{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fe7534da-f82e-4a50-8db9-f88208e649ac",
   "metadata": {},
   "source": [
    "# Analysis of CSR Expenditure and Demographics in India\n",
    "\n",
    "This notebook presents an in-depth analysis of state expenditure and demographic factors in India. The dataset used contains information about the amount spent by various Indian states during the fiscal years 2019-2020, 2020-2021, and 2021-2022, along with population and poverty rate data. The goal of this analysis is to gain valuable insights into the CSR spending patterns of different states, explore correlations between spending and demographic factors, and identify trends and growth patterns. By examining this data, we aim to provide a comprehensive understanding of how fiscal decisions and demographics interplay in India's diverse regions."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b281f6c5-87ff-451b-84b9-0d607c1f5387",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "888b1e8f-6553-44e1-80f4-9dcdac719d16",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "pip install pandas numpy matplotlib seaborn ipywidgets scikit-learn openpyxl geopandas mplleaflet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "40344420-cc29-4da1-9007-5590111edb0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as mtick\n",
    "import seaborn as sns\n",
    "import ipywidgets as widgets\n",
    "from IPython.display import display\n",
    "from ipywidgets import interactive\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
    "# Set Seaborn style\n",
    "sns.set(style=\"whitegrid\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60cfabe3-fe9e-454a-a10b-02e9c48ac9e4",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "365f15d7-fd56-44b5-bba9-dba66279dbca",
   "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>State</th>\n",
       "      <th>Amount Spent FY 2019-2020 (INR Cr.)</th>\n",
       "      <th>Amount Spent FY 2020-2021 (INR Cr.)</th>\n",
       "      <th>Amount Spent FY 2021-2022 (INR Cr.)</th>\n",
       "      <th>Population</th>\n",
       "      <th>Poverty rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bihar</td>\n",
       "      <td>110.48</td>\n",
       "      <td>89.89</td>\n",
       "      <td>165.66</td>\n",
       "      <td>128500364</td>\n",
       "      <td>33.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jharkhand</td>\n",
       "      <td>155.21</td>\n",
       "      <td>226.54</td>\n",
       "      <td>192.41</td>\n",
       "      <td>40100376</td>\n",
       "      <td>28.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Meghalaya</td>\n",
       "      <td>17.65</td>\n",
       "      <td>17.63</td>\n",
       "      <td>19.30</td>\n",
       "      <td>3772103</td>\n",
       "      <td>27.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Uttar Pradesh</td>\n",
       "      <td>577.98</td>\n",
       "      <td>907.32</td>\n",
       "      <td>1321.36</td>\n",
       "      <td>231502578</td>\n",
       "      <td>22.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Madhya Pradesh</td>\n",
       "      <td>220.46</td>\n",
       "      <td>375.51</td>\n",
       "      <td>420.04</td>\n",
       "      <td>85002417</td>\n",
       "      <td>20.63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            State  Amount Spent FY 2019-2020 (INR Cr.)  \\\n",
       "0           Bihar                               110.48   \n",
       "1       Jharkhand                               155.21   \n",
       "2       Meghalaya                                17.65   \n",
       "3   Uttar Pradesh                               577.98   \n",
       "4  Madhya Pradesh                               220.46   \n",
       "\n",
       "   Amount Spent FY 2020-2021 (INR Cr.)  Amount Spent FY 2021-2022 (INR Cr.)  \\\n",
       "0                                89.89                               165.66   \n",
       "1                               226.54                               192.41   \n",
       "2                                17.63                                19.30   \n",
       "3                               907.32                              1321.36   \n",
       "4                               375.51                               420.04   \n",
       "\n",
       "   Population  Poverty rate  \n",
       "0   128500364         33.76  \n",
       "1    40100376         28.81  \n",
       "2     3772103         27.79  \n",
       "3   231502578         22.93  \n",
       "4    85002417         20.63  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "excel_file_path = 'data/StateWiseView.xlsx'\n",
    "data = pd.read_excel(excel_file_path, sheet_name=0)\n",
    "data.rename(columns={'population':\"Population\"}, inplace=True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4e60d119-3f59-4cf5-a3b6-2889be07637b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Amount Spent FY 2019-2020 (INR Cr.)</th>\n",
       "      <th>Amount Spent FY 2020-2021 (INR Cr.)</th>\n",
       "      <th>Amount Spent FY 2021-2022 (INR Cr.)</th>\n",
       "      <th>Population</th>\n",
       "      <th>Poverty rate</th>\n",
       "      <th>Rank</th>\n",
       "      <th>State/Union Territory</th>\n",
       "      <th>HDI (2021)</th>\n",
       "      <th>Country comparison</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bihar</td>\n",
       "      <td>110.48</td>\n",
       "      <td>89.89</td>\n",
       "      <td>165.66</td>\n",
       "      <td>128500364</td>\n",
       "      <td>33.76</td>\n",
       "      <td>34</td>\n",
       "      <td>Bihar</td>\n",
       "      <td>0.571</td>\n",
       "      <td>Republic of the Congo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jharkhand</td>\n",
       "      <td>155.21</td>\n",
       "      <td>226.54</td>\n",
       "      <td>192.41</td>\n",
       "      <td>40100376</td>\n",
       "      <td>28.81</td>\n",
       "      <td>33</td>\n",
       "      <td>Jharkhand</td>\n",
       "      <td>0.589</td>\n",
       "      <td>Angola</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Meghalaya</td>\n",
       "      <td>17.65</td>\n",
       "      <td>17.63</td>\n",
       "      <td>19.30</td>\n",
       "      <td>3772103</td>\n",
       "      <td>27.79</td>\n",
       "      <td>21</td>\n",
       "      <td>Meghalaya</td>\n",
       "      <td>0.643</td>\n",
       "      <td>Tuvalu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Uttar Pradesh</td>\n",
       "      <td>577.98</td>\n",
       "      <td>907.32</td>\n",
       "      <td>1321.36</td>\n",
       "      <td>231502578</td>\n",
       "      <td>22.93</td>\n",
       "      <td>32</td>\n",
       "      <td>Uttar Pradesh</td>\n",
       "      <td>0.592</td>\n",
       "      <td>Zimbabwe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Madhya Pradesh</td>\n",
       "      <td>220.46</td>\n",
       "      <td>375.51</td>\n",
       "      <td>420.04</td>\n",
       "      <td>85002417</td>\n",
       "      <td>20.63</td>\n",
       "      <td>31</td>\n",
       "      <td>Madhya Pradesh</td>\n",
       "      <td>0.596</td>\n",
       "      <td>Equatorial Guinea</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            State  Amount Spent FY 2019-2020 (INR Cr.)  \\\n",
       "0           Bihar                               110.48   \n",
       "1       Jharkhand                               155.21   \n",
       "2       Meghalaya                                17.65   \n",
       "3   Uttar Pradesh                               577.98   \n",
       "4  Madhya Pradesh                               220.46   \n",
       "\n",
       "   Amount Spent FY 2020-2021 (INR Cr.)  Amount Spent FY 2021-2022 (INR Cr.)  \\\n",
       "0                                89.89                               165.66   \n",
       "1                               226.54                               192.41   \n",
       "2                                17.63                                19.30   \n",
       "3                               907.32                              1321.36   \n",
       "4                               375.51                               420.04   \n",
       "\n",
       "   Population  Poverty rate Rank State/Union Territory  HDI (2021)  \\\n",
       "0   128500364         33.76   34                 Bihar       0.571   \n",
       "1    40100376         28.81   33             Jharkhand       0.589   \n",
       "2     3772103         27.79   21             Meghalaya       0.643   \n",
       "3   231502578         22.93   32         Uttar Pradesh       0.592   \n",
       "4    85002417         20.63   31        Madhya Pradesh       0.596   \n",
       "\n",
       "      Country comparison  \n",
       "0  Republic of the Congo  \n",
       "1                 Angola  \n",
       "2                 Tuvalu  \n",
       "3               Zimbabwe  \n",
       "4      Equatorial Guinea  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hdi_df = pd.read_excel('data/state_HDI.xlsx', header=1)\n",
    "\n",
    "# Check if 'Unnamed: 0' column exists before attempting to drop it\n",
    "if 'Unnamed: 0' in hdi_df.columns:\n",
    "    hdi_df = hdi_df.drop('Unnamed: 0', axis=1)\n",
    "hdi_df.head()\n",
    "\n",
    "data = data.merge(hdi_df, left_on='State', right_on=\"State/Union Territory\", how='inner')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f142e988-3d17-4042-8b8c-5dedbb61fae7",
   "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>Amount Spent FY 2019-2020 (INR Cr.)</th>\n",
       "      <th>Amount Spent FY 2020-2021 (INR Cr.)</th>\n",
       "      <th>Amount Spent FY 2021-2022 (INR Cr.)</th>\n",
       "      <th>Population</th>\n",
       "      <th>Poverty rate</th>\n",
       "      <th>HDI (2021)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>3.200000e+01</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>428.564062</td>\n",
       "      <td>458.734688</td>\n",
       "      <td>588.124688</td>\n",
       "      <td>4.416897e+07</td>\n",
       "      <td>10.897813</td>\n",
       "      <td>0.663062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>653.044228</td>\n",
       "      <td>683.359113</td>\n",
       "      <td>982.482441</td>\n",
       "      <td>5.187731e+07</td>\n",
       "      <td>8.807686</td>\n",
       "      <td>0.051314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>6.600100e+04</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>0.571000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>17.132500</td>\n",
       "      <td>16.310000</td>\n",
       "      <td>39.002500</td>\n",
       "      <td>3.095980e+06</td>\n",
       "      <td>4.442500</td>\n",
       "      <td>0.627750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>204.950000</td>\n",
       "      <td>203.385000</td>\n",
       "      <td>229.165000</td>\n",
       "      <td>3.135037e+07</td>\n",
       "      <td>7.955000</td>\n",
       "      <td>0.668500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>611.042500</td>\n",
       "      <td>638.282500</td>\n",
       "      <td>658.675000</td>\n",
       "      <td>7.267573e+07</td>\n",
       "      <td>15.492500</td>\n",
       "      <td>0.696000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3353.240000</td>\n",
       "      <td>3464.810000</td>\n",
       "      <td>5229.310000</td>\n",
       "      <td>2.315026e+08</td>\n",
       "      <td>33.760000</td>\n",
       "      <td>0.752000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Amount Spent FY 2019-2020 (INR Cr.)  \\\n",
       "count                            32.000000   \n",
       "mean                            428.564062   \n",
       "std                             653.044228   \n",
       "min                               0.000000   \n",
       "25%                              17.132500   \n",
       "50%                             204.950000   \n",
       "75%                             611.042500   \n",
       "max                            3353.240000   \n",
       "\n",
       "       Amount Spent FY 2020-2021 (INR Cr.)  \\\n",
       "count                            32.000000   \n",
       "mean                            458.734688   \n",
       "std                             683.359113   \n",
       "min                               0.010000   \n",
       "25%                              16.310000   \n",
       "50%                             203.385000   \n",
       "75%                             638.282500   \n",
       "max                            3464.810000   \n",
       "\n",
       "       Amount Spent FY 2021-2022 (INR Cr.)    Population  Poverty rate  \\\n",
       "count                            32.000000  3.200000e+01     32.000000   \n",
       "mean                            588.124688  4.416897e+07     10.897813   \n",
       "std                             982.482441  5.187731e+07      8.807686   \n",
       "min                               0.450000  6.600100e+04      0.550000   \n",
       "25%                              39.002500  3.095980e+06      4.442500   \n",
       "50%                             229.165000  3.135037e+07      7.955000   \n",
       "75%                             658.675000  7.267573e+07     15.492500   \n",
       "max                            5229.310000  2.315026e+08     33.760000   \n",
       "\n",
       "       HDI (2021)  \n",
       "count   32.000000  \n",
       "mean     0.663062  \n",
       "std      0.051314  \n",
       "min      0.571000  \n",
       "25%      0.627750  \n",
       "50%      0.668500  \n",
       "75%      0.696000  \n",
       "max      0.752000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Descriptive Statistics\n",
    "# Calculate basic statistics for numeric columns\n",
    "desc_stats = data.describe()\n",
    "desc_stats"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "144b86b9-f114-4934-a3b5-ce99fac60e23",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "57dbcfb5-c7e3-42f2-a7be-7adf91344883",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Comparative Analysis - Spending patterns over the years\n",
    "# Create a line plot to visualize spending trends for each state over time\n",
    "plt.figure(figsize=(18, 6))\n",
    "for col in data.columns[1:4]:  # Columns with spending data\n",
    "    sns.lineplot(data=data, x='State', y=col, label=col)\n",
    "plt.title('Spending Trends Over Time')\n",
    "plt.xlabel('State')\n",
    "plt.ylabel('Amount Spent (INR Cr.)')\n",
    "plt.xticks(rotation=90)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3c80cd6f-fbbb-408d-9f06-bd029686d085",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 states with the highest poverty rate and their expenditure\n"
     ]
    },
    {
     "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",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Poverty rate</th>\n",
       "      <th>Population</th>\n",
       "      <th>Amount Spent FY 2019-2020 (INR Cr.)</th>\n",
       "      <th>Amount Spent FY 2020-2021 (INR Cr.)</th>\n",
       "      <th>Amount Spent FY 2021-2022 (INR Cr.)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bihar</td>\n",
       "      <td>33.76</td>\n",
       "      <td>128500364</td>\n",
       "      <td>110.48</td>\n",
       "      <td>89.89</td>\n",
       "      <td>165.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jharkhand</td>\n",
       "      <td>28.81</td>\n",
       "      <td>40100376</td>\n",
       "      <td>155.21</td>\n",
       "      <td>226.54</td>\n",
       "      <td>192.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Meghalaya</td>\n",
       "      <td>27.79</td>\n",
       "      <td>3772103</td>\n",
       "      <td>17.65</td>\n",
       "      <td>17.63</td>\n",
       "      <td>19.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Uttar Pradesh</td>\n",
       "      <td>22.93</td>\n",
       "      <td>231502578</td>\n",
       "      <td>577.98</td>\n",
       "      <td>907.32</td>\n",
       "      <td>1321.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Madhya Pradesh</td>\n",
       "      <td>20.63</td>\n",
       "      <td>85002417</td>\n",
       "      <td>220.46</td>\n",
       "      <td>375.51</td>\n",
       "      <td>420.04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            State  Poverty rate  Population  \\\n",
       "0           Bihar         33.76   128500364   \n",
       "1       Jharkhand         28.81    40100376   \n",
       "2       Meghalaya         27.79     3772103   \n",
       "3   Uttar Pradesh         22.93   231502578   \n",
       "4  Madhya Pradesh         20.63    85002417   \n",
       "\n",
       "   Amount Spent FY 2019-2020 (INR Cr.)  Amount Spent FY 2020-2021 (INR Cr.)  \\\n",
       "0                               110.48                                89.89   \n",
       "1                               155.21                               226.54   \n",
       "2                                17.65                                17.63   \n",
       "3                               577.98                               907.32   \n",
       "4                               220.46                               375.51   \n",
       "\n",
       "   Amount Spent FY 2021-2022 (INR Cr.)  \n",
       "0                               165.66  \n",
       "1                               192.41  \n",
       "2                                19.30  \n",
       "3                              1321.36  \n",
       "4                               420.04  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort the data by poverty rate in descending order\n",
    "sorted_data = data.sort_values(by='Poverty rate', ascending=False)\n",
    "\n",
    "# Get the top 5 states with the highest poverty rates\n",
    "top_5_states = sorted_data.head(5)\n",
    "\n",
    "# Extract the columns for expenditures per year\n",
    "expenditure_columns = ['Amount Spent FY 2019-2020 (INR Cr.)', 'Amount Spent FY 2020-2021 (INR Cr.)', 'Amount Spent FY 2021-2022 (INR Cr.)']\n",
    "\n",
    "# Create a DataFrame with the top 5 states and their corresponding expenditures for each year\n",
    "result_df = top_5_states[['State', 'Poverty rate', 'Population'] + expenditure_columns]\n",
    "\n",
    "# Display the DataFrame\n",
    "print(\"5 states with the highest poverty rate and their expenditure\")\n",
    "result_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8db5b335-e3c3-4276-9784-d4dc2038bcd5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation between Population and Spending (FY 2021-2022): 0.54\n",
      "Correlation between Poverty Rate and Spending (FY 2021-2022): -0.06\n",
      "Correlation between HDI (2021) and Spending (FY 2021-2022): -0.03\n"
     ]
    }
   ],
   "source": [
    "def calculate_correlation(data, field1, field2):\n",
    "    # Calculate the correlation between field1 and field2\n",
    "    correlation = data[field1].corr(data[field2])\n",
    "    return correlation\n",
    "\n",
    "# Example of how to use the function\n",
    "population_spending_corr = calculate_correlation(data, 'Population', 'Amount Spent FY 2021-2022 (INR Cr.)')\n",
    "poverty_rate_spending_corr = calculate_correlation(data, 'Poverty rate', 'Amount Spent FY 2021-2022 (INR Cr.)')\n",
    "hdi_spending_corr = calculate_correlation(data, 'HDI (2021)', 'Amount Spent FY 2021-2022 (INR Cr.)')\n",
    "\n",
    "print(f\"Correlation between Population and Spending (FY 2021-2022): {population_spending_corr:.2f}\")\n",
    "print(f\"Correlation between Poverty Rate and Spending (FY 2021-2022): {poverty_rate_spending_corr:.2f}\")\n",
    "print(f\"Correlation between HDI (2021) and Spending (FY 2021-2022): {hdi_spending_corr:.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "71f3298f-cf8a-46a0-920d-4b1b5db5f946",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c50d3fe9eced41f2a179d0ad04d444a8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='Select Year:', index=2, options=('2019-2020', '2020-2021', '2021-2…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a widget to select the year\n",
    "year_selector = widgets.Dropdown(\n",
    "    options=['2019-2020', '2020-2021', '2021-2022'],\n",
    "    value='2021-2022',\n",
    "    description='Select Year:',\n",
    ")\n",
    "\n",
    "# Function to update and display the linear regression chart\n",
    "def update_chart(selected_year, new_field):\n",
    "    # Extract CSR spending and the selected field for the selected year\n",
    "    csr_spending = data[f'Amount Spent FY {selected_year} (INR Cr.)']\n",
    "    selected_field = data[new_field]\n",
    "\n",
    "    # Reshape the data for Linear Regression\n",
    "    csr_spending = csr_spending.values.reshape(-1, 1)\n",
    "    selected_field = selected_field.values.reshape(-1, 1)\n",
    "\n",
    "    # Create a Linear Regression model\n",
    "    model = LinearRegression()\n",
    "    model.fit(selected_field, csr_spending)\n",
    "\n",
    "    # Make predictions using the model\n",
    "    predictions = model.predict(selected_field)\n",
    "\n",
    "    # Create a scatter plot of the data points\n",
    "    plt.figure(figsize=(20, 6))\n",
    "    plt.scatter(selected_field, csr_spending, color='blue', label='Data Points')\n",
    "\n",
    "    # Plot the regression line\n",
    "    plt.plot(selected_field, predictions, color='red', linewidth=2, label='Linear Regression')\n",
    "\n",
    "    # Customize the plot\n",
    "    plt.title(f'Linear Regression: {new_field} vs. CSR Spending ({selected_year})', fontsize=16)\n",
    "    plt.xlabel(f'{new_field}')\n",
    "    plt.ylabel(f'CSR Spending {selected_year} (INR Cr.)')\n",
    "    plt.legend()\n",
    "    plt.grid(True)\n",
    "\n",
    "    # Show the plot\n",
    "    plt.show()\n",
    "    \n",
    "\n",
    "\n",
    "# Create an interactive widget that updates the chart\n",
    "interactive_plot = interactive(update_chart, selected_year=year_selector, new_field=['Poverty rate', 'Population', 'HDI (2021)'])\n",
    "\n",
    "# Display the interactive widget\n",
    "display(interactive_plot)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3669624-e289-4a84-aa9b-432245a1569d",
   "metadata": {},
   "source": [
    "Parliamentary Constituencies Maps are provided by [Data{Meet} Community Maps Project](http://projects.datameet.org/maps/). Its made available under the [Creative Commons Attribution 2.5 India](http://creativecommons.org/licenses/by/2.5/in/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "46004283-e637-482d-a1a6-3218c9a6540d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "88c2ba8685d24384acf18cedc9428200",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='Select Year:', index=2, options=('2019-2020', '2020-2021', '2021-2…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load the shapefile containing state boundaries\n",
    "shapefile = 'States/Admin2.shp'\n",
    "gdf = gpd.read_file(shapefile)\n",
    "gdf = gdf.rename(columns={\"ST_NM\": \"State\"})\n",
    "\n",
    "# Create a function to update the choropleth map\n",
    "def update_map(year):\n",
    "    # Merge the shapefile data with your DataFrame (assuming a common column, e.g., 'State')\n",
    "    merged = gdf.set_index('State').join(data.set_index('State'))\n",
    "\n",
    "    # Create a choropleth map based on the selected year\n",
    "    fig, ax = plt.subplots(1, 1, figsize=(12, 8))\n",
    "    merged.plot(column=f'Amount Spent FY {year} (INR Cr.)', cmap='OrRd', linewidth=0.8, ax=ax, edgecolor='0.8', legend=True)\n",
    "\n",
    "    # Customize the plot\n",
    "    ax.set_title(f'Expenditure by State ({year})')\n",
    "    ax.axis('off')\n",
    "\n",
    "    # Show the map\n",
    "    plt.show()\n",
    "\n",
    "# Create a widget to select the year\n",
    "year_selector = widgets.Dropdown(\n",
    "    options=['2019-2020', '2020-2021', '2021-2022'],\n",
    "    value='2021-2022',\n",
    "    description='Select Year:'\n",
    ")\n",
    "\n",
    "# Create an interactive widget to update the map\n",
    "interactive_map = widgets.interactive(update_map, year=year_selector)\n",
    "\n",
    "# Display the interactive widget\n",
    "display(interactive_map)"
   ]
  }
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