2YFTOy1MnRo4q/4ems+7zweeAfwTzRKBH1bVMc7e0oCv0MxCfx5NKHpIVR3ZbUnjkz0wNDfXAD+l6Vnw/5IclOQYwDWjmqeqOgH4GM2XiZ2Ai4FfJ/nPJC4J0LCq6m9V9U2as2EXA69K8tSOy9LiYXeawOsi4A/t2EuBF4Bbf493SZ4LfL6qLq2qY6vqL13XpE4NheKvAr4LXE+zdSrAO2l6Ytija5wb6qWTZE2amVvH07w+HgS+1jYF1iLmDAzNUXuG/EtJzqP5ADiD5qz6TzstTIuNqjonyR5VdWWS5WheR3sAH6Bt4CkNac+ObkMTnj6FZsr3bsAnaM6ISY8z8AXjPGAr4C3Au9qxbYBvdFCWOjSwu8Q+wKVts86daBqRk2SZqnq40yLVqfb1sRywfNsnZyLws/bmbWln/7l8cXwb+N9+R5pAfBNgfeA6muWK9wNndFPd+GUPDEmjbqB52lBo8Vfgsqq6p9vK1EcDXzZeDBwIrAD8imYHpA2AX1fV9+yborlJsg7wz8CLgB/R7KJ1L/C2qnqgy9rUjSS/AF5bVfcmeSNwQRuouyxAQ2fX3wgcAKwHPIvmi+qnq2rrDktTz7R9AZevqvuSLANsSLOd6t02eF30DDAkjZkk36WZZvdW4FqaLvCn0KwbvKfD0tQjSXalaYq1BXBV27tguap6sOPS1HMDYelKNIHXmsCuNJ3i7wN+VVW3d1mjutHuYvRFmtl+p1XVfR2XpJ4YCM1fDtxF87dnZ5qZF6fT7LR3lkGX1E+uB5U0JtrpmJtU1f7ABcALabaeej3gWXQBkGRl4Fnth8QDgA8leTWweZJJ7e3SnAx9jvkasFMbVpxHs+TxDMOLce1ZNI323gu8Nckrk2zd7jih8W3o7O1ngDuq6ls07xt/oNnq/Syw/4XUVwYYkkbVUMMjmmnc5yd5Ns0Uu2nA54CTqur+geM0jlXV/VV1cNtg8SyabS9fBbwf+BDNtpjSsAaWFG0JnNk2fD2eZhbGB5Ks0Flx6kz79+U3wD7AvwFPAl5Ns8PElA5LUw+0s7bWBR6uqqlJ3k6zdOR/gH2TrNhthZLmxiaekkbVQMOjM2jOaCwL/DXJR2ga6t3U3h4ePQuicSrJhKqaCWxHE24d0/ZO2Yzm9fLX9jin8mpYSbageZ08g2br5n8Dzgd+UlX/0mVt6kb7BXVt4M3ARVX18SRPoGm6d2O31akn1gFuS3IUsC7wBpolaGtU1d9s3in1lzMwJI2Jqrq5qq6qqkuAb9N0bb4c+Ga3lalP2vAC4JPAxu3Yg1V1IXBsVR3Xjhle6HHaM+1X0OyO9XHg1qr6AbAp8Of2GD/rjBMDWx4+m2ZZ0VrAl9qbNwJwWZEA2s8mB9GcaPmPqroDeC2PbsHs+4bUU87AkDQmZmuuV8BBVXXn0O1+IdWQtl/KxDa0GBpbHvhWkrdU1V+7q059NfAeQ1V9Nsm6VXVbG1jsABwzdGiHZWrRWoqmx9LLaZakTeXRLZifTrO06G2dVKbeqapzgXMB2mUjFwBXD93cVV2S5s50UdJYWbr99zXA3lV1ZxJDUw1nNeCaJM9ql49As33qk6vqr/ZL0XDa8GJZ4NokPwfelOTZVfVIVf0rbYDh1rvjx8D/1uvTbMW8M/CDdux5NLN1pMepqr9V1clVNbW97kkWqaf8MiFp1LRNse6rqgcGlgY8HTinw7LUc1V1aZJfAh8GTkiyJU0PjB+3hwydVZUeo6oeSvI8mgZ8OwNva0ONn1XVB7qtTh06CPgG8DLg1CQvoOmp87VOq5IkLbTYn0bSaGi7/R9EM2X3euAq4B6a5lhTq+qhzopT77UN9vah2fpwOnAR8MuhHWtspqaRSDIZ+Efgsqo6JMnSzsAYH4Ya/bbNO++h6XmxH7AbMI2mz8FZHZYoSRoFBhiSRkWSNYDX0zTP2wJYAzgRuIYm1Litqv7UXYXqq8GAIsluwC8GZvBIc5RkJ5ovp7cDM6pqRpJDgR9X1a8Mv8afJB8F9gcOqKpft2NLV9UsXw+StPgzwJA0qpJ8EHgizVrjtYHdac6EfbyqvtthaeqpJMtU1cNJ3gq8oKreMrC9qjSsdtbOT2madN4KXAg8CHwE2KaqbuuwPHUoyctoZnTdAHyzqqZ1XJIkaZQYYEgaFQNnuM4E3lZV1wzcdgTwvao6dWiab2eFqhcG+6UMjB0MXFdV/+PUf83N4PtIklWB5wAvoumVckq7u4DGqbbx7+bAu4BVgC8O7nIkSVp8GWBIGjXtLiOHACu1/06rqjuSnEOzE4lnwWS/FC2UgV4HTwZ2ArYHLgN+4HvM+DTwmphME1isB0wCHgDeDaxaVZt2WKIkaZQYYEgaFUNri9teGB8DHqGZ2r0tcFNV7ddpgeoN+6VoNCQ5FbiUpuHrs4Cn0CxVc6vMcSrJx4A3AafShBcbApcD51XVGV3WJkkaHQYYkkZNOwNjWZqpu8+kmc59EXBtVd1rAzUNsl+K5tdAUPp04Miq2mZoHHgH8GLgdS5TG79cpihJS7YJXRcgaYnyHpot65YHdm53BBhq0Gh4IeDRfinAnjy2X8oX2n4pt7fH+UVEjzHwHrIycF2SFavqb22ocRmwZ7uUwNfOODHb7L89gfckubhtBrxqVd3bdY2SpNGzVNcFSFoyJNkYeB3wduBJbXixEXBskuUMLzSkbfY6AbgE+Jckz0qyVnvz02imfOMXUM1JVZ1Hs8PE1Ul+mOSTNA0bf9keks6K06I29Fn2zTSz/w4DlmvHdk5yQCdVSZLGhAGGpIXSTt0G2Bo4DVgVuLgdWxtYs6oeHDhO41x7xnQm8K/An4HXAh9JcgbwJxsxaiSq6mPAVsD3aJYifYumeTA0PXg0PgyF4zsCR9E07zyrHZtC09BTkrSEcAmJpNFyA7AJzReIE9qx1wC/ay8vRdMTQ+NcO917AjAD+CGP9ks5HrgWHp0W3l2V6puBpQLrAa8EXgasBvwC+FhV3T90rK+d8WNgptY5wFo0Qcbz2rHtaJpKS5KWEDbxlDRqkuwJfAC4iaYPxn3AZ6tqql9INSjJB7BfiubDUO+UJJ8BngF8CXiQZkebvwH/DMz0tTM+tcvQTqZ5bXwPuBdYwx2wJGnJYoAhaYEl2R54EfAVYHuaM6HL02ydem9VXdhheeqptl/Kd2mWjpxRVRu3/VL+E3h9VT3YaYHqpYEZGAcBx1fVRe1MnmVots38YlX92ABs/BgItbYHNgNWoJkJuBlwIfBPVfVQlzVKkkaXPTAkLYxLadYcTwQ+D/weOIJmK8yZ0Owk0VVx6hf7pWhhtOHFE4C9gHcmmVBVM6vqAWA6zZbNYAPP8WQoqPos8HxgJeBm4P+AJ9D0SJEkLUHsgSFpYdxfVfcl2Q14Ac2X0B1o1h//a5J/rqrvekZUs7FfihbUBJovpy8G/pbkJuBu4MyquhbcvWY8abfMXRa4eWipSJJ1gMnAxrQ7GkmSlhwuIZG0UNoPj2dV1dazja8MPORyAA3HfimaHwPLR1ZoZ1wMvcfsCOwO7EQz8+uzVfVPHZaqRWTgNbEDzcy/g6vq8I7LkiSNMWdgSFogA18yNwXuSPKkqrpl6PbBHQGkYfql/AQ4hWH6pRheaHYDr4lvJjmoqq6gCSweqaoDANo+Kk/oqkYtWgOvibtpdiD5cNsc+Fqa/hf/W1U3dlWfJGlsOAND0gIZOPu1DU3/ixVpmnj+CbgCuKiqHu6yRvVHklVoel6sDBxL0yPlaprXzPlVdUmSpZz+rzlJMhE4qaqeleT5NGHY74BzgSN97Yxvbb+ljYHnAnsCX6uqU7utSpI02gwwJC2UJAfTfAldHZgErEuz/vgLVXVeh6WpRwYCr92As3lsv5QXA/ZL0bAGXjuvBfYFDm3/PR24Hziwql5kACZJ0pLPAEPSAkuyBs12hi9pr68CPBHYEPhtVc3osj71i/1StDCSrAu8G9gG+HVVfTbJPwDrVdVHhrbU7LZKSZI0luyBIWm+DZwlf2p7dRvggqq6j6YZ4586LVC9Yr8UjYaqui3JD4CjgWuTLEcz2+vooUM6K06SJC0SBhiS5tvAFP81gABfBM5PciNwDc2Whvd2VZ96a3lgBeAnSeyXovnW9kpZHtiZZgnJIVV1dXuby0ckSVrCuYRE0gJLshqwNPA0YAtgA2AT4DNVdWmHpamH7JeihZFkmap6OMl+wE5VtV/XNUmSpEXLGRiSFsbSNB3fz6uqswGSbAhM67Qq9U7bL2VKVR3YXh/sl3Jxl7Wpv9q+F/dV1QMDs3SeTtMI9u+hRmcFSpKkRcoAQ9J8GWqUl+QNwBRgK+ApSaYD5wDfraobOy1SvWG/FC2oJCsAHwemJrkeuAq4BzgKmApgeCFJ0vhigCFpfg2tO9uLZjvD5YDDad5PPgZcCpzljgAC+6VooawAXAtsBryW5jV0Is3rZs0kt1WVAZgkSeOIPTAkLZAkFwJbA78B3lJVVyX5MvCfVXXTwJl3yX4pWmBJPkiz3OgKYG1gd2Aj4ONV9d0OS5MkSYuYAYak+dZO7X5eVZ2W5N+Am2jOin6vqp7YbXXqoyRr8mi/lNvbsQ2Bae4eoeEMLFc7E3hbVV0zcNsRNO83pyZZyteQJEnjg0tIJI3YwBeF5WmmcH8EuA3YGzgX+EB7nMtHZL8ULZT2tTMBuAT4lySH0ARed9DM5Lm8Pc7wQpKkccIZGJJGbCjASHIqcCWwDLAxsE57ee+quqzLGtUfA6+XH9P0S3ktTdA11C/ly1X13wZeGs7QMrR2B5uPAY/Q9FHZFrjJbVQlSRp/nIEhacTaL6PLAStX1fsB2uuTgZcAd7Rj9r/Q4JnxScBvgc8Ch7b9UrakacgIzRdT6THa8GICMAP4IfBMYBZwPE1zT99rJEkaZ5yBIWlEBs6Grgi8DrifpoHnnVX1ULfVqa/sl6KFkeQDwG40y9Z2rqoZSZapqocNLyRJGn+cgSFppJaiOfu5H/B24GFgU2BakluBP1bVrR3Wpx6xX4oWVpKNacLS1wJntOHFRsB/Jnl9VT3YbYWSJGlRM8CQNCIDXzL3BT4EFM1a9K1olgh8EjDA0OyO4/H9Ul4DHAuPeV1JwGOWhWwNnAasClzc3rw2sGZVPegMDEmSxh8DDEkjlmR14Hrg7KqaSbOEhCSTgWkdlqaesV+KRsENwCbAIcAJ7dhrgN+1l4dmhUmSpHHCHhiSRizJ9sDpNEsBvkdzdvTM9suqX0QF2C9FoyfJnjTLjW6iWY50H/DZqprqe44kSeOPAYakEUsSYH3gucAONLsCPB3416r6Zpe1qT+G+lokeReP9kv5Oc0sHfulaI7akPRFwFeA7YFf0AQX2wL3VtWFHZYnSZI6ZoAhaYG1Wxw+Fbi7qm4daNwokeRM4F94tF/KZNp+KVV1QYelqaeSrELT82Jlmj4pM4GraYKM86vqEt9nJEkavwwwJEmjru2Xcgiwf9svZWh8MjCtqh7uqjb118Dyo92As2madu4APA94MfDPVfVdl49IkjQ+GWBIkkad/VK0oJIsC5xVVVvPNr4y8JDbp0qSNH4t1XUBkqQl0jk0O0h8FFiRZinJzUneYXih4bQ9dgA2Be5I8qTB26vqfsMLSZLGN2dgSJLGnP1SNC8Dy0e2AT5PE3z9AvgTcAVwkUuPJEka3wwwJElSbyQ5mCa4WJ2m6eu6NA1gv1BV53VYmiRJ6tiErguQJEkCSLIGMKWqDmyvrwI8EdgQuLjL2iRJUvcMMCRJUqcGGrs+tb26DXBBVd0H3EezjESSJI1zBhiSJKlTA41d1wACfBE4P8mNwDU0O9jc21V9kiSpH+yBIUmSeiHJasDSwNOALYANaHaz+UxVXdphaZIkqQcMMCRJUi8kWRN4LnBeVd3ejm0ITHPXGkmS5BISSZLUmSRLV9WsJG8ApgBbAU9JMh04B/huVd3YaZGSJKkXDDAkSVKXhqaC7gUcCiwHHE7zGeVjwKXAWUNBRzclSpKkPliq6wIkSdL4NbA0ZBLwW+DZwO+q6nDgF8CJ7e0uIZEkaZyzB4YkSepUkhWA51XVaUn+DbiJZveR71XVE7utTpIk9YUBhiRJ6kSSparqkSSrA7sA6wMzgN2Bc4FLq+o4l49IkiQwwJAkSR0ZCDBOBa4ElgE2BtZpL+9dVZd1WaMkSeoPm3hKkqROtOHFcsDKVfV+gPb6ZOAlwB3tWMozLpIkjXvOwJAkSYvcUCiRZEXgdcD9wG+AO6vqoW6rkyRJfeQMDEmS1IWlgFnAfsDbgYeBTYFpSW4F/lhVt3ZYnyRJ6hlnYEiSpM4kORP4F6CAbWmWj0wCPllVF3RYmiRJ6hlnYEiSpE60u49cD5xdVTNplpCQZDIwrcPSJElSDzkDQ5IkdSLJ9sDpwG3A94DTgDPb5p427pQkSY9hgCFJkjqRJMD6wHOBHYBnAk8H/rWqvtllbZIkqX8MMCRJUi8kmQA8Fbi7qm5NslRVPdJ1XZIkqR8MMCRJkiRJUu8t1XUBkiRJkiRJ82KAIUmSJEmSes8AQ5IkSZIk9Z4BhiRJ6lySJyX5QXv5WUlePoL7vDDJSfPxHHsk2Wy0jpMkSYuWAYYkSepUkglVdUtV7dUOPQuYZ4CxAPYARhJMjPQ4SZK0CBlgSJKkBZJkUpIrk3wryaVJjk7ykiRnJbk6yTbtz9lJLmz/fVp737ck+X6SnwKnto91aZJlgX8D9knyxyT7zOkx5lHbwUkuT3Jxki8k2R54FfCf7eM+Jck7kpyf5KIkP0yy4hyO+1WSKe3jrpXk+vby5knOa4+7OMkmY/NfWpIkAUzougBJkrRY2xjYGzgAOB94PfA8mhDgH4E3AztW1cwkLwH+A3hNe9/nAs+sqruSTAKoqoeS/CswpareC5Bklbk8xuMkWQPYE9i0qirJalV1T5ITgZOqamipyj1V9c328meA/avqK8McN6eneidwaFUd3QYvS8/nfztJkjQfDDAkSdLCuK6qLgFIchlwehsaXAJMAlYFjmxnJxSwzMB9T6uqu0bwHHN7jOHcB8wAvpXkZ8Cc+mRs0QYXqwErAb8YQS2DzgH+KclE4EdVdfV83l+SJM0Hl5BIkqSF8eDA5UcGrj9Cc6Lk34FfVtUWwCuB5QeO/+sIn2NujwFAkl+0Szm+VVUzgW2AH9L0szhlDo97BPDeqnoG8OnhHrc1k0c/M/39mKr6Hs1MkweAXyTZaYS/jyRJWgDOwJAkSWNpVeDm9vJbRnif+4GV5+cxqmqXoctJVgJWrKqfJ/kdMHUOj7sycGuSZYA3DDzH7MddD2wFnAcMNRolyUbAtVX15fbyM4EzRvg7SpKk+eQMDEmSNJY+D3w2yVmMvEfEL4HNhpp4LsBjrAyclORi4EzgQ+34scBH22agTwH+BTgXOA24cuD+sx/3BeBdSc4G1ho4bh/g0iR/BDYFjhrh7ydJkhZAqqrrGiRJkiRJkubKGRiSJEmSJKn3DDAkSZIkSVLvGWBIkiRJkqTeM8CQJEmSJEm9Z4AhSZIkSZJ6zwBDkiRJkiT1ngGGJEmSJEnqvf8PMRSwzJ/DpPIAAAAASUVORK5CYII=\n",
- "text/plain": [
- "<Figure size 1080x576 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- },
- {
- "data": {
- "text/plain": [
- "<Figure size 720x288 with 0 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
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\n",
- "text/plain": [
- "<Figure size 1080x576 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- },
- {
- "data": {
- "text/plain": [
- "<Figure size 720x288 with 0 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
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\n",
- "text/plain": [
- "<Figure size 1080x576 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- },
- {
- "data": {
- "text/plain": [
- "<Figure size 720x288 with 0 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
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\n",
- "text/plain": [
- "<Figure size 1080x576 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- },
- {
- "data": {
- "text/plain": [
- "<Figure size 720x288 with 0 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
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\n",
- "text/plain": [
- "<Figure size 1080x576 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- },
- {
- "data": {
- "text/plain": [
- "<Figure size 720x288 with 0 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 1080x576 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- },
- {
- "data": {
- "text/plain": [
- "<Figure size 720x288 with 0 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 1080x576 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "# Identifying categorical columns \n",
- "categorical_cols = df.select_dtypes(include=['object']).columns.tolist()\n",
- "\n",
- "for col in categorical_cols:\n",
- " plt.figure(figsize=(10, 4))\n",
- " income_counts = pd.crosstab(df[col], df['income'])\n",
- "\n",
- " income_counts.plot(kind='bar', width=0.7, figsize=(15, 8))\n",
- " plt.title(f\"Income vs {col}\")\n",
- " plt.xlabel(col)\n",
- " plt.ylabel(\"Count\")\n",
- " plt.xticks(rotation=75)\n",
- " plt.legend(title=\"Income\")\n",
- " plt.tight_layout()\n",
- " plt.show()\n",
- " \n",
- " \n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "44ec66a6",
- "metadata": {},
- "source": [
- "We can observe following observations about categorical features from the following dataset that we have cleaned "
- ]
- },
- {
- "cell_type": "markdown",
- "id": "395453da",
- "metadata": {},
- "source": [
- "1) Working class - \n",
- "\n",
- "- Government and self-employed classes generally have slightly higher income, but private sector dominates volume."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "dc09b491",
- "metadata": {},
- "source": [
- "2) Education - \n",
- "\n",
- "- Higher education level = better income chances"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "6ab03d8a",
- "metadata": {},
- "source": [
- "3) Maritial Status - \n",
- "\n",
- "- Being married (working spouse) strongly is directlty proportional to higher income. Likely due to dual incomes or stability factors."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "ad51ba4f",
- "metadata": {},
- "source": [
- "4) Occupation\n",
- "\n",
- "- White-collar and professional roles = higher income."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "66f5d265",
- "metadata": {},
- "source": [
- "5) Relationship\n",
- "\n",
- "- Husband shows higher chances of >50K income.Possibly gender or head-of-household effects."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "48c49d99",
- "metadata": {},
- "source": [
- "6) Race\n",
- "\n",
- "- Some racial groups show slight variation, but this may reflect underlying economic opportunity gaps."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "9bffb29a",
- "metadata": {},
- "source": [
- "7) Sex\n",
- "\n",
- "- Gender pay gap is evident in this dataset. Could be influenced by occupation, hours, or role."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "207d23c1",
- "metadata": {},
- "source": [
- "8) Hours-per-week\n",
- "\n",
- "- More hours worked = better income"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "f60b8ef5",
- "metadata": {},
- "source": [
- "9) Native Country\n",
- "\n",
- "- Some countries have better income distribution, but due to smaller counts, patterns may not generalize."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "b5b84212",
- "metadata": {},
- "source": [
- "Now as we can see there are alot of countries in the dataset which might cause inefficiency and problems in the ILP model we can group these countries into Regions\n",
- "\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "dc5822ba",
- "metadata": {},
- "source": [
- "### Grouping countries by continents"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 45,
- "id": "1dcf13e6",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "region \n",
- "North America 44221\n",
- "Latin America 1888\n",
- "Other 972\n",
- "Asia 922\n",
- "Europe 780\n",
- "Middle East 59\n",
- "dtype: int64\n",
- " age workclass education marital-status occupation \\\n",
- "0 middle state-gov bachelors never-married adm-clerical \n",
- "1 middle self-emp-not-inc bachelors married-civ-spouse exec-managerial \n",
- "2 middle private hs-grad divorced handlers-cleaners \n",
- "3 middle private 11th married-civ-spouse handlers-cleaners \n",
- "4 young private bachelors married-civ-spouse prof-specialty \n",
- "\n",
- " relationship race sex hours-per-week income region \n",
- "0 not-in-family white male average 0 North America \n",
- "1 husband white male low 0 North America \n",
- "2 not-in-family white male average 0 North America \n",
- "3 husband black male average 0 North America \n",
- "4 wife black female average 0 Latin America \n"
- ]
- }
- ],
- "source": [
- "# Normalize native-country column\n",
- "df['native-country'] = df['native-country'].astype(str).str.strip().str.replace('_', '-').str.title()\n",
- "\n",
- "# Replace missing with 'Unknown'\n",
- "df['native-country'] = df['native-country'].fillna('unknown')\n",
- "\n",
- "# Country-to-region mapping function\n",
- "def map_country_to_region(country):\n",
- " north_america = ['United-States', 'Canada', 'Puerto-Rico', 'Outlying-Us(Guam-Usvi-Etc)']\n",
- " latin_america = ['Mexico', 'Cuba', 'Jamaica', 'Honduras', 'El-Salvador',\n",
- " 'Columbia', 'Guatemala', 'Nicaragua', 'Dominican-Republic',\n",
- " 'Trinadad&Tobago', 'Ecuador', 'Haiti', 'Peru']\n",
- " asia = ['India', 'China', 'Japan', 'Vietnam', 'Philippines', 'Thailand', 'Cambodia', 'Laos', 'Taiwan', 'Hong']\n",
- " europe = ['England', 'Germany', 'Italy', 'Poland', 'Portugal', 'France', 'Greece', 'Ireland', 'Hungary',\n",
- " 'Scotland', 'Yugoslavia', 'Holand-Netherlands']\n",
- " middle_east = ['Iran']\n",
- " africa = ['South-Africa', 'Egypt']\n",
- "\n",
- " if country in north_america:\n",
- " return 'North America'\n",
- " elif country in latin_america:\n",
- " return 'Latin America'\n",
- " elif country in asia:\n",
- " return 'Asia'\n",
- " elif country in europe:\n",
- " return 'Europe'\n",
- " elif country in middle_east:\n",
- " return 'Middle East'\n",
- " elif country in africa:\n",
- " return 'Africa'\n",
- " elif country == 'unknown':\n",
- " return 'unknown'\n",
- " else:\n",
- " return 'Other'\n",
- "\n",
- "# Apply mapping\n",
- "df['region'] = df['native-country'].apply(map_country_to_region)\n",
- "\n",
- "# Optional: Drop original column\n",
- "df.drop(['native-country'], axis=1, inplace=True)\n",
- "\n",
- "# Preview dataset\n",
- "print(df[['region']].value_counts())\n",
- "print(df.head())\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "34450376",
- "metadata": {},
- "source": [
- "### Replacing '-' with an underscore to help the ILP model "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 46,
- "id": "031983f3",
- "metadata": {},
- "outputs": [],
- "source": [
- "# Replacing in all cell values\n",
- "df = df.applymap(lambda x: x.replace('-', '_') if isinstance(x, str) else x)\n",
- "\n",
- "# Replacing in column names\n",
- "df.columns = [col.replace('-', '_') for col in df.columns]"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "53838188",
- "metadata": {},
- "source": [
- "### Taking a random sample of 500 rows to feed into the ILP model\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "d6409f5b",
- "metadata": {},
- "source": [
- "We would have to cut short the dataset and take a sample of 500 rows so that we can reduce computational overhead during ILP processing, ensuring faster training times and allowing us to effectively explore and refine logical patterns without overwhelming the system or introducing excessive noise."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 47,
- "id": "d97dd58e",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " age workclass education marital_status occupation \\\n",
- "0 young private hs_grad never_married other_service \n",
- "1 young private 12th never_married sales \n",
- "2 young local_gov hs_grad never_married handlers_cleaners \n",
- "3 young private some_college never_married sales \n",
- "4 middle private hs_grad never_married machine_op_inspct \n",
- "\n",
- " relationship race sex hours_per_week income \\\n",
- "0 not_in_family white male low 0 \n",
- "1 own_child white female low 0 \n",
- "2 other_relative black male average 0 \n",
- "3 own_child white female average 0 \n",
- "4 unmarried amer_indian_eskimo male high 0 \n",
- "\n",
- " region \n",
- "0 North America \n",
- "1 North America \n",
- "2 North America \n",
- "3 North America \n",
- "4 North America \n"
- ]
- }
- ],
- "source": [
- "sample_df = df.sample(n=500, random_state=42) \n",
- "\n",
- "\n",
- "sample_df = sample_df.reset_index(drop=True)\n",
- "\n",
- "df = sample_df # Making the sample dataset of 500 rows as the main dataset to carry forward with the rest of the preprocessing \n",
- "\n",
- "# Previewing the new data \n",
- "print(df.head())\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "1f2b92e4",
- "metadata": {},
- "source": [
- "### Creating a new preprocessed CSV file for preprocessed data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 48,
- "id": "2cfc0da4",
- "metadata": {},
- "outputs": [],
- "source": [
- "df.to_csv('C:/Users/Arpit Mahapatra/Desktop/MLDM Coursework 2025/mlmavericks_coursework/data/processed/ILP_preprocessed_cencus_income_data.csv', index=False)"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.8.8"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
--
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