Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "20d2de55",
   "metadata": {},
   "source": [
    "# DATA INTEGRATION\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7ce1031",
   "metadata": {},
   "source": [
    "## 1) DATA EXTRACTION\n",
    "This step involves the data extracted from various sources such as databases important sites that had data for the waste generation and management from Germany. Subsequently two datasets were found out that were deemed to be suitable and these datasets were then explored and integrated into one common dataset for further processes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c03a7553",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f5e140d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel(\"Final_worksheet.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ea24073c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Id</th>\n",
       "      <th>Year\\nLänder\\nType of plant</th>\n",
       "      <th>Input_to_waste_treatment_plants</th>\n",
       "      <th>waste_generated_in_local_unit</th>\n",
       "      <th>Waste_delivered_from_domestic_territory</th>\n",
       "      <th>Wastes_delivered_from_abroad</th>\n",
       "      <th>Output_ratio_of_waste_Treatment_plants</th>\n",
       "      <th>Output_of_waste_treatment_plants</th>\n",
       "      <th>Disposed_of_waste</th>\n",
       "      <th>Wastes_to_be_recoverd</th>\n",
       "      <th>Waste_to_preparatory_process</th>\n",
       "      <th>Distribution_to_other_plants_users_or_traders</th>\n",
       "      <th>Waste_quantities_generated_by_primary_producers/1000tons</th>\n",
       "      <th>Year</th>\n",
       "      <th>State</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EA-01</td>\n",
       "      <td>Landfills</td>\n",
       "      <td>5629.8</td>\n",
       "      <td>132.3</td>\n",
       "      <td>5436.4</td>\n",
       "      <td>61.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>590.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>380.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>185.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017</td>\n",
       "      <td>Baden-Württemberg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EA-02</td>\n",
       "      <td>Thermal waste treatment plants</td>\n",
       "      <td>1856.4</td>\n",
       "      <td>39.3</td>\n",
       "      <td>1785.6</td>\n",
       "      <td>31.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>498.1</td>\n",
       "      <td>17.1</td>\n",
       "      <td>477.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.7</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2017</td>\n",
       "      <td>Baden-Württemberg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EA-03</td>\n",
       "      <td>Combustion plants</td>\n",
       "      <td>2411.2</td>\n",
       "      <td>417.2</td>\n",
       "      <td>1977.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>177.7</td>\n",
       "      <td>81.6</td>\n",
       "      <td>95.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2017</td>\n",
       "      <td>Baden-Württemberg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EA-04</td>\n",
       "      <td>Chemical-physical treatment plants</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>355.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>590.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>380.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>185.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2017</td>\n",
       "      <td>Baden-Württemberg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EA-05</td>\n",
       "      <td>Soil treatment plants</td>\n",
       "      <td>NaN</td>\n",
       "      <td>156.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>45.0</td>\n",
       "      <td>386.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>180.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2017</td>\n",
       "      <td>Baden-Württemberg</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Id         Year\\nLänder\\nType of plant  Input_to_waste_treatment_plants  \\\n",
       "0  EA-01                           Landfills                           5629.8   \n",
       "1  EA-02      Thermal waste treatment plants                           1856.4   \n",
       "2  EA-03                   Combustion plants                           2411.2   \n",
       "3  EA-04  Chemical-physical treatment plants                           1100.0   \n",
       "4  EA-05               Soil treatment plants                              NaN   \n",
       "\n",
       "   waste_generated_in_local_unit  Waste_delivered_from_domestic_territory  \\\n",
       "0                          132.3                                   5436.4   \n",
       "1                           39.3                                   1785.6   \n",
       "2                          417.2                                   1977.0   \n",
       "3                          155.0                                    355.0   \n",
       "4                          156.0                                      NaN   \n",
       "\n",
       "   Wastes_delivered_from_abroad  Output_ratio_of_waste_Treatment_plants  \\\n",
       "0                          61.1                                     NaN   \n",
       "1                          31.5                                     NaN   \n",
       "2                          17.0                                     NaN   \n",
       "3                          35.0                                     NaN   \n",
       "4                           NaN                                     NaN   \n",
       "\n",
       "   Output_of_waste_treatment_plants  Disposed_of_waste  Wastes_to_be_recoverd  \\\n",
       "0                             590.0               45.0                  380.0   \n",
       "1                             498.1               17.1                  477.2   \n",
       "2                             177.7               81.6                   95.8   \n",
       "3                             590.0               45.0                  380.0   \n",
       "4                               NaN               45.0                  386.0   \n",
       "\n",
       "   Waste_to_preparatory_process  \\\n",
       "0                           NaN   \n",
       "1                           NaN   \n",
       "2                           NaN   \n",
       "3                           NaN   \n",
       "4                           NaN   \n",
       "\n",
       "   Distribution_to_other_plants_users_or_traders  \\\n",
       "0                                          185.0   \n",
       "1                                            3.7   \n",
       "2                                            0.3   \n",
       "3                                          185.0   \n",
       "4                                          180.0   \n",
       "\n",
       "   Waste_quantities_generated_by_primary_producers/1000tons  Year  \\\n",
       "0                                                NaN         2017   \n",
       "1                                                0.9         2017   \n",
       "2                                                0.9         2017   \n",
       "3                                                0.9         2017   \n",
       "4                                                0.9         2017   \n",
       "\n",
       "               State  \n",
       "0  Baden-Württemberg  \n",
       "1  Baden-Württemberg  \n",
       "2  Baden-Württemberg  \n",
       "3  Baden-Württemberg  \n",
       "4  Baden-Württemberg  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "60a7dced",
   "metadata": {},
   "outputs": [],
   "source": [
    "df= df.rename(columns = {\"Year\\nLänder\\nType of plant\":\"Type_of_plant\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b1b3f9ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id                                                            0\n",
       "Type_of_plant                                                 0\n",
       "Input_to_waste_treatment_plants                              81\n",
       "waste_generated_in_local_unit                                80\n",
       "Waste_delivered_from_domestic_territory                      96\n",
       "Wastes_delivered_from_abroad                                101\n",
       "Output_ratio_of_waste_Treatment_plants                      960\n",
       "Output_of_waste_treatment_plants                             82\n",
       "Disposed_of_waste                                           111\n",
       "Wastes_to_be_recoverd                                       140\n",
       "Waste_to_preparatory_process                                839\n",
       "Distribution_to_other_plants_users_or_traders               156\n",
       "Waste_quantities_generated_by_primary_producers/1000tons    226\n",
       "Year                                                          0\n",
       "State                                                         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "274559c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id                                                            0\n",
       "Type_of_plant                                                 0\n",
       "Input_to_waste_treatment_plants                              81\n",
       "waste_generated_in_local_unit                                80\n",
       "Waste_delivered_from_domestic_territory                      96\n",
       "Wastes_delivered_from_abroad                                101\n",
       "Output_ratio_of_waste_Treatment_plants                      960\n",
       "Output_of_waste_treatment_plants                             82\n",
       "Disposed_of_waste                                           111\n",
       "Wastes_to_be_recoverd                                       140\n",
       "Waste_to_preparatory_process                                839\n",
       "Distribution_to_other_plants_users_or_traders               156\n",
       "Waste_quantities_generated_by_primary_producers/1000tons    226\n",
       "Year                                                          0\n",
       "State                                                         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b276e59f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 960 entries, 0 to 959\n",
      "Data columns (total 15 columns):\n",
      " #   Column                                                    Non-Null Count  Dtype  \n",
      "---  ------                                                    --------------  -----  \n",
      " 0   Id                                                        960 non-null    object \n",
      " 1   Type_of_plant                                             960 non-null    object \n",
      " 2   Input_to_waste_treatment_plants                           879 non-null    float64\n",
      " 3   waste_generated_in_local_unit                             880 non-null    float64\n",
      " 4   Waste_delivered_from_domestic_territory                   864 non-null    float64\n",
      " 5   Wastes_delivered_from_abroad                              859 non-null    float64\n",
      " 6   Output_ratio_of_waste_Treatment_plants                    0 non-null      float64\n",
      " 7   Output_of_waste_treatment_plants                          878 non-null    float64\n",
      " 8   Disposed_of_waste                                         849 non-null    float64\n",
      " 9   Wastes_to_be_recoverd                                     820 non-null    float64\n",
      " 10  Waste_to_preparatory_process                              121 non-null    float64\n",
      " 11  Distribution_to_other_plants_users_or_traders             804 non-null    float64\n",
      " 12  Waste_quantities_generated_by_primary_producers/1000tons  734 non-null    float64\n",
      " 13  Year                                                      960 non-null    int64  \n",
      " 14  State                                                     960 non-null    object \n",
      "dtypes: float64(11), int64(1), object(3)\n",
      "memory usage: 112.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b1ba9bf2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ba6f7925",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 960 entries, 0 to 959\n",
      "Data columns (total 15 columns):\n",
      " #   Column                                                    Non-Null Count  Dtype  \n",
      "---  ------                                                    --------------  -----  \n",
      " 0   Id                                                        960 non-null    object \n",
      " 1   Type_of_plant                                             960 non-null    object \n",
      " 2   Input_to_waste_treatment_plants                           879 non-null    float64\n",
      " 3   waste_generated_in_local_unit                             880 non-null    float64\n",
      " 4   Waste_delivered_from_domestic_territory                   864 non-null    float64\n",
      " 5   Wastes_delivered_from_abroad                              859 non-null    float64\n",
      " 6   Output_ratio_of_waste_Treatment_plants                    0 non-null      float64\n",
      " 7   Output_of_waste_treatment_plants                          878 non-null    float64\n",
      " 8   Disposed_of_waste                                         849 non-null    float64\n",
      " 9   Wastes_to_be_recoverd                                     820 non-null    float64\n",
      " 10  Waste_to_preparatory_process                              121 non-null    float64\n",
      " 11  Distribution_to_other_plants_users_or_traders             804 non-null    float64\n",
      " 12  Waste_quantities_generated_by_primary_producers/1000tons  734 non-null    float64\n",
      " 13  Year                                                      960 non-null    int64  \n",
      " 14  State                                                     960 non-null    object \n",
      "dtypes: float64(11), int64(1), object(3)\n",
      "memory usage: 112.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6a7118f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id                                                            0\n",
       "Type_of_plant                                                 0\n",
       "Input_to_waste_treatment_plants                              81\n",
       "waste_generated_in_local_unit                                80\n",
       "Waste_delivered_from_domestic_territory                      96\n",
       "Wastes_delivered_from_abroad                                101\n",
       "Output_ratio_of_waste_Treatment_plants                      960\n",
       "Output_of_waste_treatment_plants                             82\n",
       "Disposed_of_waste                                           111\n",
       "Wastes_to_be_recoverd                                       140\n",
       "Waste_to_preparatory_process                                839\n",
       "Distribution_to_other_plants_users_or_traders               156\n",
       "Waste_quantities_generated_by_primary_producers/1000tons    226\n",
       "Year                                                          0\n",
       "State                                                         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6d454ee9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing the waste categories....\n",
    "waste_categories = pd.read_excel(\"Waste_type_categories.xlsx\",header=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6cadd6bc",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>ID</th>\n",
       "      <th>Categories</th>\n",
       "      <th>Waste Type</th>\n",
       "      <th>Waste producers(No.)</th>\n",
       "      <th>Waste quantities per 1000 tons</th>\n",
       "      <th>Waste quantities generated by primary producers per 1000 tons</th>\n",
       "      <th>Year</th>\n",
       "      <th>Wastes generated in the own local unit 1000 t</th>\n",
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       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>EA-01</td>\n",
       "      <td>Manufacture of food products, beverages, tobacco</td>\n",
       "      <td>Wastes resulting from expl., mining of minerals</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2017</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>EA-02</td>\n",
       "      <td>Manufacture of food products, beverages, tobacco</td>\n",
       "      <td>Wastes from agriculture,horticulture,forestry ...</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2017</td>\n",
       "      <td>26619.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>EA-03</td>\n",
       "      <td>Manufacture of food products, beverages, tobacco</td>\n",
       "      <td>Wastes from wood processing etc.</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2017</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>EA-04</td>\n",
       "      <td>Manufacture of food products, beverages, tobacco</td>\n",
       "      <td>Wastes from the leather, fur, textile industries</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>EA-05</td>\n",
       "      <td>Manufacture of food products, beverages, tobacco</td>\n",
       "      <td>Wastes from petrol. refining, natural gas puri...</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2017</td>\n",
       "      <td>835.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0     ID                                        Categories  \\\n",
       "0           0  EA-01  Manufacture of food products, beverages, tobacco   \n",
       "1           1  EA-02  Manufacture of food products, beverages, tobacco   \n",
       "2           2  EA-03  Manufacture of food products, beverages, tobacco   \n",
       "3           3  EA-04  Manufacture of food products, beverages, tobacco   \n",
       "4           4  EA-05  Manufacture of food products, beverages, tobacco   \n",
       "\n",
       "                                          Waste Type  Waste producers(No.)  \\\n",
       "0    Wastes resulting from expl., mining of minerals                  27.0   \n",
       "1  Wastes from agriculture,horticulture,forestry ...                  27.0   \n",
       "2                   Wastes from wood processing etc.                  27.0   \n",
       "3   Wastes from the leather, fur, textile industries                  27.0   \n",
       "4  Wastes from petrol. refining, natural gas puri...                  27.0   \n",
       "\n",
       "   Waste quantities per 1000 tons  \\\n",
       "0                             0.7   \n",
       "1                             0.7   \n",
       "2                             0.7   \n",
       "3                             0.7   \n",
       "4                             0.7   \n",
       "\n",
       "   Waste quantities generated by primary producers per 1000 tons  Year  \\\n",
       "0                                                0.9              2017   \n",
       "1                                                0.9              2017   \n",
       "2                                                0.9              2017   \n",
       "3                                                0.9              2017   \n",
       "4                                                0.9              2017   \n",
       "\n",
       "   Wastes generated in the own local unit 1000 t  \n",
       "0                                           27.0  \n",
       "1                                        26619.5  \n",
       "2                                            3.6  \n",
       "3                                            0.0  \n",
       "4                                          835.2  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "waste_categories.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ff17deda",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0                                                       0\n",
       "ID                                                               0\n",
       "Categories                                                       0\n",
       "Waste Type                                                       0\n",
       "Waste producers(No.)                                             0\n",
       "Waste quantities per 1000 tons                                   0\n",
       "Waste quantities generated by primary producers per 1000 tons    0\n",
       "Year                                                             0\n",
       "Wastes generated in the own local unit 1000 t                    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "waste_categories.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ef2ebed1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#waste_categories[\"Waste quantities per 1000 tons\"].fillna(waste_categories[\"Waste quantities per 1000 tons\"].mean(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d1d3f163",
   "metadata": {},
   "outputs": [],
   "source": [
    "#waste_categories[\"Waste quantities generated by primary producers per 1000 tons\"].fillna(waste_categories[\"Waste quantities generated by primary producers per 1000 tons\"].mean(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2ff65994",
   "metadata": {},
   "outputs": [],
   "source": [
    "#waste_categories[\"Waste producers(No.)\"].fillna(waste_categories[\"Waste producers(No.)\"].mean(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fc937f0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#waste_categories[\"Wastes generated in the own local unit 1000 t\"].fillna(waste_categories[\"Wastes generated in the own local unit 1000 t\"].mean(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b2b130a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#waste_categories.to_excel(\"Waste_type_categories.xlsx\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c736bc05",
   "metadata": {},
   "source": [
    "# DATA TRANSFORMATION\n",
    "This step involves transforming data that involves cleaning and preprocessing data into a useful format for suitable analysis and usage. This will include dealing with null-values, cleaning the data and then merging different datasets into a useful single dataset that can be used for data analysis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a31eb818",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 784 entries, 0 to 783\n",
      "Data columns (total 9 columns):\n",
      " #   Column                                                         Non-Null Count  Dtype  \n",
      "---  ------                                                         --------------  -----  \n",
      " 0   Unnamed: 0                                                     784 non-null    int64  \n",
      " 1   ID                                                             784 non-null    object \n",
      " 2   Categories                                                     784 non-null    object \n",
      " 3   Waste Type                                                     784 non-null    object \n",
      " 4   Waste producers(No.)                                           784 non-null    float64\n",
      " 5   Waste quantities per 1000 tons                                 784 non-null    float64\n",
      " 6   Waste quantities generated by primary producers per 1000 tons  784 non-null    float64\n",
      " 7   Year                                                           784 non-null    int64  \n",
      " 8   Wastes generated in the own local unit 1000 t                  784 non-null    float64\n",
      "dtypes: float64(4), int64(2), object(3)\n",
      "memory usage: 55.2+ KB\n"
     ]
    }
   ],
   "source": [
    "# Analysing the waste_categories dataset. This dataset 784 entries. and some of its columns have reasonable\n",
    "## null values. \n",
    "waste_categories.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6daae251",
   "metadata": {},
   "outputs": [],
   "source": [
    "### We will start by deleting those rows that even have atleast one null value.\n",
    "## The idea is to deal with as much as real-world dataset. \n",
    "waste_categories.dropna(axis=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "474d6c1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Wastes resulting from expl., mining of minerals',\n",
       "       'Wastes from agriculture,horticulture,forestry etc.',\n",
       "       'Wastes from wood processing etc.',\n",
       "       'Wastes from the leather, fur, textile industries',\n",
       "       'Wastes from petrol. refining, natural gas purific.',\n",
       "       'Wastes from inorganic chemical processes',\n",
       "       'Wastes from organic chemical processes',\n",
       "       'Wastes from the MFSU of coatings, adhesives etc.',\n",
       "       'Wastes from the photographic industry',\n",
       "       'Wastes from surface treatment of metals a.plastics',\n",
       "       'Waste organic solvents, refrigerants, propellants',\n",
       "       'Wastes from human or animal health care',\n",
       "       'Wastes from waste management facilities etc.',\n",
       "       'Municipal wastes (household waste, similar wastes)'], dtype=object)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "###Finding out the unique values of types of wastes in waste-type dataset\n",
    "waste_categories[\"Waste Type\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "704e222d",
   "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>Id</th>\n",
       "      <th>Type_of_plant</th>\n",
       "      <th>Input_to_waste_treatment_plants</th>\n",
       "      <th>waste_generated_in_local_unit</th>\n",
       "      <th>Waste_delivered_from_domestic_territory</th>\n",
       "      <th>Wastes_delivered_from_abroad</th>\n",
       "      <th>Output_ratio_of_waste_Treatment_plants</th>\n",
       "      <th>Output_of_waste_treatment_plants</th>\n",
       "      <th>Disposed_of_waste</th>\n",
       "      <th>Wastes_to_be_recoverd</th>\n",
       "      <th>Waste_to_preparatory_process</th>\n",
       "      <th>Distribution_to_other_plants_users_or_traders</th>\n",
       "      <th>Waste_quantities_generated_by_primary_producers/1000tons</th>\n",
       "      <th>Year</th>\n",
       "      <th>State</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EA-01</td>\n",
       "      <td>Landfills</td>\n",
       "      <td>5629.8</td>\n",
       "      <td>132.3</td>\n",
       "      <td>5436.4</td>\n",
       "      <td>61.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>590.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>380.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>185.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017</td>\n",
       "      <td>Baden-Württemberg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EA-02</td>\n",
       "      <td>Thermal waste treatment plants</td>\n",
       "      <td>1856.4</td>\n",
       "      <td>39.3</td>\n",
       "      <td>1785.6</td>\n",
       "      <td>31.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>498.1</td>\n",
       "      <td>17.1</td>\n",
       "      <td>477.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.7</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2017</td>\n",
       "      <td>Baden-Württemberg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EA-03</td>\n",
       "      <td>Combustion plants</td>\n",
       "      <td>2411.2</td>\n",
       "      <td>417.2</td>\n",
       "      <td>1977.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>177.7</td>\n",
       "      <td>81.6</td>\n",
       "      <td>95.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2017</td>\n",
       "      <td>Baden-Württemberg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EA-04</td>\n",
       "      <td>Chemical-physical treatment plants</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>355.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>590.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>380.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>185.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2017</td>\n",
       "      <td>Baden-Württemberg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EA-05</td>\n",
       "      <td>Soil treatment plants</td>\n",
       "      <td>NaN</td>\n",
       "      <td>156.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>45.0</td>\n",
       "      <td>386.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>180.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2017</td>\n",
       "      <td>Baden-Württemberg</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Id                       Type_of_plant  Input_to_waste_treatment_plants  \\\n",
       "0  EA-01                           Landfills                           5629.8   \n",
       "1  EA-02      Thermal waste treatment plants                           1856.4   \n",
       "2  EA-03                   Combustion plants                           2411.2   \n",
       "3  EA-04  Chemical-physical treatment plants                           1100.0   \n",
       "4  EA-05               Soil treatment plants                              NaN   \n",
       "\n",
       "   waste_generated_in_local_unit  Waste_delivered_from_domestic_territory  \\\n",
       "0                          132.3                                   5436.4   \n",
       "1                           39.3                                   1785.6   \n",
       "2                          417.2                                   1977.0   \n",
       "3                          155.0                                    355.0   \n",
       "4                          156.0                                      NaN   \n",
       "\n",
       "   Wastes_delivered_from_abroad  Output_ratio_of_waste_Treatment_plants  \\\n",
       "0                          61.1                                     NaN   \n",
       "1                          31.5                                     NaN   \n",
       "2                          17.0                                     NaN   \n",
       "3                          35.0                                     NaN   \n",
       "4                           NaN                                     NaN   \n",
       "\n",
       "   Output_of_waste_treatment_plants  Disposed_of_waste  Wastes_to_be_recoverd  \\\n",
       "0                             590.0               45.0                  380.0   \n",
       "1                             498.1               17.1                  477.2   \n",
       "2                             177.7               81.6                   95.8   \n",
       "3                             590.0               45.0                  380.0   \n",
       "4                               NaN               45.0                  386.0   \n",
       "\n",
       "   Waste_to_preparatory_process  \\\n",
       "0                           NaN   \n",
       "1                           NaN   \n",
       "2                           NaN   \n",
       "3                           NaN   \n",
       "4                           NaN   \n",
       "\n",
       "   Distribution_to_other_plants_users_or_traders  \\\n",
       "0                                          185.0   \n",
       "1                                            3.7   \n",
       "2                                            0.3   \n",
       "3                                          185.0   \n",
       "4                                          180.0   \n",
       "\n",
       "   Waste_quantities_generated_by_primary_producers/1000tons  Year  \\\n",
       "0                                                NaN         2017   \n",
       "1                                                0.9         2017   \n",
       "2                                                0.9         2017   \n",
       "3                                                0.9         2017   \n",
       "4                                                0.9         2017   \n",
       "\n",
       "               State  \n",
       "0  Baden-Württemberg  \n",
       "1  Baden-Württemberg  \n",
       "2  Baden-Württemberg  \n",
       "3  Baden-Württemberg  \n",
       "4  Baden-Württemberg  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6c91617e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id                                                            0\n",
       "Type_of_plant                                                 0\n",
       "Input_to_waste_treatment_plants                              81\n",
       "waste_generated_in_local_unit                                80\n",
       "Waste_delivered_from_domestic_territory                      96\n",
       "Wastes_delivered_from_abroad                                101\n",
       "Output_ratio_of_waste_Treatment_plants                      960\n",
       "Output_of_waste_treatment_plants                             82\n",
       "Disposed_of_waste                                           111\n",
       "Wastes_to_be_recoverd                                       140\n",
       "Waste_to_preparatory_process                                839\n",
       "Distribution_to_other_plants_users_or_traders               156\n",
       "Waste_quantities_generated_by_primary_producers/1000tons    226\n",
       "Year                                                          0\n",
       "State                                                         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8892ea8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "### It is evident that the dataset has one column Waste_to_preparatory_process column that has abundant \n",
    "## null-values so it is better to get rid of these values. \n",
    "## Similarly Output_ratio_of_waste_treatment_plants also has very much null values.\n",
    "## So we get rid of this column as well. \n",
    "df.drop(\"Waste_to_preparatory_process\",axis=1,inplace=True)\n",
    "df.drop(\"Output_ratio_of_waste_Treatment_plants\",axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "cd360b28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id                                                            0\n",
       "Type_of_plant                                                 0\n",
       "Input_to_waste_treatment_plants                              81\n",
       "waste_generated_in_local_unit                                80\n",
       "Waste_delivered_from_domestic_territory                      96\n",
       "Wastes_delivered_from_abroad                                101\n",
       "Output_of_waste_treatment_plants                             82\n",
       "Disposed_of_waste                                           111\n",
       "Wastes_to_be_recoverd                                       140\n",
       "Distribution_to_other_plants_users_or_traders               156\n",
       "Waste_quantities_generated_by_primary_producers/1000tons    226\n",
       "Year                                                          0\n",
       "State                                                         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "597fbe01",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Since now we have a dataset with columns that have some of its values as null. \n",
    "### The idea is to have real-world dataset as much as possible. So in order to have a real-world dataset.\n",
    "## we will get rid of the rows that have even one null values in them.\n",
    "df.dropna(axis=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0ccaba2e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 491 entries, 1 to 769\n",
      "Data columns (total 13 columns):\n",
      " #   Column                                                    Non-Null Count  Dtype  \n",
      "---  ------                                                    --------------  -----  \n",
      " 0   Id                                                        491 non-null    object \n",
      " 1   Type_of_plant                                             491 non-null    object \n",
      " 2   Input_to_waste_treatment_plants                           491 non-null    float64\n",
      " 3   waste_generated_in_local_unit                             491 non-null    float64\n",
      " 4   Waste_delivered_from_domestic_territory                   491 non-null    float64\n",
      " 5   Wastes_delivered_from_abroad                              491 non-null    float64\n",
      " 6   Output_of_waste_treatment_plants                          491 non-null    float64\n",
      " 7   Disposed_of_waste                                         491 non-null    float64\n",
      " 8   Wastes_to_be_recoverd                                     491 non-null    float64\n",
      " 9   Distribution_to_other_plants_users_or_traders             491 non-null    float64\n",
      " 10  Waste_quantities_generated_by_primary_producers/1000tons  491 non-null    float64\n",
      " 11  Year                                                      491 non-null    int64  \n",
      " 12  State                                                     491 non-null    object \n",
      "dtypes: float64(9), int64(1), object(3)\n",
      "memory usage: 53.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "97d379b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 784 entries, 0 to 783\n",
      "Data columns (total 9 columns):\n",
      " #   Column                                                         Non-Null Count  Dtype  \n",
      "---  ------                                                         --------------  -----  \n",
      " 0   Unnamed: 0                                                     784 non-null    int64  \n",
      " 1   ID                                                             784 non-null    object \n",
      " 2   Categories                                                     784 non-null    object \n",
      " 3   Waste Type                                                     784 non-null    object \n",
      " 4   Waste producers(No.)                                           784 non-null    float64\n",
      " 5   Waste quantities per 1000 tons                                 784 non-null    float64\n",
      " 6   Waste quantities generated by primary producers per 1000 tons  784 non-null    float64\n",
      " 7   Year                                                           784 non-null    int64  \n",
      " 8   Wastes generated in the own local unit 1000 t                  784 non-null    float64\n",
      "dtypes: float64(4), int64(2), object(3)\n",
      "memory usage: 61.2+ KB\n"
     ]
    }
   ],
   "source": [
    "waste_categories.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a0dc44d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Type_of_plant</th>\n",
       "      <th>Input_to_waste_treatment_plants</th>\n",
       "      <th>waste_generated_in_local_unit</th>\n",
       "      <th>Waste_delivered_from_domestic_territory</th>\n",
       "      <th>Wastes_delivered_from_abroad</th>\n",
       "      <th>Output_of_waste_treatment_plants</th>\n",
       "      <th>Disposed_of_waste</th>\n",
       "      <th>Wastes_to_be_recoverd</th>\n",
       "      <th>Distribution_to_other_plants_users_or_traders</th>\n",
       "      <th>Waste_quantities_generated_by_primary_producers/1000tons</th>\n",
       "      <th>Year</th>\n",
       "      <th>State</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EA-02</td>\n",
       "      <td>Thermal waste treatment plants</td>\n",
       "      <td>1856.4</td>\n",
       "      <td>39.3</td>\n",
       "      <td>1785.6</td>\n",
       "      <td>31.5</td>\n",
       "      <td>498.1</td>\n",
       "      <td>17.1</td>\n",
       "      <td>477.2</td>\n",
       "      <td>3.7</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2017</td>\n",
       "      <td>Baden-Württemberg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EA-03</td>\n",
       "      <td>Combustion plants</td>\n",
       "      <td>2411.2</td>\n",
       "      <td>417.2</td>\n",
       "      <td>1977.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>177.7</td>\n",
       "      <td>81.6</td>\n",
       "      <td>95.8</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2017</td>\n",
       "      <td>Baden-Württemberg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EA-04</td>\n",
       "      <td>Chemical-physical treatment plants</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>355.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>590.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>380.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2017</td>\n",
       "      <td>Baden-Württemberg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>EA-09</td>\n",
       "      <td>Shredding plants and scrap shears</td>\n",
       "      <td>2105.2</td>\n",
       "      <td>47.2</td>\n",
       "      <td>1949.9</td>\n",
       "      <td>108.0</td>\n",
       "      <td>2122.1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1060.0</td>\n",
       "      <td>1061.9</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2017</td>\n",
       "      <td>Baden-Württemberg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>EA-10</td>\n",
       "      <td>Sorting plants</td>\n",
       "      <td>2600.5</td>\n",
       "      <td>53.9</td>\n",
       "      <td>2333.9</td>\n",
       "      <td>212.8</td>\n",
       "      <td>2556.4</td>\n",
       "      <td>8.2</td>\n",
       "      <td>1305.4</td>\n",
       "      <td>1242.8</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2017</td>\n",
       "      <td>Baden-Württemberg</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Id                       Type_of_plant  Input_to_waste_treatment_plants  \\\n",
       "1  EA-02      Thermal waste treatment plants                           1856.4   \n",
       "2  EA-03                   Combustion plants                           2411.2   \n",
       "3  EA-04  Chemical-physical treatment plants                           1100.0   \n",
       "8  EA-09   Shredding plants and scrap shears                           2105.2   \n",
       "9  EA-10                      Sorting plants                           2600.5   \n",
       "\n",
       "   waste_generated_in_local_unit  Waste_delivered_from_domestic_territory  \\\n",
       "1                           39.3                                   1785.6   \n",
       "2                          417.2                                   1977.0   \n",
       "3                          155.0                                    355.0   \n",
       "8                           47.2                                   1949.9   \n",
       "9                           53.9                                   2333.9   \n",
       "\n",
       "   Wastes_delivered_from_abroad  Output_of_waste_treatment_plants  \\\n",
       "1                          31.5                             498.1   \n",
       "2                          17.0                             177.7   \n",
       "3                          35.0                             590.0   \n",
       "8                         108.0                            2122.1   \n",
       "9                         212.8                            2556.4   \n",
       "\n",
       "   Disposed_of_waste  Wastes_to_be_recoverd  \\\n",
       "1               17.1                  477.2   \n",
       "2               81.6                   95.8   \n",
       "3               45.0                  380.0   \n",
       "8                0.3                 1060.0   \n",
       "9                8.2                 1305.4   \n",
       "\n",
       "   Distribution_to_other_plants_users_or_traders  \\\n",
       "1                                            3.7   \n",
       "2                                            0.3   \n",
       "3                                          185.0   \n",
       "8                                         1061.9   \n",
       "9                                         1242.8   \n",
       "\n",
       "   Waste_quantities_generated_by_primary_producers/1000tons  Year  \\\n",
       "1                                                0.9         2017   \n",
       "2                                                0.9         2017   \n",
       "3                                                0.9         2017   \n",
       "8                                                0.9         2017   \n",
       "9                                                0.9         2017   \n",
       "\n",
       "               State  \n",
       "1  Baden-Württemberg  \n",
       "2  Baden-Württemberg  \n",
       "3  Baden-Württemberg  \n",
       "8  Baden-Württemberg  \n",
       "9  Baden-Württemberg  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "25f35359",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 784 entries, 0 to 783\n",
      "Data columns (total 9 columns):\n",
      " #   Column                                                         Non-Null Count  Dtype  \n",
      "---  ------                                                         --------------  -----  \n",
      " 0   Unnamed: 0                                                     784 non-null    int64  \n",
      " 1   ID                                                             784 non-null    object \n",
      " 2   Categories                                                     784 non-null    object \n",
      " 3   Waste Type                                                     784 non-null    object \n",
      " 4   Waste producers(No.)                                           784 non-null    float64\n",
      " 5   Waste quantities per 1000 tons                                 784 non-null    float64\n",
      " 6   Waste quantities generated by primary producers per 1000 tons  784 non-null    float64\n",
      " 7   Year                                                           784 non-null    int64  \n",
      " 8   Wastes generated in the own local unit 1000 t                  784 non-null    float64\n",
      "dtypes: float64(4), int64(2), object(3)\n",
      "memory usage: 61.2+ KB\n"
     ]
    }
   ],
   "source": [
    "waste_categories.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ac0d57ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>ID</th>\n",
       "      <th>Categories</th>\n",
       "      <th>Waste Type</th>\n",
       "      <th>Waste producers(No.)</th>\n",
       "      <th>Waste quantities per 1000 tons</th>\n",
       "      <th>Waste quantities generated by primary producers per 1000 tons</th>\n",
       "      <th>Year</th>\n",
       "      <th>Wastes generated in the own local unit 1000 t</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>EA-01</td>\n",
       "      <td>Manufacture of food products, beverages, tobacco</td>\n",
       "      <td>Wastes resulting from expl., mining of minerals</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2017</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>EA-02</td>\n",
       "      <td>Manufacture of food products, beverages, tobacco</td>\n",
       "      <td>Wastes from agriculture,horticulture,forestry ...</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2017</td>\n",
       "      <td>26619.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>EA-03</td>\n",
       "      <td>Manufacture of food products, beverages, tobacco</td>\n",
       "      <td>Wastes from wood processing etc.</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2017</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>EA-04</td>\n",
       "      <td>Manufacture of food products, beverages, tobacco</td>\n",
       "      <td>Wastes from the leather, fur, textile industries</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>EA-05</td>\n",
       "      <td>Manufacture of food products, beverages, tobacco</td>\n",
       "      <td>Wastes from petrol. refining, natural gas puri...</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2017</td>\n",
       "      <td>835.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0     ID                                        Categories  \\\n",
       "0           0  EA-01  Manufacture of food products, beverages, tobacco   \n",
       "1           1  EA-02  Manufacture of food products, beverages, tobacco   \n",
       "2           2  EA-03  Manufacture of food products, beverages, tobacco   \n",
       "3           3  EA-04  Manufacture of food products, beverages, tobacco   \n",
       "4           4  EA-05  Manufacture of food products, beverages, tobacco   \n",
       "\n",
       "                                          Waste Type  Waste producers(No.)  \\\n",
       "0    Wastes resulting from expl., mining of minerals                  27.0   \n",
       "1  Wastes from agriculture,horticulture,forestry ...                  27.0   \n",
       "2                   Wastes from wood processing etc.                  27.0   \n",
       "3   Wastes from the leather, fur, textile industries                  27.0   \n",
       "4  Wastes from petrol. refining, natural gas puri...                  27.0   \n",
       "\n",
       "   Waste quantities per 1000 tons  \\\n",
       "0                             0.7   \n",
       "1                             0.7   \n",
       "2                             0.7   \n",
       "3                             0.7   \n",
       "4                             0.7   \n",
       "\n",
       "   Waste quantities generated by primary producers per 1000 tons  Year  \\\n",
       "0                                                0.9              2017   \n",
       "1                                                0.9              2017   \n",
       "2                                                0.9              2017   \n",
       "3                                                0.9              2017   \n",
       "4                                                0.9              2017   \n",
       "\n",
       "   Wastes generated in the own local unit 1000 t  \n",
       "0                                           27.0  \n",
       "1                                        26619.5  \n",
       "2                                            3.6  \n",
       "3                                            0.0  \n",
       "4                                          835.2  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "waste_categories.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "3d268041",
   "metadata": {},
   "outputs": [],
   "source": [
    "waste_categories = waste_categories.rename(columns = {\"Id\":\"ID\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f4e6d3c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.rename(columns={\"Id\":\"ID\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ce03dcbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "keys_matches = ~df[\"ID\"].isin(waste_categories[\"ID\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c92dede3",
   "metadata": {},
   "outputs": [],
   "source": [
    "matched = df[keys_matches]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "aa0f595c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([], dtype=object)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matched[\"ID\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d6017152",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.merge(left=df, right=waste_categories.loc[:, [\"ID\",\"Categories\",\"Waste Type\"]], on=\"ID\", how=\"right\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "b7c5184d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Type_of_plant</th>\n",
       "      <th>Input_to_waste_treatment_plants</th>\n",
       "      <th>waste_generated_in_local_unit</th>\n",
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       "      <th>Year</th>\n",
       "      <th>State</th>\n",
       "      <th>Categories</th>\n",
       "      <th>Waste Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EA-01</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Manufacture of food products, beverages, tobacco</td>\n",
       "      <td>Wastes resulting from expl., mining of minerals</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EA-02</td>\n",
       "      <td>Thermal waste treatment plants</td>\n",
       "      <td>1856.4</td>\n",
       "      <td>39.3</td>\n",
       "      <td>1785.6</td>\n",
       "      <td>31.5</td>\n",
       "      <td>498.1</td>\n",
       "      <td>17.1</td>\n",
       "      <td>477.2</td>\n",
       "      <td>3.7</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>Baden-Württemberg</td>\n",
       "      <td>Manufacture of food products, beverages, tobacco</td>\n",
       "      <td>Wastes from agriculture,horticulture,forestry ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EA-03</td>\n",
       "      <td>Combustion plants</td>\n",
       "      <td>2411.2</td>\n",
       "      <td>417.2</td>\n",
       "      <td>1977.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>177.7</td>\n",
       "      <td>81.6</td>\n",
       "      <td>95.8</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>Baden-Württemberg</td>\n",
       "      <td>Manufacture of food products, beverages, tobacco</td>\n",
       "      <td>Wastes from wood processing etc.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EA-04</td>\n",
       "      <td>Chemical-physical treatment plants</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>355.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>590.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>380.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>Baden-Württemberg</td>\n",
       "      <td>Manufacture of food products, beverages, tobacco</td>\n",
       "      <td>Wastes from the leather, fur, textile industries</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EA-05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Manufacture of food products, beverages, tobacco</td>\n",
       "      <td>Wastes from petrol. refining, natural gas puri...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ID                       Type_of_plant  Input_to_waste_treatment_plants  \\\n",
       "0  EA-01                                 NaN                              NaN   \n",
       "1  EA-02      Thermal waste treatment plants                           1856.4   \n",
       "2  EA-03                   Combustion plants                           2411.2   \n",
       "3  EA-04  Chemical-physical treatment plants                           1100.0   \n",
       "4  EA-05                                 NaN                              NaN   \n",
       "\n",
       "   waste_generated_in_local_unit  Waste_delivered_from_domestic_territory  \\\n",
       "0                            NaN                                      NaN   \n",
       "1                           39.3                                   1785.6   \n",
       "2                          417.2                                   1977.0   \n",
       "3                          155.0                                    355.0   \n",
       "4                            NaN                                      NaN   \n",
       "\n",
       "   Wastes_delivered_from_abroad  Output_of_waste_treatment_plants  \\\n",
       "0                           NaN                               NaN   \n",
       "1                          31.5                             498.1   \n",
       "2                          17.0                             177.7   \n",
       "3                          35.0                             590.0   \n",
       "4                           NaN                               NaN   \n",
       "\n",
       "   Disposed_of_waste  Wastes_to_be_recoverd  \\\n",
       "0                NaN                    NaN   \n",
       "1               17.1                  477.2   \n",
       "2               81.6                   95.8   \n",
       "3               45.0                  380.0   \n",
       "4                NaN                    NaN   \n",
       "\n",
       "   Distribution_to_other_plants_users_or_traders  \\\n",
       "0                                            NaN   \n",
       "1                                            3.7   \n",
       "2                                            0.3   \n",
       "3                                          185.0   \n",
       "4                                            NaN   \n",
       "\n",
       "   Waste_quantities_generated_by_primary_producers/1000tons    Year  \\\n",
       "0                                                NaN            NaN   \n",
       "1                                                0.9         2017.0   \n",
       "2                                                0.9         2017.0   \n",
       "3                                                0.9         2017.0   \n",
       "4                                                NaN            NaN   \n",
       "\n",
       "               State                                        Categories  \\\n",
       "0                NaN  Manufacture of food products, beverages, tobacco   \n",
       "1  Baden-Württemberg  Manufacture of food products, beverages, tobacco   \n",
       "2  Baden-Württemberg  Manufacture of food products, beverages, tobacco   \n",
       "3  Baden-Württemberg  Manufacture of food products, beverages, tobacco   \n",
       "4                NaN  Manufacture of food products, beverages, tobacco   \n",
       "\n",
       "                                          Waste Type  \n",
       "0    Wastes resulting from expl., mining of minerals  \n",
       "1  Wastes from agriculture,horticulture,forestry ...  \n",
       "2                   Wastes from wood processing etc.  \n",
       "3   Wastes from the leather, fur, textile industries  \n",
       "4  Wastes from petrol. refining, natural gas puri...  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "fadc950a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(axis=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "438702e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 491 entries, 1 to 769\n",
      "Data columns (total 15 columns):\n",
      " #   Column                                                    Non-Null Count  Dtype  \n",
      "---  ------                                                    --------------  -----  \n",
      " 0   ID                                                        491 non-null    object \n",
      " 1   Type_of_plant                                             491 non-null    object \n",
      " 2   Input_to_waste_treatment_plants                           491 non-null    float64\n",
      " 3   waste_generated_in_local_unit                             491 non-null    float64\n",
      " 4   Waste_delivered_from_domestic_territory                   491 non-null    float64\n",
      " 5   Wastes_delivered_from_abroad                              491 non-null    float64\n",
      " 6   Output_of_waste_treatment_plants                          491 non-null    float64\n",
      " 7   Disposed_of_waste                                         491 non-null    float64\n",
      " 8   Wastes_to_be_recoverd                                     491 non-null    float64\n",
      " 9   Distribution_to_other_plants_users_or_traders             491 non-null    float64\n",
      " 10  Waste_quantities_generated_by_primary_producers/1000tons  491 non-null    float64\n",
      " 11  Year                                                      491 non-null    float64\n",
      " 12  State                                                     491 non-null    object \n",
      " 13  Categories                                                491 non-null    object \n",
      " 14  Waste Type                                                491 non-null    object \n",
      "dtypes: float64(10), object(5)\n",
      "memory usage: 61.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "8123a999",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 491 entries, 1 to 769\n",
      "Data columns (total 15 columns):\n",
      " #   Column                                                    Non-Null Count  Dtype  \n",
      "---  ------                                                    --------------  -----  \n",
      " 0   ID                                                        491 non-null    object \n",
      " 1   Type_of_plant                                             491 non-null    object \n",
      " 2   Input_to_waste_treatment_plants                           491 non-null    float64\n",
      " 3   waste_generated_in_local_unit                             491 non-null    float64\n",
      " 4   Waste_delivered_from_domestic_territory                   491 non-null    float64\n",
      " 5   Wastes_delivered_from_abroad                              491 non-null    float64\n",
      " 6   Output_of_waste_treatment_plants                          491 non-null    float64\n",
      " 7   Disposed_of_waste                                         491 non-null    float64\n",
      " 8   Wastes_to_be_recoverd                                     491 non-null    float64\n",
      " 9   Distribution_to_other_plants_users_or_traders             491 non-null    float64\n",
      " 10  Waste_quantities_generated_by_primary_producers/1000tons  491 non-null    float64\n",
      " 11  Year                                                      491 non-null    float64\n",
      " 12  State                                                     491 non-null    object \n",
      " 13  Categories                                                491 non-null    object \n",
      " 14  Waste Type                                                491 non-null    object \n",
      "dtypes: float64(10), object(5)\n",
      "memory usage: 61.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "35b8413f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename(columns={\"Waste Type\":\"Waste_Type\"},inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65962ded",
   "metadata": {},
   "source": [
    "# DATA STORAGE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "544f160b",
   "metadata": {},
   "outputs": [],
   "source": [
    "## In this step we store our data into our database. The database is in Postgresql. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "8a32ca88",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import psycopg2\n",
    "#from sqlalchemy import create_engine\n",
    "\n",
    "# Connect to the database\n",
    "#conn = psycopg2.connect(\n",
    "  #  host=\"localhost\",\n",
    "  #  database=\"Waste_generation\",\n",
    "  #  user=\"postgres\",\n",
    "   # password=\"Imamali@143\"\n",
    "#)\n",
    "\n",
    "# Create an SQLAlchemy engine from the connection\n",
    "#engine = create_engine('postgresql+psycopg2://', creator=lambda: conn)\n",
    "\n",
    "# Define the DataFrame\n",
    "#df = ...\n",
    "\n",
    "# Write the DataFrame to a table in the database\n",
    "#table_name = 'Waste_Generation'\n",
    "#df.to_sql('Waste_Generation', engine, if_exists='replace', index=False)\n",
    "\n",
    "#Close the database connection\n",
    "#conn.close()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97f028b1",
   "metadata": {},
   "source": [
    "# DATA PRE-PROCESSING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "f9c087c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Under this section we will be doing Data pre-processing in relation with the data analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2be37f5b",
   "metadata": {},
   "source": [
    "# Feature Engineering\n",
    "In this section we would look at different columns and would try to do feature engineering carving out new features and deleting those that are not relevant anymore."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "60b80b95",
   "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>ID</th>\n",
       "      <th>Type_of_plant</th>\n",
       "      <th>Input_to_waste_treatment_plants</th>\n",
       "      <th>waste_generated_in_local_unit</th>\n",
       "      <th>Waste_delivered_from_domestic_territory</th>\n",
       "      <th>Wastes_delivered_from_abroad</th>\n",
       "      <th>Output_of_waste_treatment_plants</th>\n",
       "      <th>Disposed_of_waste</th>\n",
       "      <th>Wastes_to_be_recoverd</th>\n",
       "      <th>Distribution_to_other_plants_users_or_traders</th>\n",
       "      <th>Waste_quantities_generated_by_primary_producers/1000tons</th>\n",
       "      <th>Year</th>\n",
       "      <th>State</th>\n",
       "      <th>Categories</th>\n",
       "      <th>Waste_Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EA-02</td>\n",
       "      <td>Thermal waste treatment plants</td>\n",
       "      <td>1856.4</td>\n",
       "      <td>39.3</td>\n",
       "      <td>1785.6</td>\n",
       "      <td>31.5</td>\n",
       "      <td>498.1</td>\n",
       "      <td>17.1</td>\n",
       "      <td>477.2</td>\n",
       "      <td>3.7</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>Baden-Württemberg</td>\n",
       "      <td>Manufacture of food products, beverages, tobacco</td>\n",
       "      <td>Wastes from agriculture,horticulture,forestry ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EA-03</td>\n",
       "      <td>Combustion plants</td>\n",
       "      <td>2411.2</td>\n",
       "      <td>417.2</td>\n",
       "      <td>1977.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>177.7</td>\n",
       "      <td>81.6</td>\n",
       "      <td>95.8</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>Baden-Württemberg</td>\n",
       "      <td>Manufacture of food products, beverages, tobacco</td>\n",
       "      <td>Wastes from wood processing etc.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ID                   Type_of_plant  Input_to_waste_treatment_plants  \\\n",
       "1  EA-02  Thermal waste treatment plants                           1856.4   \n",
       "2  EA-03               Combustion plants                           2411.2   \n",
       "\n",
       "   waste_generated_in_local_unit  Waste_delivered_from_domestic_territory  \\\n",
       "1                           39.3                                   1785.6   \n",
       "2                          417.2                                   1977.0   \n",
       "\n",
       "   Wastes_delivered_from_abroad  Output_of_waste_treatment_plants  \\\n",
       "1                          31.5                             498.1   \n",
       "2                          17.0                             177.7   \n",
       "\n",
       "   Disposed_of_waste  Wastes_to_be_recoverd  \\\n",
       "1               17.1                  477.2   \n",
       "2               81.6                   95.8   \n",
       "\n",
       "   Distribution_to_other_plants_users_or_traders  \\\n",
       "1                                            3.7   \n",
       "2                                            0.3   \n",
       "\n",
       "   Waste_quantities_generated_by_primary_producers/1000tons    Year  \\\n",
       "1                                                0.9         2017.0   \n",
       "2                                                0.9         2017.0   \n",
       "\n",
       "               State                                        Categories  \\\n",
       "1  Baden-Württemberg  Manufacture of food products, beverages, tobacco   \n",
       "2  Baden-Württemberg  Manufacture of food products, beverages, tobacco   \n",
       "\n",
       "                                          Waste_Type  \n",
       "1  Wastes from agriculture,horticulture,forestry ...  \n",
       "2                   Wastes from wood processing etc.  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "53d2d1ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['total_waste_generated'] = df['waste_generated_in_local_unit'] + df['Waste_delivered_from_domestic_territory'] + df['Wastes_delivered_from_abroad'] + (df['Waste_quantities_generated_by_primary_producers/1000tons']*1000)+df[\"Distribution_to_other_plants_users_or_traders\"]+df[\"Wastes_to_be_recoverd\"]+df[\"Disposed_of_waste\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "8fb99df2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#df['Total_waste_delivered'] = df['Waste_delivered_from_domestic_territory'] + df['Wastes_delivered_from_abroad']\n",
    "df['waste_treatment_rate'] = df['Output_of_waste_treatment_plants'] / df['Input_to_waste_treatment_plants']*100\n",
    "df['Total_recovered_materials'] = df['Wastes_to_be_recoverd'] + df['Distribution_to_other_plants_users_or_traders']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "c8936c85",
   "metadata": {},
   "outputs": [],
   "source": [
    "conversion_factor = 0.5\n",
    "df['electricity_generation_potential'] = df['Total_recovered_materials'] * conversion_factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "98cb6875",
   "metadata": {},
   "outputs": [],
   "source": [
    "#for column in df.select_dtypes(include=np.number):\n",
    "   # mask = np.isinf(df[column])\n",
    "   # df.loc[mask, column] = np.nan\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "d7afd2db",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(axis=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "18d41ba8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Average_waste_generation_per_State'] = df.groupby('State')['total_waste_generated'].transform('mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "92293537",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 491 entries, 1 to 769\n",
      "Data columns (total 20 columns):\n",
      " #   Column                                                    Non-Null Count  Dtype  \n",
      "---  ------                                                    --------------  -----  \n",
      " 0   ID                                                        491 non-null    object \n",
      " 1   Type_of_plant                                             491 non-null    object \n",
      " 2   Input_to_waste_treatment_plants                           491 non-null    float64\n",
      " 3   waste_generated_in_local_unit                             491 non-null    float64\n",
      " 4   Waste_delivered_from_domestic_territory                   491 non-null    float64\n",
      " 5   Wastes_delivered_from_abroad                              491 non-null    float64\n",
      " 6   Output_of_waste_treatment_plants                          491 non-null    float64\n",
      " 7   Disposed_of_waste                                         491 non-null    float64\n",
      " 8   Wastes_to_be_recoverd                                     491 non-null    float64\n",
      " 9   Distribution_to_other_plants_users_or_traders             491 non-null    float64\n",
      " 10  Waste_quantities_generated_by_primary_producers/1000tons  491 non-null    float64\n",
      " 11  Year                                                      491 non-null    float64\n",
      " 12  State                                                     491 non-null    object \n",
      " 13  Categories                                                491 non-null    object \n",
      " 14  Waste_Type                                                491 non-null    object \n",
      " 15  total_waste_generated                                     491 non-null    float64\n",
      " 16  waste_treatment_rate                                      491 non-null    float64\n",
      " 17  Total_recovered_materials                                 491 non-null    float64\n",
      " 18  electricity_generation_potential                          491 non-null    float64\n",
      " 19  Average_waste_generation_per_State                        491 non-null    float64\n",
      "dtypes: float64(15), object(5)\n",
      "memory usage: 80.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "522a2d31",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(df[[\"Waste_quantities_generated_by_primary_producers/1000tons\",\"waste_generated_in_local_unit\",\"Input_to_waste_treatment_plants\",\"Output_of_waste_treatment_plants\",\"Wastes_to_be_recoverd\",\"Distribution_to_other_plants_users_or_traders\"]],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "ac2e3d97",
   "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>ID</th>\n",
       "      <th>Type_of_plant</th>\n",
       "      <th>Waste_delivered_from_domestic_territory</th>\n",
       "      <th>Wastes_delivered_from_abroad</th>\n",
       "      <th>Disposed_of_waste</th>\n",
       "      <th>Year</th>\n",
       "      <th>State</th>\n",
       "      <th>Categories</th>\n",
       "      <th>Waste_Type</th>\n",
       "      <th>total_waste_generated</th>\n",
       "      <th>waste_treatment_rate</th>\n",
       "      <th>Total_recovered_materials</th>\n",
       "      <th>electricity_generation_potential</th>\n",
       "      <th>Average_waste_generation_per_State</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EA-02</td>\n",
       "      <td>Thermal waste treatment plants</td>\n",
       "      <td>1785.6</td>\n",
       "      <td>31.5</td>\n",
       "      <td>17.1</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>Baden-Württemberg</td>\n",
       "      <td>Manufacture of food products, beverages, tobacco</td>\n",
       "      <td>Wastes from agriculture,horticulture,forestry ...</td>\n",
       "      <td>3254.4</td>\n",
       "      <td>26.831502</td>\n",
       "      <td>480.9</td>\n",
       "      <td>240.45</td>\n",
       "      <td>10413.840541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EA-03</td>\n",
       "      <td>Combustion plants</td>\n",
       "      <td>1977.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>81.6</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>Baden-Württemberg</td>\n",
       "      <td>Manufacture of food products, beverages, tobacco</td>\n",
       "      <td>Wastes from wood processing etc.</td>\n",
       "      <td>3488.9</td>\n",
       "      <td>7.369774</td>\n",
       "      <td>96.1</td>\n",
       "      <td>48.05</td>\n",
       "      <td>10413.840541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EA-04</td>\n",
       "      <td>Chemical-physical treatment plants</td>\n",
       "      <td>355.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>Baden-Württemberg</td>\n",
       "      <td>Manufacture of food products, beverages, tobacco</td>\n",
       "      <td>Wastes from the leather, fur, textile industries</td>\n",
       "      <td>2055.0</td>\n",
       "      <td>53.636364</td>\n",
       "      <td>565.0</td>\n",
       "      <td>282.50</td>\n",
       "      <td>10413.840541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>EA-09</td>\n",
       "      <td>Shredding plants and scrap shears</td>\n",
       "      <td>1949.9</td>\n",
       "      <td>108.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>Baden-Württemberg</td>\n",
       "      <td>Manufacture of food products, beverages, tobacco</td>\n",
       "      <td>Wastes from the photographic industry</td>\n",
       "      <td>5127.3</td>\n",
       "      <td>100.802774</td>\n",
       "      <td>2121.9</td>\n",
       "      <td>1060.95</td>\n",
       "      <td>10413.840541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>EA-10</td>\n",
       "      <td>Sorting plants</td>\n",
       "      <td>2333.9</td>\n",
       "      <td>212.8</td>\n",
       "      <td>8.2</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>Baden-Württemberg</td>\n",
       "      <td>Manufacture of food products, beverages, tobacco</td>\n",
       "      <td>Wastes from surface treatment of metals a.plas...</td>\n",
       "      <td>6057.0</td>\n",
       "      <td>98.304172</td>\n",
       "      <td>2548.2</td>\n",
       "      <td>1274.10</td>\n",
       "      <td>10413.840541</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ID                       Type_of_plant  \\\n",
       "1  EA-02      Thermal waste treatment plants   \n",
       "2  EA-03                   Combustion plants   \n",
       "3  EA-04  Chemical-physical treatment plants   \n",
       "8  EA-09   Shredding plants and scrap shears   \n",
       "9  EA-10                      Sorting plants   \n",
       "\n",
       "   Waste_delivered_from_domestic_territory  Wastes_delivered_from_abroad  \\\n",
       "1                                   1785.6                          31.5   \n",
       "2                                   1977.0                          17.0   \n",
       "3                                    355.0                          35.0   \n",
       "8                                   1949.9                         108.0   \n",
       "9                                   2333.9                         212.8   \n",
       "\n",
       "   Disposed_of_waste    Year              State  \\\n",
       "1               17.1  2017.0  Baden-Württemberg   \n",
       "2               81.6  2017.0  Baden-Württemberg   \n",
       "3               45.0  2017.0  Baden-Württemberg   \n",
       "8                0.3  2017.0  Baden-Württemberg   \n",
       "9                8.2  2017.0  Baden-Württemberg   \n",
       "\n",
       "                                         Categories  \\\n",
       "1  Manufacture of food products, beverages, tobacco   \n",
       "2  Manufacture of food products, beverages, tobacco   \n",
       "3  Manufacture of food products, beverages, tobacco   \n",
       "8  Manufacture of food products, beverages, tobacco   \n",
       "9  Manufacture of food products, beverages, tobacco   \n",
       "\n",
       "                                          Waste_Type  total_waste_generated  \\\n",
       "1  Wastes from agriculture,horticulture,forestry ...                 3254.4   \n",
       "2                   Wastes from wood processing etc.                 3488.9   \n",
       "3   Wastes from the leather, fur, textile industries                 2055.0   \n",
       "8              Wastes from the photographic industry                 5127.3   \n",
       "9  Wastes from surface treatment of metals a.plas...                 6057.0   \n",
       "\n",
       "   waste_treatment_rate  Total_recovered_materials  \\\n",
       "1             26.831502                      480.9   \n",
       "2              7.369774                       96.1   \n",
       "3             53.636364                      565.0   \n",
       "8            100.802774                     2121.9   \n",
       "9             98.304172                     2548.2   \n",
       "\n",
       "   electricity_generation_potential  Average_waste_generation_per_State  \n",
       "1                            240.45                        10413.840541  \n",
       "2                             48.05                        10413.840541  \n",
       "3                            282.50                        10413.840541  \n",
       "8                           1060.95                        10413.840541  \n",
       "9                           1274.10                        10413.840541  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "b0ba60e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 491 entries, 1 to 769\n",
      "Data columns (total 14 columns):\n",
      " #   Column                                   Non-Null Count  Dtype  \n",
      "---  ------                                   --------------  -----  \n",
      " 0   ID                                       491 non-null    object \n",
      " 1   Type_of_plant                            491 non-null    object \n",
      " 2   Waste_delivered_from_domestic_territory  491 non-null    float64\n",
      " 3   Wastes_delivered_from_abroad             491 non-null    float64\n",
      " 4   Disposed_of_waste                        491 non-null    float64\n",
      " 5   Year                                     491 non-null    float64\n",
      " 6   State                                    491 non-null    object \n",
      " 7   Categories                               491 non-null    object \n",
      " 8   Waste_Type                               491 non-null    object \n",
      " 9   total_waste_generated                    491 non-null    float64\n",
      " 10  waste_treatment_rate                     491 non-null    float64\n",
      " 11  Total_recovered_materials                491 non-null    float64\n",
      " 12  electricity_generation_potential         491 non-null    float64\n",
      " 13  Average_waste_generation_per_State       491 non-null    float64\n",
      "dtypes: float64(9), object(5)\n",
      "memory usage: 57.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "41499190",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "797fa7c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Manufacture of food products, beverages, tobacco',\n",
       "       'Textiles,textile products,leather,leather products',\n",
       "       'Manuf. of wood, products of wood and cork etc.',\n",
       "       'Manuf. of paper, paper products, recorded media',\n",
       "       'Manufacture of coke and refined petroleum products',\n",
       "       'Manufacture of chemicals and chemical products',\n",
       "       'Manuf. of basic pharmaceutical products and prep.',\n",
       "       'Manufacture of rubber and plastic products',\n",
       "       'Manufacture of other nonmetallic mineral products',\n",
       "       'Manuf. of fabricated metal products, ex. machinery',\n",
       "       'Manufacture of computer and electrical equipment',\n",
       "       'Manufacture of furniture and other manufacturing',\n",
       "       'Repair and installation of machinery and equipment',\n",
       "       'Electricity, gas, steam, air conditioning supply'], dtype=object)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Categories\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f657621e",
   "metadata": {},
   "source": [
    "## Now we will divide the categories into five distinct groups based on similar characterisitics.\n",
    "1) Food, Beverages, and Tobacco Manufacturing:\n",
    "\n",
    "- Manufacture of food products, beverages, tobacco\n",
    "\n",
    "2) Textile, Leather, and Paper Manufacturing:\n",
    "\n",
    "- Textiles, textile products, leather, leather products\n",
    "- Manuf. of wood, products of wood and cork etc.\n",
    "- Manuf. of paper, paper products, recorded media\n",
    "\n",
    "3) Chemical and Pharmaceutical Manufacturing:\n",
    "\n",
    "- Manufacture of coke and refined petroleum products\n",
    "- Manufacture of chemicals and chemical products\n",
    "- Manuf. of basic pharmaceutical products and prep.\n",
    "\n",
    "4) Plastics, Metals, and Other Manufacturing:\n",
    "\n",
    "- Manufacture of rubber and plastic products\n",
    "- Manufacture of other nonmetallic mineral products\n",
    "- Manuf. of fabricated metal products, ex. machinery\n",
    "- Manufacture of furniture and other manufacturing\n",
    "\n",
    "5) Machinery and Equipment Repair and Installation, and Electricity Supply:\n",
    "\n",
    "- Repair and installation of machinery and equipment\n",
    "- Electricity, gas, steam, air conditioning supply"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "4a025ac6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# create a dictionary that maps original categories to the five groups\n",
    "category_map = {\n",
    "    'Manufacture of food products, beverages, tobacco': 'Food, Beverages, and Tobacco Manufacturing',\n",
    "    'Textiles,textile products,leather,leather products': 'Textile, Leather, and Paper Manufacturing',\n",
    "    'Manuf. of wood, products of wood and cork etc.': 'Textile, Leather, and Paper Manufacturing',\n",
    "    'Manuf. of paper, paper products, recorded media': 'Textile, Leather, and Paper Manufacturing',\n",
    "    'Manufacture of coke and refined petroleum products': 'Chemical and Pharmaceutical Manufacturing',\n",
    "    'Manufacture of chemicals and chemical products': 'Chemical and Pharmaceutical Manufacturing',\n",
    "    'Manuf. of basic pharmaceutical products and prep.': 'Chemical and Pharmaceutical Manufacturing',\n",
    "    'Manufacture of rubber and plastic products': 'Plastics, Metals, and Other Manufacturing',\n",
    "    'Manufacture of other nonmetallic mineral products': 'Plastics, Metals, and Other Manufacturing',\n",
    "    'Manuf. of fabricated metal products, ex. machinery': 'Plastics, Metals, and Other Manufacturing',\n",
    "    'Manufacture of computer and electrical equipment': 'Plastics, Metals, and Other Manufacturing',\n",
    "    'Manufacture of furniture and other manufacturing': 'Plastics, Metals, and Other Manufacturing',\n",
    "    'Repair and installation of machinery and equipment': 'Machinery and Equipment Repair and Installation, and Electricity Supply',\n",
    "    'Electricity, gas, steam, air conditioning supply': 'Machinery and Equipment Repair and Installation, and Electricity Supply'\n",
    "}\n",
    "\n",
    "# create a new column 'category_group' based on the mapping\n",
    "df[\"Categories\"] = df[\"Categories\"].map(category_map)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "05336853",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Categories\"].unique()\n",
    "category_map_further = {'Food, Beverages, and Tobacco Manufacturing': 'Food & Beverage Production',\n",
    "                'Textile, Leather, and Paper Manufacturing': \"Textile & Material Production\",\n",
    "                'Chemical and Pharmaceutical Manufacturing': 'Chemical & Pharma Production.',\n",
    "                'Plastics, Metals, and Other Manufacturing': 'Materials Manufacturing',\n",
    "                'Machinery and Equipment Repair and Installation, and Electricity Supply': 'Equipment Maintenance & Repair'}\n",
    "df[\"Categories\"] = df[\"Categories\"].map(category_map_further)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "c6f212f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#category_mapping = {\"Manufacture of food products, beverages, tobacco\":\"Food, Beverages, Tobacco\",\n",
    "                    #\"Textiles, textile products, leather, leather products\":\"Textiles, Leather, Wood, Paper\",\n",
    "                   # \"Manuf. of wood, products of wood and cork etc.\":\"Textiles, Leather, Wood, Paper\",\n",
    "                   # \"Manuf. of paper, paper products, recorded media\":\"Textiles, Leather, Wood, Paper\",\n",
    "                  #  \"Manufacture of coke and refined petroleum products\":\"Petroleum, Chemicals, Pharmaceuticals, Plastics\",\n",
    "                  #  \"Manufacture of chemicals and chemical products\":\"Petroleum, Chemicals, Pharmaceuticals, Plastics\",\n",
    "                  #  \"Manuf. of basic pharmaceutical products and prep.\":\"Petroleum, Chemicals, Pharmaceuticals, Plastics\",\n",
    "                  # \"Manufacture of rubber and plastic products\":\"Metal Products, Electrical Equipment, Furniture\",\n",
    "                   # \"Manufacture of other nonmetallic mineral products\":\"Metal Products, Electrical Equipment, Furniture\",\n",
    "                   # \"Manuf. of fabricated metal products, ex. machinery\":\"Metal Products, Electrical Equipment, Furniture\",\n",
    "                  #  \"Manufacture of furniture and other manufacturing\":\"Metal Products, Electrical Equipment, Furniture\",\n",
    "                   # \"Repair and installation of machinery and equipment\":\"Machinery, Installation, Electricity Supply\",\n",
    "                   # \"Electricity, gas, steam, air conditioning supply\":\"Machinery, Installation, Electricity Supply\"}\n",
    "\n",
    "#df[\"Categories\"] = df[\"Categories\"].map(category_mapping)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "dbd81f67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Thermal waste treatment plants', 'Combustion plants',\n",
       "       'Chemical-physical treatment plants',\n",
       "       'Shredding plants and scrap shears', 'Sorting plants',\n",
       "       'Dismantling facilities', 'Landfills',\n",
       "       'Wastes at surface extraction sites', 'Other treatment plants',\n",
       "       'Natural substances produced in mining',\n",
       "       'Wastes at underground extraction sites', 'Soil treatment plants',\n",
       "       'Dismantling facilities for end-of-life vehicles',\n",
       "       'Biological treatment plants',\n",
       "       'Mechanical-biological waste treatment plants'], dtype=object)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Type_of_plant\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f10e42fe",
   "metadata": {},
   "source": [
    "# Similarly we would categorize the type of plants into different categories based on their similar features:\n",
    "\n",
    "1) Thermal/Combustion Plants: \n",
    " - Thermal waste treatment plants\n",
    " - Combustion plants\n",
    " \n",
    "2) Chemical/Physical Treatment Plants:\n",
    " - Chemical-physical treatment plants\n",
    " \n",
    "3) Recycling/Sorting Plants:\n",
    " - Shredding plants and scrap shears\n",
    " - Sorting plants\n",
    " - Dismantling facilities\n",
    " - Dismantling facilities for end-of-life vehicles\n",
    " \n",
    "4) Landfills/Disposal Sites:\n",
    " - Landfills\n",
    " - Wastes at surface extraction sites\n",
    " - Other treatment plants\n",
    " - Natural substances produced in mining\n",
    " - Wastes at underground extraction sites\n",
    " - Soil treatment plants\n",
    " \n",
    "5) Biological/Mechanical Treatment Plants\n",
    " - Biological treatment plants\n",
    " - Mechanical-biological waste treatment plants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "415fddbe",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Type_of_plant'] = df['Type_of_plant'].replace(['Thermal waste treatment plants', 'Combustion plants'], 'Thermal/Combustion Plants')\n",
    "df['Type_of_plant'] = df['Type_of_plant'].replace('Chemical-physical treatment plants', 'Chemical/Physical Treatment Plants')\n",
    "df['Type_of_plant'] = df['Type_of_plant'].replace(['Shredding plants and scrap shears', 'Sorting plants', 'Dismantling facilities', 'Dismantling facilities for end-of-life vehicles'], 'Recycling/Sorting Plants')\n",
    "df['Type_of_plant'] = df['Type_of_plant'].replace(['Landfills', 'Wastes at surface extraction sites', 'Other treatment plants', 'Natural substances produced in mining', 'Wastes at underground extraction sites', 'Soil treatment plants'], 'Landfills/Disposal Sites')\n",
    "df['Type_of_plant'] = df['Type_of_plant'].replace(['Biological treatment plants', 'Mechanical-biological waste treatment plants'], 'Biological/Mechanical Treatment Plants')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "12e6cdd3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Wastes from agriculture,horticulture,forestry etc.',\n",
       "       'Wastes from wood processing etc.',\n",
       "       'Wastes from the leather, fur, textile industries',\n",
       "       'Wastes from the photographic industry',\n",
       "       'Wastes from surface treatment of metals a.plastics',\n",
       "       'Waste organic solvents, refrigerants, propellants',\n",
       "       'Municipal wastes (household waste, similar wastes)',\n",
       "       'Wastes resulting from expl., mining of minerals',\n",
       "       'Wastes from organic chemical processes',\n",
       "       'Wastes from the MFSU of coatings, adhesives etc.',\n",
       "       'Wastes from human or animal health care',\n",
       "       'Wastes from waste management facilities etc.',\n",
       "       'Wastes from petrol. refining, natural gas purific.',\n",
       "       'Wastes from inorganic chemical processes'], dtype=object)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Waste_Type\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "767cda8e",
   "metadata": {},
   "source": [
    "# Types of Wastes\n",
    "We would also categorized the type of wastes based on which waste can be used in which type to produce\n",
    "electricity. Here are the categories:\n",
    "\n",
    "1) Biogas Wastes: \n",
    "    \n",
    "    -Wastes from agriculture,horticulture,forestry etc.\n",
    "\n",
    "2) Biomass: \n",
    "\n",
    "    -Wastes from wood processing etc.\n",
    "\n",
    "3) Industrial waste:\n",
    "\n",
    "\n",
    "    -Wastes from the leather, fur, textile industries \n",
    "    -Wastes from the photographic industry\n",
    "    -Wastes from surface treatment of metals a.plastics \n",
    "    -Wastes from organic chemical processes\n",
    "    -Wastes from the MFSU of coatings, adhesives etc.\n",
    "    -Wastes from petrol. refining, natural gas purific. \n",
    "    -Wastes from inorganic chemical processes\n",
    "\n",
    "4) Municipal solid waste:\n",
    "\n",
    "    -Municipal wastes (household waste, similar wastes)\n",
    "\n",
    "5) Hazardous waste:\n",
    "\n",
    "    -Waste organic solvents, refrigerants, propellants\n",
    "    -Wastes resulting from expl., mining of minerals\n",
    "    -Wastes from human or animal health care\n",
    "    -Wastes from waste management facilities etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "b8914af8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a dictionary to map waste types to categories\n",
    "waste_types = {\n",
    "    'Wastes from agriculture,horticulture,forestry etc.': 'Biogas Waste',\n",
    "    'Wastes from wood processing etc.': 'Biomass Waste',\n",
    "    'Wastes from the leather, fur, textile industries': 'Textile and leather waste',\n",
    "    'Wastes from the photographic industry': 'Industrial waste',\n",
    "    'Wastes from surface treatment of metals a.plastics': 'Industrial waste',\n",
    "    'Waste organic solvents, refrigerants, propellants': 'Hazardous waste',\n",
    "    'Municipal wastes (household waste, similar wastes)': 'Municipal solid waste',\n",
    "    'Wastes resulting from expl., mining of minerals': 'Hazardous waste',\n",
    "    'Wastes from organic chemical processes': 'Industrial waste',\n",
    "    'Wastes from the MFSU of coatings, adhesives etc.': 'Industrial waste',\n",
    "    'Wastes from human or animal health care': 'Hazardous waste',\n",
    "    'Wastes from waste management facilities etc.': 'Municipal solid waste',\n",
    "    'Wastes from petrol. refining, natural gas purific.': 'Industrial waste',\n",
    "    'Wastes from inorganic chemical processes': 'Industrial waste'\n",
    "}\n",
    "\n",
    "# Map waste types to categories and create a new column in the dataframe\n",
    "df['Waste_Type'] = df['Waste_Type'].map(waste_types)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "097a472b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Year'] = df['Year'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "160816f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['State'] = df['State'].replace({\n",
    "    'Nidersachsen': 'Niedersachsen',\n",
    "    'Saschsen': 'Sachsen',\n",
    "    'Saschsen-Anhalt': 'Sachsen-Anhalt',\n",
    "    'Thüringen': 'Thuringen'\n",
    "})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "0291ea06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Biogas Waste', 'Biomass Waste', 'Textile and leather waste',\n",
       "       'Industrial waste', 'Hazardous waste', 'Municipal solid waste'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Waste_Type\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "09803b88",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_excel(\"Waste_Energy_Analysis.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "1ddf08dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XLyzVsbPndbi6KepwAGBMI7kDAAAApBB31y+2HVeamW5ZVi6z5OmO1d3yGcXKzkjT+oPVUYcCAGMayR0AAAAghWyvqNW+M+f0uoumqDAnI+pw+pSZnqZVs0q080Sd6s+3RR0OAIxZJHcAAACAFNHc1qFfPX9SM4pztHZOSdThDMjaOSVylzYerok6FAAYs0juAAAAACniiT1n1NTaoTcvK1daEnfHijcxL0sLy/K16VCN2js7ow4HAMYkkjsAAABACqg+16J1B6q1Ymaxyotyog5nUC6fO1ENLe168UR91KEAwJhEcgcAAABIAb/ZeUoxM91wUVnUoQzagrI8FeVkaOvRs1GHAgBjEskdAAAAIMkdqW7UzhP1umZhqQqyk3sQ5Z6kmWnZzCLtO31O9c0MrAwAiUZyBwAAAEhyj754WnlZ6XrV/ElRhzJky2cUyyVtP1YbdSgAMOaQ3AEAAACS2IHKczpY1ahXLyxVZnrqVt9L87M0ozhHW4+elbtHHQ4AjCmpe3cAAAAAxjh316MvnlZBdrrWpMjU531ZMatYp+tbtJOBlQEgoUjuAAAAAElq35lzOlrTpGsXTVZGLPWr7peWFymWZrp/S0XUoQDAmJL6dwgAAABgjPrdnkoV5mRo1eziqENJiJzMmBaV5euhF06qs5OuWQCQKCR3AAAAgCR0tKZJh6sb9ar5k5SeNnaq7ZeUF+pMQ4u2MC06ACTM2LlLAAAAAGPIk3srlZMRGzOtdrosnpKvzPQ0/er5k1GHAgBjBskdAAAAIMmcaWjWrpP1unxuibLSY1GHk1BZGTFdu7BUv9lxiq5ZAJAgJHcAAACAJPP0virF0kxXzJsUdSgj4uZLpupUfbOeO0bXLABIBJI7AAAAQBJpamnXtmO1WjGzWHlZ6VGHMyKuXzI57Jp1KupQAGBMILkDAAAAJJHNR86qvdN1xbyJUYcyYvKzM3TNglL9esdJudM1CwCGi+QOAAAAkCQ63bX+ULXmTspVWUF21OGMqNdfVKaTdc3aeaI+6lAAIOWR3AEAAACSxO6TDaptatPlc8duq50ur1k8WWbSb3edjjoUAEh5JHcAAACAJLH+YLUKczK0ZGpB1KGMuEl5WVo+o0iP7ToTdSgAkPKGndwxs5+Y2RvMjEQRAABAH6g3oS9V51q0v/Kc1swpUSzNog5nVFy/pEwvHK/T6frmqEMBgJSWiIrF1yW9S9I+M/u8mS3ur4CZ3Whme8xsv5l9sof1ZmZfCdc/b2YrBlH2L83MzWxszhsJAABS2aDrTRg/thw5qzSTVs4qjjqUUfPaJWWSROsdABimYSd33P237v5uSSskHZb0qJmtM7MPmllG9+3NLCbpa5JukrRU0m1mtrTbZjdJWhC+bldQEeq3rJnNkHSDpKPDPS8AAIBEG2y9CeNHR6dr69GzWliWr4Ls8fOrsLAsT9OLc/QY4+4AwLAkpEmwmU2U9AFJfyzpOUlfVlBpebSHzddI2u/uB929VdJ9km7pts0tku71wHpJRWY2dQBlvyjpE5KYTxEAACSlQdabME7sPd2ghuZ2rZpVEnUoo8rM9NolZXp6f5XOt3ZEHQ4ApKxEjLnzU0lPSZog6U3u/mZ3/6G7f0xSXg9FyiUdi/tcES4byDa9ljWzN0s67u7b+4n3djPbbGabKysr+z0/AACARBlCvQnjxOYjZ5WXla5FU/KjDmXUXb9kslraO7XuQFXUoQBAykpPwD6+6e4PxS8wsyx3b3H3VT1s39PocN1b2vS2TY/LzWyCpL+R9Lr+gnX3uyXdLUmrVq2ihQ8AABhNg603YRxoaG7TnlP1etX8SeNmIOV4q2eXKDsjTU/urdT14Rg8AIDBSUS3rH/uYdmzfWxfIWlG3Ofpkk4McJvels+TNEfSdjM7HC7famZTBhA/AADAaBlsvQnjwLZjtep0aeU465LVJTsjpsvnTtST+2i5AwBDNeSWO2HipFxSjpkt10utagoUNDXuzSZJC8xsjqTjkm5VMGtEvAckfdTM7pO0VlKdu580s8qeyrr7TkmT42I7LGmVu3OHAAAAkRtGvQnjwLZjtZpenKPS/KyoQ4nMNQtK9Y97XtSxmibNKOGfBAAM1nC6Zb1ewWCA0yX9R9zyBkmf7q2Qu7eb2UclPSwpJulb7r7TzO4I198l6SFJN0vaL6lJ0gf7KjuMcwAAABgNQ6o3Yew7Xd+sk3XNeuOlU6MOJVLXLCyVJP1+b6Xec/msiKMBgNQz5OSOu98j6R4z+wN3/8kgyz6kIIETv+yuuPcu6c6Blu1hm9mDiQcAAGAkDafehLFt+7FapZl0SXlh1KFEal5prsqLcvQkyR0AGJLhdMt6j7t/V9JsM/s/3de7+3/0UAwAAGDcod6EnnS6a1tFreaV5ik/OyPqcCJlZrpm4ST9cvtJtXV0KiOWiKFBAWD8GM7/mrnhzzxJ+T28AAAAEBh2vcnMYmb2nJk9ODIhYrQdrW5SbVObls0oijqUpHDNglKda2nXc0drow4FAFLOcLpl/Xf48x8SFw4AAMDYk6B6059L2qVgEGaMAdsqapURMy2dxlcqSVeGU8E/ubdSa+aMz5nDAGCoht3e0cz+1cwKzCzDzB4zsyoze08iggMAABhLhlpvMrPpkt4g6ZsjHyVGQ0ena+fxOi2eUqCs9FjU4SSFwpwMXTq9UOsOMOEtAAxWIjqzvs7d6yW9UVKFpIWS/ioB+wUAABhrhlpv+pKkT0jqHLnQMJoOVzeqsbVDF4/zgZS7u2reJG2vqFNDc1vUoQBASklEcqdr9LebJf3A3WsSsE8AAICxaND1JjN7o6Qz7r6ln+1uN7PNZra5srIyAaFiJL1wvE4ZMdOiMoaqjHflvInq6HRtOsyfFAAwGIlI7vzSzHZLWiXpMTMrldScgP0CAACMNUOpN10l6c1mdljSfZKuM7Pvdt/I3e9291Xuvqq0tDTRcSOBOt2180S9Fk0pUGY6s0LFWzGrWJnpaXpmf3XUoQBAShn23cTdPynpCkmr3L1NUqOkW4a7XwAAgLFmKPUmd/+Uu09399mSbpX0uLszvmEKO1zVqMaWdl1Cl6xXyM6IadWsYq07QHIHAAZjyLNldbNE0mwzi9/fvQnaNwAAwFhCvWmco0tW366aP0n/9vAeVZ9r0cS8rKjDAYCUMOzkjpn9r6R5krZJ6ggXu6ikAAAAvMxw603u/jtJvxuB0DBKOt314ol6LSrLp0tWL66YN1GStP5gjd5w6dSIowGA1JCIljurJC11d0/AvgAAAMYy6k3jXEVNkxpa2nXRNLpk9ebS8kLlZaVr3YEqkjsAMECJeFywQ9KUBOwHAABgrKPeNM69eLJeMTMtmkKXrN6kx9K0dk6JnmXcHQAYsES03Jkk6UUz2yippWuhu785AfsGAAAYS6g3jWMezpI1tzRX2RmxqMNJamvnluix3Wd0pqFZk/Ozow4HAJJeIpI7n0nAPgAAAMaDz0QdAKJzoPKcqhtbddX8SVGHkvTWzAnG3dl06CxdswBgABIxFfrvJR2WlBG+3yRp63D3CwAAMNZQbxrfHnnxtCRpydSCiCNJfhdNK9CEzJg2HqJrFgAMxLCTO2b2YUn3S/rvcFG5pJ8Pd78AAABjDfWm8e2RnadVXpSjwpyMqENJehmxNK2cVayNh89GHQoApIREDKh8p6SrJNVLkrvvkzQ5AfsFAAAYa6g3jVOn65u17Vitlk6j1c5ArZ5dot2n6lXX1BZ1KACQ9BKR3Glx99auD2aWLonpPQEAAF6JetM49fjuM5KkJVNI7gzUmjklcpc2H6mJOhQASHqJSO783sw+LSnHzG6Q9GNJv0zAfgEAAMYa6k3j1OO7z6i8KEdlBVlRh5Iyls0oUmYsTRsPkdwBgP4kIrnzSUmVkl6Q9CeSHpL0twnYLwAAwFhDvWkcam7r0DP7q/SaxaUys6jDSRnZGTFdNqNQG0juAEC/hj0Vurt3mtnPJf3c3SuHHxIAAMDYRL1pfNpwqEZNrR26bvFknapriTqclLJ6donufvKgmlrbNSFz2H+6AMCYNeSWOxb4jJlVSdotaY+ZVZrZ3yUuPAAAgNRHvWl8e2L3GWWlp+mKuZOiDiXlrJlTovZO13NHa6MOBQCS2nC6ZX1cwWwPq919oruXSFor6Soz+4tEBAcAADBGfFzUm8Yld9fju8/oynkTlZMZizqclLNyVrHSTHTNAoB+DCe58z5Jt7n7oa4F7n5Q0nvCdQAAAAhQbxqnDlQ26mhNk65bUhZ1KCkpPztDF00r1MZD1VGHAgBJbTjJnQx3r+q+MOw/njGM/QIAAIw11JvGqSfCKdCvWzw54khS1+rZJXruaK1a2zujDgUAktZwkjutQ1wHAAAw3lBvGqd+v7dS8yfnqbwoJ+pQUtaaOSVqae/UC8drow4FAJLWcIacv8zM6ntYbpKyh7FfAACAsYZ60zh0vrVDGw/X6D1rZ0UdSkpbPbtYUjDuzspZJRFHAwDJacjJHXdnRDgAAIABoN40Pm08XKPW9k5ds5BZsoZjYl6WFkzO08ZDNfrTa6OOBgCS03C6ZQEAAADoxZN7K5WZnqa1cyZGHUrKWz2nRFsOn1VHp0cdCgAkJZI7AAAAwAh4al+l1swuYQr0BFg7p0QNLe3adbKn3o0AAJI7AAAAQIKdqmvW3tPndPUCumQlwpo5wVg7Gw/VRBwJACQnkjsAAABAgj25r1KSdM3C0ogjGRumFuaovChHW46ejToUAEhKJHcAAACABHtqX5Um5WVp8ZT8qEMZM1bMKtbWIyR3AKAnJHcAAACABOrsdD29r1LXLJgkM4s6nDFj5cwinaxr1ona81GHAgBJh+QOAAAAkEA7TtTpbFMbXbISbOWsYNydrXTNAoBXILkDAAAAJNBT+6okSa9iMOWEWjw1XzkZMW2haxYAvALJHQAAACCBntxbqYumFWhSXlbUoYwpGbE0XTq9kHF3AKAHJHcAAACABDnX0q4tR87q6gV0yRoJK2cVa+eJep1v7Yg6FABIKiR3AAAAgARZf6Ba7Z2uaxbSJWskrJxVrPZO1/MVtVGHAgBJheQOAAAAkCBP7qtUTkZMK2cVRx3KmLR8ZnBdtzCoMgC8TCTJHTO70cz2mNl+M/tkD+vNzL4Srn/ezFb0V9bM/incdpuZPWJm00brfAAAAAApGEz58rklykqPRR3KmFSSm6m5pbmMuwMA3Yx6csfMYpK+JukmSUsl3WZmS7ttdpOkBeHrdklfH0DZf3P3S919maQHJf3dCJ8KAAAAcEHF2SYdqmpkvJ0RtnJmsbYcOSt3jzoUAEgaUbTcWSNpv7sfdPdWSfdJuqXbNrdIutcD6yUVmdnUvsq6e31c+VxJ/G8PAACAUbPuQLUk6ar5jLczklbOKtbZpjYdqmqMOhQASBrpERyzXNKxuM8VktYOYJvy/sqa2WclvU9SnaTXJC5kAAAAoG/rD1RrYm6mFpblRR3KmNY1ntGWI2c1t5RrjeH5/oajUYegd62dGXUIGAOiaLljPSzr3sqmt236LOvuf+PuMyR9T9JHezy42e1mttnMNldWVg4wZAAAAKB37q51B6p1+byJMuupyopEmVeap4LsdG1lUGUAuCCK5E6FpBlxn6dLOjHAbQZSVpK+L+kPejq4u9/t7qvcfVVpKf2hAQAAMHyHq5t0qr5ZV86bGHUoY15ammnFrGDcHQBAIIrkziZJC8xsjpllSrpV0gPdtnlA0vvCWbMul1Tn7if7KmtmC+LKv1nS7pE+EQAAAECS1h2okiRdMZfkzmhYObNYe0+fU935tqhDAYCkMOpj7rh7u5l9VNLDkmKSvuXuO83sjnD9XZIeknSzpP2SmiR9sK+y4a4/b2aLJHVKOiLpjlE8LQAAAIxj6w5Ua0pBtuZMyo06lHGha9yd546e1bWLJkccDQBEL4oBleXuDylI4MQvuyvuvUu6c6Blw+U9dsMCAABIZWaWLelJSVkK6m73u/vfRxsV4rm71h+o1jULSxlvZ5RcNqNIaSZtPUJyBwCkiJI7AAAAGLAWSde5+zkzy5D0tJn92t3XRx0YAntPn1N1Y6uuYLydUZObla4lUwu0hUGVAUBSNGPuAAAAYIA8cC78mBG+us80igg9y3g7kVg5q1jbjtaqvaMz6lAAIHIkdwAAAJKcmcXMbJukM5IedfcNPWxzu5ltNrPNlZWVox7jeLbuQLVmlORoRsmEqEMZV1bOKlZja4f2nG6IOhQAiBzJHQAAgCTn7h3uvkzSdElrzOziHra5291Xufuq0tLSUY9xvOrodG04VEOrnQismBkMqryVKdEBgOQOAABAqnD3Wkm/k3RjtJGgy66T9ao736Yr502KOpRxZ3pxjkrzs7SF5A4AkNwBAABIZmZWamZF4fscSa+VtDvSoHDBsweqJYnBlCNgZlo5s1hbj9ZGHQoARI7kDgAAQHKbKukJM3te0iYFY+48GHFMCK07UKW5pbkqK8iOOpRxaeWsYh2tadKZhuaoQwGASDEVOgAAQBJz9+clLY86DrxSW0enNh6q0VtXlEcdyri1YlbXuDu1uvHiKRFHAwDRoeUOAAAAMAQvHK9TY2uHrpjLeDtRubi8QJmxNG09yrg7AMY3kjsAAADAEHSNt3P53JKIIxm/stJjumR6IYMqAxj3SO4AAAAAQ/DsgWotnpKviXlZUYcyrq2cVawXKurU0t4RdSgAEBmSOwAAAMAgtbR3aPORGmbJSgIrZhartaNTO47XRR0KAESG5A4AAAAwSNuO1qq5rVNXzCW5E7UVs4okBYMqA8B4RXIHAAAAGKR1B6qVZtJakjuRm5yfrZklExh3B8C4RnIHAAAAGKRnD1brommFKszJiDoUKBh3Z8vRs3L3qEMBgEiQ3AEAAAAG4Xxrh547elZXMt5O0lgxq1iVDS2qOHs+6lAAIBIkdwAAAIBB2HLkrNo6XJeT3EkaK2cWS5I2H6mJOBIAiAbJHQAAAGAQ1h2oUnqaafXskqhDQWjRlHzlZsYYdwfAuEVyB8PS0tahF47XqbmtI+pQAAAARsWzB6t16fRC5WWlRx0KQrE00/KZxdrCjFkAximSOxiS860d+vm24/rcb3brBxuP6r5NR9XJAHYAAGCMO9fSrucr6nTlvElRh4JuVs4q1p5T9Wpobos6FAAYdSR3MCQP7zylzYdrdPG0Qr16Yan2nj6np/ZVRR0WAADAiNp0qEYdna4rGG8n6aycVaxOl7Yfq4s6FAAYdbQlxaAdq2nS5iM1Wj27RLcsK5e7q7qxVY++eEqzSiZo9qTcqEMEAAAYEesOVCkzlqaVs4qjDgXdLJtZJLNgwOtXLaBlFYDxhZY7GLT/fHyf0sx07aLJkiQz09uWl6swJ0O/2Xkq4ugAAABGzrMHq7V8ZpGyM2JRh4JuCrIztKgsX1uOMqgygPGH5A4G5XBVo36y9bjWzClRYU7GheXZGTGtml2iozVNqjtPP2cAADD21Da1aueJesbbSWIrZhXruSNn1dnJWJAAxheSOxiUr//ugDJiplcvLH3FuounFUqSdp6gnzMAABh7NhyqkbsYbyeJrZxZrIaWdu07cy7qUABgVJHcwYC1dXTq1ztO6uZLpio/O+MV60vzs1RWkKUdx0nuAACAsefZA9XKzkjTshlFUYeCXnSNhbTlCF2zAIwvJHcwYBsO1qi+uV03XjSl120uLi/UkeomnalvHsXIAAAARt66A1VaPbtEmelUoZPVrIkTNDE3k+QOgHGHOxMG7Dc7TyonI6ZreuiS1eXiaYVyiYGVAQDAmFLZ0KK9p8/RJSvJmZlWzCrWVgZVBjDOkNzBgHR2uh7ZeVqvXlja5+wQZQXZKs3P0kMvnBzF6AAAAEbW+oPVkqQr5pLcSXYrZxXrUFWjqs+1RB0KAIwakjsYkOeO1epMQ4tuvLj3LlldLppWoI2HanSupX0UIgMAABh5zx6sVl5Wui4pL4w6FPRjVTjuztajtdEGAgCjiOQOBuSRnaeUnmZ6zeLJ/W47e2KuOl3afqx25AMDAAAYBc8eqNbaOSVKj1F9TnYXlxcqI2aMuwNgXOHuhAF5eOcpXTl/kgpzXjlLVncziidIkrZyQwUAAGPAybrzOlTVyHg7KSI7I6aLywu15UhN1KEAwKghuYN+Hatp0uHqJr1mUe8DKcfLyYxpweQ8BrIDAABjwrMHwvF2SO6kjJUzi7W9ok4t7R1RhwIAo4LkDvq14VDw1GMwFZoVM4v13LFauftIhQUAADAqnj1QraIJGVoypSDqUDBAq+eUqLW9U89X1EUdCgCMCpI76Nf6g9UqnpChhZPzB1xmxawi1Ta16WBV4whGBgAAMPLWHajW5XMmKi3Nog4FA7R6dokkaeMhumYBGB9I7qBf6w9Wa+0gKzTLZ4azFDDuDgAASGHHapp0vPY8XbJSTElupuZPztOmwyR3AIwPJHfQp2M1Tao4e15r55YMqtz80jzlZ6czBSUAAEhp6w5USZKuJLmTclbPLtGWw2fV0ckwAQDGPpI76FPXeDuXzx1chSYtzbRsRpGeY1BlAACQwtYdqNakvCzNn5wXdSgYpLVzStTQ0q7dp+qjDgUARhzJHfRpw8FgAMFFZQMfb6fLipnF2nO6QQ3NbSMQGQAAwMhydz2zv1pXzpsoM8bbSTWr5wQtzzcx7g6AcYDkDvq0/lC11s4pGdIAgitmFctdzFIAAABS0t7T51R1rkVXzadLVioqL8pReVGONh2mJTmAsY/kDnpVcbZJx2rOD7pLVpeLpwXThe46SVNYAACQep7Z3zXezqSII8FQrZ5drA2HauTOuDsAxrb0KA5qZjdK+rKkmKRvuvvnu623cP3NkpokfcDdt/ZV1sz+TdKbJLVKOiDpg+5eOyonlIS+v+Fon+vftXZmv/vYcDBowrp2ztCSOxPzsjQ5P0svktwBAAApaN2BKs0smaAZJROiDgVDtHpOiX6+7YQOVzdpzqTcqMMBgBEz6i13zCwm6WuSbpK0VNJtZra022Y3SVoQvm6X9PUBlH1U0sXufqmkvZI+NcKnMuatP1itwpwMLZ4y+PF2uiyeWqDdJxsSGBUAAMDIa+/o1IaDNbpqPq12Utma2Yy7A2B8iKJb1hpJ+939oLu3SrpP0i3dtrlF0r0eWC+pyMym9lXW3R9x9/aw/HpJ00fjZMayDYdqhjzeTpclU/K1/8w5tXV0JjAyAACAkfX88To1tLQz3k6Kmz85T8UTMrTxMMkdAGNbFMmdcknH4j5XhMsGss1AykrSH0n6dU8HN7PbzWyzmW2urKwcZOjjx/Ha8zpa0zTk8Xa6LJlaoNaOTh2sbExQZAAAACNvXTjezhXDrAshWmamVbNLtInkDoAxLorkTk/NQLqPcNbbNv2WNbO/kdQu6Xs9Hdzd73b3Ve6+qrS0dADhjk8bDlZL0rCTO4unBl26dp9i3B0AAJA6ntlfrSVTCzQxLyvqUDBMa2aX6Eh1k87UN0cdCgCMmCiSOxWSZsR9ni7pxAC36bOsmb1f0hslvdsZEn9YEjHejiTNK81TRswYVBkAgCEysxlm9oSZ7TKznWb251HHNNY1t3Voy9GzumoerXbGgjVzgnF36JoFYCyLIrmzSdICM5tjZpmSbpX0QLdtHpD0PgtcLqnO3U/2VTacReuvJb3Z3ZtG62TGqg2HarRmmOPtSFJGLE3zJ+czqDIAAEPXLun/uvsSSZdLurOHySiQQJsPn1VreyeDKY8RF00r0ITMGIMqAxjTRj25Ew56/FFJD0vaJelH7r7TzO4wszvCzR6SdFDSfknfkPSnfZUNy3xVUr6kR81sm5ndNVrnNNacqD2vI9XDH2+ny5Kp+XTLAgBgiNz9pLtvDd83KKgD9TTmIBLkmQNVSk+zCy0+kNrSY2laMbNYG0juABjD0qM4qLs/pCCBE7/srrj3LunOgZYNl89PcJjj1oZDXePtJKZCs2RKgX669bhqGltVkpuZkH0CADAemdlsScslbehh3e2SbpekmTNnjm5gY8y6/VVaNqNIuVmRVJUxAlbPLtGXHturuvNtKszJiDocAEi4KLplIcmtP1CjwpwMLZlSkJD9LZka7Gc34+4AADBkZpYn6SeSPu7ur7ipMmlEYtSdb9MLx+t0JV2yxpTVc4rlLm05Quud8aaz03WupV1nGpp1pqFZ9c1tau/ojDosIOF4HIFXWH+oWqtnD3+8nS5dM2btOtVARQkAgCEwswwFiZ3vuftPo45nLFt/sFqdLgZTHmOWzyhWRsy04WCNrltcFnU4GAHNbR16vqJOO47Xae/pBu0/c04nas/rdEOLOjpfPtdOmkllBdmaUTxB6THTjJIJmjspV/nZtOpC6iK5g5c5WReMt/Pey2clbJ+T8rI0KS+LljsAAAyBmZmk/5G0y93/I+p4xrpn9lcpJyOm5TOLow4FCZSTGdOyGUV69mB11KEgQTo6XdsravX7PZX6/d5K7Thep/YwiVM8IUMLyvJ1+dyJmlKYrcKcDE3IjElmamnrUP35NlWE44w+d/is1h2olkmaNXGCLplepOUzipSdEYv2BIFBIrmDl9lwMGiqmqjBlLssLMvTvjPnErpPAADGiaskvVfSC2a2LVz26XAcQiTYM/urtHpOiTLTGb1grLli3iR99fF9qm9uUwEtNFJSU2u7Ht99Rg/vPK2n9lWqtqlNZtJl04v04WvmasXMYl02o1CleVkK8uL9+99nj+hUXbN2n6rXzhP1+uX2E3p45ymtmFmsVy8sZYwmpAySO3iZ9QerVZCdfmGcnERZMDlPP9l6XO4+4P9oAQCA5O5PS+LmOQpO1TXrQGWj3rFqRtShYARcMXeivvLYPm08WKPXLqVrVqpobuvQ7/dW6pfbT+ixXWd0vq1Dk/KydP3iMr16Uamunj9JxcOYtCWWZiovzlF5cY6uX1KmirNNevZAtTYdqtHmwzW6Yu5EXbtosnIyacmD5EZyBy+z/mC11syZqFiCxtvpMr8sX+da2nWyrlnTinISum8AAIBEeHJfpSTpmoUMSD0WLZ9ZpMz0NK07UE1yJwXsOF6nH246pp9vO66G5naV5GbqbSvK9abLpmn17JKE/73SZXrxBL191QS9dkmZHtt9Wk/vr9Jzx2r1psum6eJpBTyoRtIiuYMLTtU163B1k96TwPF2uiycnCdJ2nfmHMkdAACQlH6/t1KT87O0eEp+1KFgBGRnxLRyZjHj7iSxhuY2/WLbCd236ah2HK9XVnqabrp4it62YrqunDdR6bHR6y5ZnJupP1w5Q1fMm6SfPVehH2w8qoumFeity8s1IZM/o5F8+K3EBRsOBTe6RI+3I0kLyoJK0r7TDXo1T8MAAECS6eh0Pb2vSjcsLePJ/Bh25byJ+sKje3W2sXVYXXmQWBVnm/TtZw7rh5uO6VxLuxZPydc/vPkivWVZuQonRDvmTXlRjj7y6vl6Zn+VHn3xtL56dr/euXqGZk3MjTQuoDuSO7hg/cFq5Y/AeDuSVJKbqUl5mdp3mkGVAQBA8tleUau68208hBrjrpg3UXo0eKh548VTow5n3Hvu6Fl98+lD+vULJ5VmpjdcOlUfvGqOLptemFRJ1lia6ZqFpZpbmqv7Nh3TN546qDddNk1r5yT+oTgwVCR3cMGzB6q1ZgT7r86fnKe9ZxpGZN8AAADD8fs9lUoz6VXzJ0UdCkbQpdOLlJMR07oDJHeitP1Yrf79kT16al+V8rPT9eFr5ur9V8xO+uEbphdP0EdfM18/3HRMv9h2QqfrW/SGS6aO2N9PwGCQ3IEk6VhNkw5XN+l9V8wesWMsmJyvnz/HjFkAACD5/H5vpS6bUURXnTEuMz1Nq+eU6Jn9VVGHMi7tPlWvLzyyV4++eFrFEzL06ZsX691rZyk3K3X+LM3OiOm9V8zSb3ac0tP7q1Tb1Krb1vz/7d13nBX1vfj/1/uc7b0X2KUvHaSDgIhiQWNNrNhjYtREjfmlmG9ucs1NcpObG5PYvfYSK1EjalSwIEjvSF1gWZal7C7LwvZ6Pr8/ZhaPuA3YPXPK+/l4zOPMmTPlPTNndz7nPZ/PZ/oQ7sP+gJRqi34DFcCxC9wZeT13t2pwZhxVDc2UVDb02DaUUkoppU5URU0jG4qPMCNPm2SFgjMGpbGrrIYDR+ucDiVklFbVc+/r67ngwcUs31XOT84dzOJfnM1tMwYGVGKnlUuEC0dlc/Fpvdh+sIrnluymrrHF6bBUiAu8vyTVI77YeYjMhEgG2U+16gmDMqxOlfNLqshKjOqx7SillFJKnYgvdh7CGDhziCZ3fOWVFUWObbuyvgmAxTsOcdWEXMfi8Ac9fR48xrCioJz5W0po9hhm5KVzRl4aMRFhzFu/nzmT+/To9nva6QNSiY1wM3d1Mc8sKeDWaQOIjnA7HZYKUVpzR+HxGJbuKmfaoLQebS41OPOrx6ErpZRSSvmLz/PLSIwO57ScJKdDUT6QlRBFWlwkX+zQplk9qbiilscX7uLdjQfITY7hnrPzOH9EVtA9Rnx0ThLXT+lDSWUDzy3dTX2T1uBRztDkjmLLgUoO1zT2eAeCqXGRpMRGsKNEO1VWSimllH8wxvB5fhln5KVpp6ghQkSYPiiVJTsP4fEYp8MJOs0tHj7YdIDHF+6isq6Jqyfmcsu0fqTFRzodWo8ZkpXAnEl9OHCknueWaIJHOUOTO4ov7P52fPF0iEEZcVpzRymllFJ+Y+uBKsqqGpihj0APKdPz0imvaWTrwUqnQwkqJZX1PP75LhbvOMSEfince+5gTstJComHqQzLTuCaSbnsO1LH80sLadAEj/IxTe4oluw8xODMODISer4fnMGZceSXVGGM3iVRSimllPMW7SgD4ExN7oSU1oeIaNOs7rOuqILHFu6ksr6ZG6f05fKxvYkKD63+Z0b0SuTqiX0orqjlhWWFNDZ7nA5JhRBN7oS4+qYWVu4+zPRBvinQ5GXEU1XfTGmVPjFLKaWUUs77fHsZQ7PiyfTBTS7lPzITohicGXesBrs6ec0eD/9at4+5a4rJSY7h7rMHMTQ7wemwHDOqdyJXTchlT3ktr64sokWb/ikf0eROiFteUE5Ds4czBvd8kyyAvNZOlUu0aZZSSimlnFXd0MzqPYf1KVkhavqgdFbuPqz9o5yC2oZmnltSyMrCw8zIS+e70/oTHxXudFiOG52TxCVjerG9pIp/rdunrRaUT2hyJ8R9uq2U6HA3pw9I9cn28rweh66UUkop5aRlu8ppajHaJCtEnTkknYZmD8sLyp0OJSAdqmrgsc93sfdwLVdNyGX2yCztlNzL5P6pnD00gzVFFSzYUuJ0OCoEaHInhBlj+GRrKdPz0nzWHjYtLoKkmHDtVFkppZRSjvs8v5SYCDcT+qY4HYpywOT+KUSFu1i4vczpUAJOcUUtTyzaRUNTC9+b3p8xuUlOh+SXZg3NYGK/FBbml7F0lzYBVD1LkzshLL+kmn1H6pg1NMNn2xQRBmfE6+PQlVJKKeUoYwyfbStj6sA0IsK0SByKosLdTBuYxqfbSrXZzAnYWVrN04t3Exnm4gdnDqRPaqzTIfktEeGS03oxLDuB9zceYGPxEadDUkFMr2Qh7OOtVvXAs3yY3AEYlGk9Dl0vokoppZRyyraDVew7Usc5w3xbDlL+ZebQDIoO11JwqMbpUAJCfkkVLy4rJCU2gh+cOZC0uEinQ/J7bpdwzcRc+qTEMHdNMbvKtAWD6hlhTgegnPPptlJG9U70+dMhBmfE8UpdE2VVDT55/LpSSiml1PE+sW9yne3jm1zKv8y0+1v6bFspA9PjHI7Gv207WMnLK4rIiI/k1mn9iYk89Z+Sr6wo6obI/F+428UNp/flyUUF/GP5Hm6bMYDsxGinw1JBRmvuhKjDNY2sLapglgN3q/IyrU6Vtd8dpZRSSjllwdZSTstJ1BtNIS43JYa8jDjtd6cTBWXVvLKiiKyEKG6d3j2JnVATExHGzVP7ERXu5vklhRyuaXQ6JBVkNLkToj7bVooxMGtops+33fo4dH1illJKKaWcUFpVz4a9R5g1zPflIOV/zhqawYrd5dQ0NDsdil/ad6SOl5bvITk2glum9iMmQhM7JyspJoKbp/aj2WN4bsluqvU7p7qRJndC1AebDtArMYoRvRJ8vu30uEgSo/WJWUoppZRyxmfbSgE4R5M7Cpg5JJ2mFsMXO/VpRsc7VN3A80t2Ex3u5rvd1BQr1GUmRHHj6X2prG/ihaWFNDS1OB2SChKa3AlB9U0tLMo/xAWjsnG5xOfbFxHyMuLYWaLJHaWUUkr53sdbS+mVGMWw7HinQ1F+YGK/FBKiwliwpcTpUPxKZV0Tzy3ZjQFumdafxOhwp0MKGn1TY7lmYh8OHK3j5ZVFNDZ7nA5JBQFN7oSgrQcqaWzxcOGoLMdiyMuMJ7+0Sp+YpZRSSimfqm9q4Ysdh5g1LBMR39/kUv4n3O1i1rBMPtlaQnOL/sgGqGts4bmlu6lpbOHmqf1Ij9enYnW3YdkJXD62NztLq/np3A14PPq7SJ0aTe6EoE37K8lKiGJsbrJjMeRlxHGktomy6gbHYlBKKaVU6FmUX0ZdUwvnjdAmWeor5w3PpKK2iVWFFU6H4rgWj+HllXs4VN3I9ZP7kpMc43RIQWt83xTOG57JvA37+f37W/XGtzolmtwJMfVNLewoqWL2yCxHmmS1GpJlVYPOP6hNs5RSSinlOx9uPkhCVBhTBqQ6HYryIzMGpxMR5mL+loNOh+K497/cT0FZDZeP6c2gDH08fE87c3A6N0/tx7NLdvN/iwqcDkcFME3uhJhtB6to9hi+NTrb0ThakzvbDlY6GodSSimlQkdTi4ePt5RwzvBMwt1aDFZfiY0M44xBaczfXBLStSdW7C5necFhpg9KY1xf52r5hxIR4TcXDeei0dn86YNtzF291+mQVIDSq1qI2bTvKPFRYYzv4+w/67S4SNLiItl2UB+HrpRSSinfWFFwmMr6ZmaPcK7fQeW/zh+Rxb4jdWw5EJo3HwsOVfPuhv0Mzoxj9kj9G/Ell0t44KrTmD4ojV+8uZF3N+x3OiQVgPRZdiGktrGZ7SVVTOmf4miTrFZDs+LZrskdpZRSSvnIh5sPEB3uZsbgdKdDUX5o1rAMXAIfbS5hRK9Ep8PxqcM1jbyyoojU2EiumdgHl3Y27lOvrCgCrO/g3opa7nltHct2lTOyt2++h3Mm9/HJdlTP0po7IWTTvkpaPIYxDnak7G1oVjz5JVW0aM/wSimllOphHo/ho80lnDU0nahwt9PhKD+UGhfJxH4pvL9xf0g1zWps9vCP5XswBm44va/+fTgoMszNzaf3Iyc5htdWFbFlf2jWIlMnR5M7IWT93iOkxUXSKynK6VAAq9+dhmYPheU1ToeilFJKqSC3tqiCsqoGztcmWaoDF5/Wi11lNSHTdYAxhnfW76Oksp5rJuWSFqePPHdaZLibm6f2o1dSNK+uLGJbiDYTVCdOkzshoqK2kcLyGsbkJiF+Us1yWHYCgDbNUkoppVSPe2/jASLDXJw9NMPpUJQfu2BkFm6XhEyfJ6sLK1i39whnD8sgLyPe6XCULSrczS1T+5OVGMXLmuBRXaTJnRCxce8RAMbkJjkah7dBGXG4BP1npZRSSqke1eIxvP/lAc4akkF8VLjT4Sg/lhoXybRBabwbAk2zNu8/yrsb95OXEcdZQzTp6W+iI9zcMq0fWQlR/GPFHtYVVTgdkvJzmtwJAcYY1u09Qp+UGFJiI5wO55iocDf902JDptqrUkoppZyxYnc5ZVUNXHxaL6dDUQHg4tHZ7D1cx4bio06H0mMq65u48+W1xES4uXJCrnag7KdiIsK4dXp/+qXGMndNMUt2HnI6JOXH9GlZIWD/0XpKqxq4xKtA09oje3t81WP60KwENu0P3gunUkoppZz37oYDxES4tUmW6pLzRmTxq7c38e6G/X5V6727GGP4+dyNFFfU8b3p/YmL1J+E/iwq3M1NU/vxxuq9vP/lAaobmjlveKbfdLWh/IcjNXdEZLaIbBeRnSJyXxufi4g8ZH++UUTGdbasiFwpIptFxCMiE3y1L4FgzZ4KwlzCaTlJTofyDUOz4tlTXktNQ7PToSillFIqCDW1ePhw0wHOGZZJdIQ+BUh1LjE6nDOHpPPexv1B+VTXZ5cU8uHmg9w3eyh9U2OdDkd1QbjbxbWT+jCxXwqf55fx9rp9QfndVKfG58kdEXEDjwIXAMOBa0Vk+HGzXQDk2cNtwONdWHYT8G1gUU/vQyBpbvGwYe8RhmUn+GWBZkiW1XFbfok2zVJKKaVU91uy8xAVtU3aJEudkG+P7U1JZQOLd5Q5HUq3WltUwR//vZVzh2fyvTP6Ox2OOgEuES4b04uZQ9JZvaeC55buplZvkCsvTtTcmQTsNMYUGGMagdeAS4+b51LgRWNZDiSJSHZHyxpjthpjtvtuNwLDtoNV1DW1ML5vstOhtGlolvXErK0HNLmjlFJKqe43b/1+4qPCmDE4zelQVACZNSyTlNgI5q4udjqUblNR08iPXl5LdlIUf7nyNG3WE4BEhPOGZ3HFuByKymt5dOFODh6tdzos5SecSO70BvZ6vS+2p3Vlnq4s2yERuU1EVovI6rKy4MrEt2VtUQUJUWEMyohzOpQ25aZEEx8Vxmbtd0cppZRqk4g8KyKlIrLJ6VgCTXVDMx9sOshFo7OJDPO/GszKf0WEubhsTG/mbznI4ZpGp8M5ZR6P4d431nOoupHH5ownMVqfGhfIxvVN5vtnDKDZY3ji811s2qe/pZQzyZ22UsTHNxhsb56uLNshY8yTxpgJxpgJ6enpJ7JowKmqbyK/pIqxfZL9tgd8EWFU70S+1H9ISimlVHueB2Y7HUQg+uDLA9Q1tfCdcTlOh6IC0FUTc2hqMbyzfp/ToZyyJxbtYuH2Mn598XBG5SQ6HY7qBrkpMfxw5iAyEyJ5ZWUR8zcf1H54QpwTyZ1iINfrfQ6wv4vzdGVZZVu/9wgeA2P7JDkdSodG5SSy9UAlDc0tToeilFJK+R1jzCLgsNNxBKK31u6jb2qM3zZPV/5taFYCo3on8vqqvRgTuD+aV+4+zAPz87lodDbX++iJuMo3EqLD+d4ZA5jQN5mF+WU8tbiAiiCoaaZOjhPJnVVAnoj0F5EI4Bpg3nHzzANutJ+aNQU4aow50MVlFdYjDtfsqSA3OZqM+Cinw+nQ6N5JNLUY8g9WOx2KUkopFbBCrel5Z4orallWUM63x+Zo3yLqpF01IYdtB6vYWByYtczLqxu469W19EmJ4Y/fHqV/C0Eo3O3i2+NyuHpiLiWV9Tz82Q42Fh9xOizlAJ8nd4wxzcCPgI+ArcAbxpjNInK7iNxuz/ZvoADYCTwF3NnRsgAicrmIFAOnA++LyEc+3C2/s+9IHaVVDYwLgDtVo+2qoRv3HXE2EKWUUiqAhVLT8654e63VlObb406oe0alvubSsb2JjXDzwtJCp0M5YVY/OxuoqG3ikTljiY/SfnaC2Wk5Sdx1dh7pcZG8tmovb64tpqFJW0aEkjAnNmqM+TdWAsd72hNe4wb4YVeXtae/DbzdvZEGrrVFFYS5hNG9k5wOpVM5ydEkxYTzZfFRmOx0NEoppZQKdB6P4c21xUzun0JuSozT4agAlhAVzhXjc3hlZRH3XTjU72vEe3v8810syi/jD5ePZEQv7WcnFKTERnDbjIF8sq2Ez7eXsau0msvH9iYvM97p0JQPONEsS/WwhuYWNuw9yvBeCURH+P+TIVo7VQ7U6q5KKaWU8i/LCsopLK/l6om5nc+sVCdumtqPphbDqyv2dj6zn1hRUM4D87dz8Wm9mDNJ+9kJJW6X9bj022YMIMzt4rmlhby5tpi6Rq3FE+w0uROEPtlaSl1TC+P7+H+TrFajcxLJL6miXqsOKqWUUl8jIq8Cy4AhIlIsIrc6HZO/e2VFEUkx4Vw4KtvpUFQQGJAex8wh6fxjxR4amz1Oh9OpQ9UN3PXqOvqmxmo/OyGsb2osd509iDMHp7OuqIK/f5LP1gOVToelepAmd4LQ3NV7SYwOZ2BGnNOhdNmo3kk0ewzbDlY5HYpSSinlV4wx1xpjso0x4caYHGPMM07H5M/Kqhr4aPNBvjMuh6hw/6/BrALDLdP6U1bVwPtf+veDej0ew72vr+doXROPzhlHXKQjvXAoPxHudnH+iCzuOHMQsRFhvLR8D6+vKqKmodnp0FQP0OROkCmtrOfz/DLG5ibhCqAs/Si7U+UvtWd3pZRSSp2CN1bvpdljuFaboqhudMagNAZnxvHYZ7vwePz3seiPfraTxTsOcf8lIxjeK8HpcJSf6J0czZ1nDWTWsAw27avk7x/ns6H4CFZXtypYaHInyLy9bh8eA+MCqEkWQK/EKFJjI9ig/e4opZRS6iR5PIbXVhUxZUAKgwKoBrPyfy6XcNfZeeworebfmw44HU6bPt1Wwl8/zueyMb24RvubUscJc7mYNTSTH541iOTYCF5ftZeXlu/haF2T06GpbqLJnSBijOGfa4oZ3zeZtPhIp8M5ISLCmNwk1hZVOB2KUkoppQLUwvxS9h6uY87kvk6HooLQhaOyGZQRx0Of7PC72ju7yqq559X1DM9O4I/fHq397Kh2ZSVGcfuZA7lwZBa7yqr5+8f5vLR8j999p9WJ0+ROENlYfJQdpdVcMT7H6VBOyqT+KRSU1VBW1eB0KEoppZQKQE8v3k1WQhQXjMxyOhQVhNwu4a6zB5FfUs2Hmw86Hc4xlfVNfP/F1USEuXjyxgkB8bRc5SyXCNPz0rln1mByk2P49b82cfWTy9hZWu10aOoUaHIniMxds5eocBffGh2YT4aY1D8FgJW7DzsciVJKKaUCzZb9lSzdVc5NU/sR7tYiruoZF43uxcD0WP62IJ/mFuefnNXiMfz4tfUUldfy2HXj6J0U7XRIKoCkxEZwy7R+/O8Vo8kvqebCBxfzyKc7aPKD77Y6cXrlCxL1TS3MW7+f80dkkRAV7nQ4J2Vk70RiItys3F3udChKKaWUCjDPLtlNdLibOdqRsupBbpfw89lD2VFazT+W73E6HB6Yv51Pt5Xyn5eMYPKAVKfDUQFIRLhyQi4f/+RMzh2eyV/m53Pxw1+wUR90E3A0uRMkPth0gMr6Zq4O4M7Twt0uxvdNZoXW3FFKKaXUCSitqmfe+v1cOSGHxJjAvMmlAsd5wzOZPiiNvy7I53BNo2NxvLN+H48t3MW1k3K5frImNdWpSY+P5NHrxvHkDeOpqG3kskeX8L8fbaOxWWvxBApN7gSJV1fupV9qDKcHeMZ+Ur8UtpdUcaTWuQulUkoppQLL80sKafJ4uGVaf6dDUSFARPjPi4dT09jCA/O3OxLDkp2H+OncDUzun8JvLxmpHSirbnPeiCwW/ORMrhyfy6Of7eLyx5aQX1LldFiqCzS5EwR2lVWzcvdhrp7YJ+D/sU/qn4IxsKpQn5qllFJKqc4dqW3kxWV7uHBUNv3TYp0OR4WIvMx4bpjSl1dXFrG60Le1zrceqOT2l9YwIC2OJ2+cQESY/qRT3SshKpz/uWI0T904gYNH67no4S94enGBPlHLz+l/giDw+qq9hLmE74zv7XQop+y03CQiwlysKNB+d5RSSinVueeWFFLd0MxdZw9yOhQVYn56/hB6J0fzkzc2UN3Q7JNt7iyt5oZnVhAbGcZzt0wkMVqbIaqec+7wTD66dwYz8tL5/ftbue7pFew7Uud0WKodmtwJcI3NHt5cU8ysYRlkxEc5Hc4piwp3MyY3iZU+vgOilFJKqcBTWd/Es0t2c/6ITIZmJTgdjgoxcZFh/O2qMRRX1PJf727u8e0VHqphzlPLAeHl70+mlz4ZS/lAWlwkT904nj9/ZzQbi48w+2+LeGttMcZoLR5/o8mdAPfx1hLKaxq5JoieDDGlfwqb9h3laG2T06EopZRSyo+9sKSQqvpm7jo7z+lQVIia0C+FO2YO5I3Vxbyzfl+PbWdnaRXXPrWcphYPL39vMgPT43psW0odT0S4amIuH9wzg6HZ8fzkjQ3c+/p6n9VYU10T5nQA6tS8urKIXolRzMhL79b1vrKiqFvXdyJmDs3goU938tn2Ui4bG/hNzZRSSinV/SpqGnlycQHnDMtgZO9Ep8NRIeyeWYNZtbuCn83dSFZCVLc/knxdUQW3PL+KMJeLl783hSFZ8d26fqW6qk9qDK/ddjqPfbaTv32cz4biozwyZywjeun/YH+gNXcC2N7DtXyx8xBXTsjF7QrsjpS9jclJIj0+kgVbSpwORSmllFJ+6uFPd1LT0MzPZw91OhQV4iLCXDx543hyUqK57aU17CztvicL/fvLA8x5agUJUeG8ecfpDO+lzQ+Vs9wu4a5Zebz6/SnUNjZz+aNLeXFZoTbT8gOa3Algc1fvBeCqibkOR9K9XC7hnGEZLNxeSkNzi9PhKKWUUsrPFJXX8tLyQq6akMvgTK3FoJyXFBPBC7dMItzt4ponl7Nh75FTWl9zi4c//nsrd768liFZ8fzzjtPpm6pPg1P+Y/KAVD64ZwbTBqXym3c2c8c/1nK0TrvVcJI2ywpQzS0e3lhdzIy8dHr7YWdqnTXrmjO54z6Czh2eyasr97K84DBnDu7eJmdKKaWUCmz/O387YS4X95472OlQlDomNyWG126bwi3Pr+TqJ5fx4DVjOX9E1gmvZ/P+o/y/t75kQ/FRbpjSl/+4aBiRYe4eiFipU5MSG8EzN03kmS928z8fbuPCBxfz8JyxjOuT7HRoIUlr7gSoz/PLOFhZz7WTgqvWTqupA9OIDnezYMtBp0NRSimllB9ZUVDOuxv28/0z+pOZEPhPClXBZVBGHG/dMY0hmfH84KU13P3qOkor67u07IGjddw/bzOXPLKEfUfqeGTOWH532UhN7Ci/5nIJ358xgLm3n44IXPXEMp5ctAuPR5tp+ZrW3AlQzy8tJCM+klnDMp0OpUdEhbuZMTiNj7eU8rtLDSLB06eQUkoppU5OY7OH//jXJnKSo7lj5iCnw1GqTenxkbz+g9N5fOEuHl+4i0+3lXLJmF58e2xvxvZJ/lpfmVX1TSzdVc5Hmw7y7sb9eAxcNSGH+2YPIzEm3MG9UOrEjO2TzPt3n8F9b27kv/+9jeUFh3ngytNIjo1wOrSQocmdAJRfUsXiHYf46XmDCXcHb+Wrc4dn8dHmEjYWH+W03CSnw1FKKaWUw57+ooAdpdU8e/MEoiO0NoPyX1Hhbu49dzCXje3NQ5/s4O21+3hlRRHhbqF3UjTREWEcrmngUHUjLR5DXGQYcyb14XtnDCA3Jcbp8JU6KYnR4Tx23TheXLaHP7y/lQsfWszD145lQr8Up0MLCZrcCUDPLSkkMszFtZM67rcm0J07LJPIMBdz1+zV5I5SSikV4orKa3nokx3MHpHF2UODs+ayCj7902L529Vj+N1lzXyytYRtB6soKq+lvqmFkb0SyE6MYuqgNMb3TQ7qm7YqdIgIN03tx7g+yfzo1bVc/eRy/r/zBnP7jIG4gugJz/5IkzsBpqKmkbfWFnP52N6kxkU6HU6PSowJ51ujs3l77T7uu2AYcZH6dVVKKaVCUYvH8JM31hPucvGflwx3OhylTlhcZBiXjunNpU4HopSPjMpJ5N27pvPLt77kzx9uZ0XBYf561WlB/xvWSZoeDjCvrCyiodnDd6f3dzoUn7hucl9qGluYt36/06EopZRSyiFPfL6L1Xsq+N1lI8lO9L+nhCqllPqmhKhwHrl2LL+/bCTLCsq58KHFrCgodzqsoKXJnQBS19jCc0t2c0ZeGoMz450OxyfG9UliaFY8L6/YgzHa47pSSikVar4sPsrfFuRz0ehsLh3Ty+lwlFJKnQAR4fopfXn7zqnERIRx7VPLefiTHbTo07S6nSZ3AsjLK/ZwqLqRu87OczoUnxERrpvSl837K9lQfNTpcJRSSinlQxU1jdz5yhrS4yP5w2Wj9OmZSikVoEb0spppXTS6Fw8syOfaJ5ez93Ct02EFFU3uBIi6xhae+LyAqQNTmdQ/tHobv2xML2Ii3Dy9uMDpUJRSSinlI80tHu5+bR0lRxt47Lpx+lhopZQKcHGRYTx4zRgeuPI0thyo5IIHFzN39V5todFNNLkTIF5ZWcSh6gbumRU6tXZaxUeF891p/Xlv4wHW7z3idDhKKaWU8oE/f7SdxTsO8bvLRjC2T7LT4SillOoGIsJ3xufwwT1nMDw7gZ/9cyN3/GMth2sanQ4t4OnjhwJATUMzT3y+i6kDU5k8INXpcLrFKyuK2v1szuRvPuL99pkDeW1VEf/9/lZe/8EUrZatlFJKBbFnvtjNk4sKuGFKX66e+M1ygVJKqcCWmxLDq7dN4anFBTwwfzvn/e0wv7l4BBePztbfeidJa+4EgIc/3UlZVQM/PX+I06E4Ji4yjB+fM5iVhYeZv6XE6XCUUkop1UPeXFPM797bwgUjs7j/khFOh6OUUqqHuF3C7WcOZN6PptM7KZq7X13HLc+vorhC++I5GZrc8XO7yqp55osCrhifw7gQr5J8zcRcBqbH8vv3t1BZ3+R0OEoppZTqZm+vK+bnb25k+qA0/n7NGNwuvXurlFLBblh2Am/dOY1fXzSclbsPc+5fF/H04gKaWzxOhxZQNLnjx4wx3D9vM1Hhbn4xe6jT4TguzO3iT98ZzYEj9fzk9Q149PF5SimlVNB4bslu7n19A5P7p/B/N4wnMsztdEhKKaV8xO0Sbp3en/n3zmDKgBR+//5WLnxoMQu3lzodWsDQ5I4fm7dhP4t3HOIn5w4mPT7S6XD8wsR+KfzqW8P4eGsJjy3c6XQ4SimllDpFzS0e/vjvrfz23S2cNzyTZ2+eSGykdguplFKhKCc5hmdvnsgT14+jodnDzc+t4oZnVrDtYKXTofk9vXL6qd2Havh/b33J+L7J3DClr9Ph+JWbp/Zj/d4jPLAgn8SYCD0+SimlVIA6XNPIXa+uZcnOcq6f0of7Lx5BmFvvPSqlVCgTEWaPzObsoZm8tHwPD32ygwsfXMwV43O4c+Yg+qXFOh2iX9Lkjh+qb2rhR6+sJTzMxcPXjtVCznFEhD99ezRV9c38+l+b2H+kjp+dNwSXtstXSimlAsZn20q5762NVNQ28efvjOaqiblOh6SUUsqPRIS5uHV6f74zrjcPf7qTl5bv4Z9rirlwVDZ3zBzIiF6JTofoVzRr4Gc8HsOv3t7E5v2V/OWK0+iVFO10SH4pOsLNkzeM59pJfXh84S6ue3oF+SVVToellFJKqU6UVTXw07kbuOX5VSRFR/DWHVM1saOUUqpdSTER/Pqi4Xzxi7O4bcZAFm4v41sPfcHNz61kUX6Z9sVq05o7fsQYw6/+tYk31xZz7zmDOWd4ptMh+bUwt4v/vnwkI3sn8OcPt3Phg4u5emIuN5zel6FZCU6Hp5RSSikvtY3NPLekkMc+20lDs4cfnjWQu2flacfJSimluiQjPor7LhjKHTMH8tKyQp5bUsiNz66kd1I0V4zP4coJOeQkxzgdpmM0ueMnmlo83D9vM6+uLOLOmQO5e9Ygp0NyzCsrijr8fM7kPsfGRYTrJvflgpHZ/GX+duauKeblFUWMyU3i7KEZnJGXxvBeCVpwVEoppRxyuKaRF5cV8sLSQipqmzh3eCa/vGAoA9LjnA5NKaVUAEqMDudHZ+fx/RkDWLClhNdX7eWhT3fw0Kc7mDowldkjspg1LDPkWsFocscP7D1cy92vrWNd0RFuP3MgPzt/CCLaf8yJSImN4L8vH8XPzhvCr97+kg3FR/nbgnz+uiAfl0BqXCSZCVFkJURy+dgcspOi6JUYTXp8JG4f9tVzIokrpZRSKlA1NLewdGc5c9fsZcGWEppaDOcMy+COmQMZ3zfF6fCUUkoFgcgwNxeN7sVFo3tRXFHLP9cU8876/fz6nc38+p3NjOiVwDnDMpkxOJ1RvROJCAvuXmk0ueOgmoZmXlhWyOMLd4GBR+eM41ujs50Oy+91liCZnpfO9Lx0ahqaKThUw8GjdRysbGD/kTo27zvKx1tLj83rdgmZ8ZGkxkWSFBNOUkwEyTHhJEVb40kx4USFu4lwu4gIs4Ywl9DsMbR4DE0tHlo85mvvG5qtodEeGppbjo1/ue8oxzcJ9U4tbd5/1JomEO52EeF2EW4PYW5h076juF2C2yWEuVxEhrmIjnATFe4mOtzNjVP7Eh8ZFpDJwY7Oqya9lFLK/+09XMuyXeUs2lHGwu1lVDc0kxIbwY2n9+OaibnkZcY7HaJSSqkglZMcw4/PGcyPzxnMztJqPt5awsdbSnjo0x08+MkOIsNcnJaTxIR+yUzol8yw7ASyEqIC8ndTexxJ7ojIbOBBwA08bYz503Gfi/35hUAtcLMxZm1Hy4pICvA60A8oBK4yxlT4Yn9OhDGGzfsref/LA7yxai/lNY2cNSSd+y8ZQd9UfaRbd4qNDGNU70RG9f6qF/XGZg/lNQ0crWs6NlTWNVHT0ELhoRpqGytpMYajdU2YbuyXK8wlxzLFLpFjCR3vTRig4FA1AB5jNdWzBitx1BV/mb+dCLeL+KgwEqLDSTj2Gs63RmeTlRhFdmIU6XGRPn0KW3OLh8M1jZRWNVBW3UBZlddQ3UBlXRNFh2ut/W02NHk8uKQ1iSU8tnAn4W4XsRFuYiLDrNeIMOIiw4iLCuPaSbmkx0WREB2YiS2llOpMZ2UnX6qqb6LwUC0Fh6opKKthy4FKNhYfoaSyAYC0uEguGp3N+SOymDooVZtGK6WU8qlBGXEMyojj9jMHUl7dwKrCw6wqrGD1ngqeXFTAYwut31YJUWEMyYpncGY8A9LjyEmOpndSNDnJ0SRGhwfc7wqfJ3dExA08CpwLFAOrRGSeMWaL12wXAHn2MBl4HJjcybL3AZ8YY/4kIvfZ73/hq/3y5vEYKuubOFLbRHlNA8UVdRSV17Jp/1HW77UKP26XcObgdH541iDG9012IsyQFBHmIjsxmuzE9ttfzpnc52vnsMGr9s0Hmw7i8RhcLrGSD8KxcZdLuHRMLyLcLiLDXUS63cdq+7Q2/TrZZlkej5XweGV5kVVTyBhaWgz1zS3UNbVQ3+ihvqmFmsZmKuuaqKxvPpYwqaxvpsVjeP/LA8fW53YJGfGRx5I9qbGRVo2lmAiSY8NJio4gKtxt7UeYi8gwN26X0OIxGGNoMVbCqbaxheqGZqrrm6lpaOZoXRNlVQ0cqm7gUHXjsfHDtY1tJsvio8JIj4skMSYct0uIDg+3aim5BI+9jRaPtb2GJg8llQ3UNtZQ29jytcTYM1/sts6v20V6fCRp8ZGkx0WQHh9Jelwk6fGRJMdGEBsZRmxEGLGRbvvVShBFhrlw+bB5ngpuHo+hscVjDV61+Frft/5PaWj20NBkjzd5TWv20NBkjdfbr43Nns43LBDuchEeJt+o+RceJl9/77YSzq3zHRsPc32tpmLrfJFu97F1uF0ScIWdQNfFslOPe3fDfv7rvS2UVTV4xQb9U2OZOjCNMblJTB2YyqCMOP2OKKWU8gupcZHMHpnN7JFWC5naxma+LD5KfkkV20uqyD9Yzbsb9lNZ3/y15WIj3KTFR5IcE0FKrNWiIyUmguTYCJJjIo79noixbz7HR4Ux0OG+5JyouTMJ2GmMKQAQkdeASwHvAsqlwIvGGAMsF5EkEcnGqpXT3rKXAjPt5V8AFuJQcuee19fz7ob935jeLzWGKQNSmTLA6uQpOTbCgehUZzpKwHT2B7ui4HCPbRsgMvzE734aYyVhpg1K42BlHQeO1nPwaP2x1+0HqyivKe+22kpR4XaCJS6SvqkxTOiXTFpcJLsP1ZAQFUZcVDjxdo2b8JOsPeQxhvqmFqrrm6lqaOa03KRv1Abad6Se9XuPUl7T0KX9ErFqWLU2eXOJ9US21tpDrg5+qLT3UUe/bYS2P+zq76G29slgOpynK8fBHDdTW4t8Y71tzPXNeboSS8fxt72ek4i3jRV/Y0on8be37dbadqdKgDC39V20Xjv/YhisxFJrQrQ1OdrdTwcV4ViiSOz3Lpf1jXaJIGJ1du8S63vust+LWJ9fMCqLX14wrHuDCn5dKTv1uF5JUcwcnM6A9Dj6p8UyID2WPikxRJ3EtUkppZRyQkxEGJMHpDJ5QOqxacYYKmqb2FdRx74jtRRX1LHvSB2Haxrt1gfWb6aK2kZqG1vaXG+vxCiW/nKWr3ajTU4kd3oDe73eF2PVzulsnt6dLJtpjDkAYIw5ICIZbW1cRG4DbrPfVovI9pPZiZOxB/gceMg3m0sDDvlmU6oTIXcufPZHdeJC7lz4MT0X/sOn52Ix8P96bvV9e27VjupK2amzMo7+zZ08PXanxi+P33VOB9A1fnnsAogevy5q4+9Bj90J2gPIVwWc7j5+XSrfOJHcaev24/H3FdubpyvLdsgY8yTw5IksE4hEZLUxZoLTcSg9F/5Ez4X/0HPhP/RcBIQulX86KuPoeT55euxOjR6/k6fH7tTo8Tt5euxOjVPHz4lngRUDuV7vc4Dj2zC1N09Hy5bYTbewX0tRSimllAp8XSk7KaWUUiqEOZHcWQXkiUh/EYkArgHmHTfPPOBGsUwBjtpNrjpadh5wkz1+E/BOT++IUkoppZQPdKXspJRSSqkQ5vNmWcaYZhH5EfAR1uM8nzXGbBaR2+3PnwD+jfUY9J1Yj0K/paNl7VX/CXhDRG4FioArfbhb/ijom54FED0X/kPPhf/Qc+E/9Fz4uU7KP12l5/nk6bE7NXr8Tp4eu1Ojx+/k6bE7NY4cP2nrqSFKKaWUUkoppZRSKjA40SxLKaWUUkoppZRSSnUTTe4opZRSSimllFJKBTBN7gQhEZktIttFZKeI3Od0PMFIRJ4VkVIR2eQ1LUVEFojIDvs12euzX9rnY7uInO81fbyIfGl/9pCItPW4W9UOEckVkc9EZKuIbBaRe+zpei58TESiRGSliGywz8Vv7el6LhwiIm4RWSci79nv9VwEERG50v5b84jIhOM+O6HzKSKRIvK6PX2FiPTzWuYm+zuzQ0RuIsRomcrS0+Wejr6Dgc4XZZUgP349Xr4I5uMHPVseCIFjV2jv93oRWW1P89/jZ4zRIYgGrI4WdwEDgAhgAzDc6biCbQBmAOOATV7T/gzcZ4/fB/yPPT7cPg+RQH/7/Ljtz1YCpwMCfABc4PS+BdIAZAPj7PF4IN8+3noufH8uBIizx8OBFcAUPReOnpOfAK8A79nv9VwE0QAMA4YAC4EJXtNP+HwCdwJP2OPXAK/b4ylAgf2abI8nO73vPjzGWqb66lj0aLmnve9gMAz4oKwS5Mevx8sXwXz87H3qsfJACBy7QiDtuGl+e/y05k7wmQTsNMYUGGMagdeASx2OKegYYxYBh4+bfCnwgj3+AnCZ1/TXjDENxpjdWE+BmyQi2UCCMWaZsf6iX/RaRnWBMeaAMWatPV4FbAV6o+fC54yl2n4bbg8GPReOEJEc4FvA016T9VwEEWPMVmPM9jY+Opnz6f3d+Ccwy76reD6wwBhz2BhTASwAZvfcXvkdLVPZfFDuae87GPB8VFYJ5uPni/JF0B4/H5QHgvbYdcBvj58md4JPb2Cv1/tie5rqeZnGmANgXciBDHt6e+ektz1+/HR1EuxqjGOx7ujouXCAXe13PVCK9YNQz4Vz/g78HPB4TdNzERpO5nweW8YY0wwcBVI7WFeoCPX970x3/k9p7zsYVHqwrBLUx88H5YtgPn5/p2fLA8F87MBKJM4XkTUicps9zW+PX9jJLqj8VluZPn3evbPaOyd6rrqJiMQBbwI/NsZUdpDw1nPRg4wxLcAYEUkC3haRkR3Mrueih4jIRUCpMWaNiMzsyiJtTNNz4QdE5GMgq42PfmWMeae9xdqY1tn51O9A20J9/09Wd34Hg0YPl1WC+vj5oHwRlMfPR+WBoDx2XqYZY/aLSAawQES2dTCv48dPa+4En2Ig1+t9DrDfoVhCTYld7Q77tdSe3t45KbbHj5+uToCIhGMVll42xrxlT9Zz4SBjzBGsfkBmo+fCCdOAS0SkEKsZydki8g/0XAQcY8w5xpiRbQztJXbg5M7nsWVEJAxIxGqCE+plilDf/8505/+U9r6DQcEHZZWgPn6terB8EazHzxflgWA9dgAYY/bbr6XA21jNdf32+GlyJ/isAvJEpL+IRGB1zDTP4ZhCxTzgJnv8JuAdr+nX2L2h9wfygJV2Nb4qEZlit6280WsZ1QX2cXsG2GqM+avXR3oufExE0u07aohINHAOsA09Fz5njPmlMSbHGNMP6xrwqTHmevRchIqTOZ/e340rsL4zBvgIOE9Eku2ngZxnTwsVWqbqWHf+T2nvOxjwfFRWCebj54vyRVAePx+VB4Ly2AGISKyIxLeOY10DN+HPx8/4QS/UOnTvAFyI1RP/Lqyq247HFGwD8CpwAGjCyrjeitU+8hNgh/2a4jX/r+zzsR2vp80AE+x/EruARwBxet8CaQCmY1Vd3Aist4cL9Vw4ci5GA+vsc7EJ+I09Xc+Fs+dlJl89HUPPRRANwOX29acBKAE+OtnzCUQBc7E6f1wJDPBa5rv29J3ALU7vtwPHWctUpufLPR19BwN9wAdllSA/fj1evgjm4+e17zPpgfJAMB87rCclbrCHza3XAH8+fq0rVUoppZRSSimllFIBSJtlKaWUUkoppZRSSgUwTe4opZRSSimllFJKBTBN7iillFJKKaWUUkoFME3uKKWUUkoppZRSSgUwTe4opZRSSimllFJKBTBN7iillFJKKaWUUkoFME3uKBXgRKRQRNJOYrmZIjK1g88vEZH7OlnHUvu1n4jMOdEY/M3x+yEiE0TkIYdj+rGIxJzC8l87zyJyu4jc2Mky94vIT092m0oppUKXiFwuIkZEhjodi6+JyGUiMtzpOE6ViIwRkQu93ndaJvQHIjJFRFaIyHoR2Soi99vTOyzzei3fpfmU8lea3FEqdM0E2ryAiUiYMWaeMeZPHa3AGNO6fD8gIJI7IhLWwcf98NoPY8xqY8zdPR5Ux34MnHRyh+POszHmCWPMi6cYk1JKKdWea4EvgGtOdUUi4j71cHzqMiAgkjudlIfGAMeSO10pE/pSB9+LF4DbjDFjgJHAG/b0mbRT5j1OV+dTyi9pckepACIi14vISvuOxP8df3Fr73MRmS0ia0Vkg4h8IiL9gNuBe+15zxCR50XkryLyGfA/InKziDxiL58pIm/by29ovashItX2pv8EnGGv614RWSwiY7ziWiIio9vZp3QRWWDH938isqe1JlIH+1MtIn+wY1kuIple63pTRFbZwzR7+v0i8qSIzAdetGvoLLa3udbrLs3x+zFTRN6z15EiIv8SkY32Nkd7rftZEVkoIgUi0m4yyN7uNhF5wV7PP1tr5YjILBFZJyJf2uuLtNfVC/jMPi+IyHkissyOe66IxNnTC0Xkt/b0L0VkaDvn+VitHBH5vn2cNtjH7VSSSEoppUKcfU2aBtwKXCMiF4jIG16fzxSRd+3xjq5nvxGRL4Ar27tWichA+3q8SkT+y6tMgoj8zJ6+UUR+20G8P2+9bovI30TkU3t8loj8wx5/XERWi8hm73WJyJ9EZIu9jb/YZYlLgP+1r7kD7eFDEVljlzvarc10ovtjlym2ishTdmzzRSTaa13f2K58s6w3SUSW2uWPpSIyREQigP8Crrb342r5epmwr1hlyY32ax+vdT9kr6dARK7oYF9nisgiscqWW0TkCRFxncj3op1VZwAHAIwxLcaYLe2UhS4Wq4bPOhH5WKxyblvztVmuVMpvGWN00EGHABiAYcC7QLj9/jHgRqAQSOvg83RgL9Dfnp5iv94P/NRr/c8D7wFu+/3NwCP2+OvAj+1xN5Boj1fbrzOB97zWdRPwd3t8MLC6g/16BPilPT4bMB3tjz1ugIvt8T8D/2GPvwJMt8f7AFu99nUNEG2/jwGi7PG81vja2I9j74GHgf+0x88G1nuteykQacdd3hpzG/vaz459mv3+WeCnQJR9jgbb01/0Ot6FQJo9ngYsAmLt978AfuM13132+J3A0+2c52PvgVSv6b/3Wv5ry+iggw466KBDVwbgeuAZe3wpMAko8rpuPW7P09n17Ode62zvWvUecK09fjtflUnOA54EBOtG9nvAjHbinQLMtccXAyuBcOA/gR/Y01vLTW5gITAaSAG2A2J/lmS/Pg9c4bX+T4A8e3wy8GkHx+6E9gerTNEMjLHnewO4vqPt8s2yXgIQZo+fA7xpj9+MXQY8/j1W2ewme/y7wL+81j3XjnE4sLODfZ0J1AMD7OO6ALjiRL4X7az3N0AF8DbwA74q693P18tCyV7n7nvAA+3M12a5Ugcd/HXoqDqeUsq/zALGA6tEBCAaKO3C51OARcaY3QDGmMMdbGOuMaaljelnYyWKsD8/2kmsc4Ffi8jPsC78z3cw73TgcnvdH4pIRSf7A9CIVTgBK2lzrj1+DjDcnh8gQUTi7fF5xpg6ezwceESs2kUtWAmozkwHvmPH+amIpIpIov3Z+8aYBqBBREqBTKC4nfXsNcYsscf/AdyNVajZbYzJt6e/APwQ+Ptxy07BKjAtsfcxAljm9flb9usa4Ntd2KeRIvJ7IAmIAz7qwjJKKaVUe67lq2vXa1g1LD4ELhaRfwLfAn4OnEnH17PXvcbbu1adjtUMCqwf4X+xx8+zh3X2+zisGzmL2oh3DTDeLis0AGuBCcAZWNdngKtE5DYgDMi2496ClZx4WkTe56syyTF2jZOpwFyvcklkGzG0OtH9KcIqO6z32pd+Xdiud1kvEXhBRPKwbj6FdxCfd5ytZYyXsG6ytfqXMcYDbBG7VnUHVhpjCgBE5FWsclY9Xf9efIMx5r9E5GWs4zUH6/s4s41Zc4DXRSTb3sbudlbZZrnSGFPVyb4p5QhN7igVOAR4wRjzy69NFLm5k88vwbpgd0XNqQYJYIypFZEFwKXAVVgFpfZIB9O/sT+2JmNM6z618NX/MhdwulcSx1qRdVH23rd7gRLgNHuZ+g7i6yjO1hgavKZ5x9OW48+FaWfd7cWwwBhzbTuft8bRWQytngcuM8ZssL9HM7sYh1JKKfU1IpKKdTNopIgYrBoZBrgF64bFYWCVMaZKrAtzR9cz72v285zYtUqAPxpj/q+zmI0xTSJSaMe4FNgInAUMBLaKSH+sGrYTjTEVIvI8Vm2QZhGZhHUj6hrgR/a+e3MBR4zV/8upaHN/7GZEx5c/oruwXe9j+zvgM2PM5fb6Fp5EfN7lGu94OivbtFce6ur3ou2VGrMLeFxEngLK7O/l8R4G/mqMmSciM7Fq7LSlzXKlUv5K+9xRKnB8AlwhIhlwrA+Yvl34fBlwpl1AQURS7PmrgHi65hPgDnt5t4gkHPd5W+t6GngIqyDXUW2hL7ASQIjIeVhVZbuyv22Zj1XAwl5mTDvzJQIH7LtLN2AVQNvbj1aLgOvs9c4EDhljKjuJpy19ROR0e7y108ltWHfbBtnTbwA+byOm5cC01vlEJEZEOqt11NE+xQMHRCQce9+UUkqpk3QF8KIxpq8xpp8xJherRkQzMA74Pl/VvDiR61l716rl2DVq+XrnzR8B3/Xqq6V3a1miHYuwEjiLsJpm3Y7V9NpgNVuqAY7aNVEusNcZh9VE/d9YDz4YY6/r2DXXLiPsFpEr7WVERE7rII5u2Z8T3G4isM8ev9lrekdlh6Ve8V2HVY45GZNEpL9Yfe1cba/nZMo5x4jIt+SrajZ5WAmvI3xzf7z3+yav6cfP19VypVJ+QZM7SgUIY8wW4D+A+SKyEaspT3ZnnxtjyoDbgLdEZANfFazeBS5v7TSuk83fA5wlIl9iVfsdcdznG4FmsTo7vNeOZw1QCTzXybp/C5wnImuxCk0HgKrO9rcddwMTxOrkbwtWAa0tjwE3ichyrCZZrXeCvrEfXu5vXTdWx8s3cXK22tveiNVm/3FjTD3WXcO59jH2AE/Y8z8JfCAin9nn8mbgVXv55UBnj5rt6Dz/GliBdWy3neT+KKWUUmDdsHj7uGlvYiUC3sO6xr8HcILXs/auVT8GfiIiK7HKB0ftdc/Hata0zL6m/pOOb2YttpdfZowpwarNu9he1was5lCbsfrJa21WHQ+8Z8f+OVaNYLCaov1MrI56B2IlP261y1+bsWo0t6e79ocT2O6fgT+KyBK+utEF8BlWc6T1InL1ccvcDdxi7/sNWGXEk7EMqzy1CSsJ+PZJlnO83QBsF5H1WE3GrrOboB1fFrofq8y1GDjktfzx83W1XKmUX2jtSEoppbqViPTCqt471K4h0958kUCLXcX5dKxkxxjfROlbdpXn94wxI52ORSmllApkYj01q84YY0TkGqzOiDtKnvi1YNufjtg1oH9qjLnI4VCUCira545SqtuJyI3AH4CfdJTYsfUB3rCr5TZiVdtWSimllOrIeKyHIwhW05vvOhvOKQu2/VFK+ZjW3FFK+YSI3MI3q+4uMcb80Il4epJYnfd90sZHs4wx5b6ORymllApl/nRdFpFfYT1FzNtcY8wffBmHL4jIKKzmUd4ajDGTT3G9jwLTjpv8oDGms64AlApqmtxRSimllFJKKaWUCmDaobJSSimllFJKKaVUANPkjlJKKaWUUkoppVQA0+SOUkoppZRSSimlVADT5I5SSimllFJKKaVUAPv/AegmGPIUa0kxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1152x1728 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import warnings\n",
    "\n",
    "with warnings.catch_warnings():\n",
    "    warnings.simplefilter(\"ignore\")\n",
    "\n",
    "    # select numerical columns to plot\n",
    "    numerical_cols = df.select_dtypes(include=['float64']).columns.tolist()\n",
    "\n",
    "    # remove 'Year' and 'Type_of_plant' from the list\n",
    "    #numerical_cols.remove('Year')\n",
    "    #numerical_cols.remove('Type_of_plant')\n",
    "\n",
    "    # set up the subplots\n",
    "    n_rows = (len(numerical_cols) + 1) // 2\n",
    "    fig, axes = plt.subplots(n_rows, 2, figsize=(16, 6 * n_rows))\n",
    "    axes = axes.flatten()\n",
    "\n",
    "    # loop over numerical columns and plot the distplot\n",
    "    for i, col in enumerate(numerical_cols):\n",
    "        sns.distplot(df[col], ax=axes[i])\n",
    "        axes[i].set_title(col)\n",
    "\n",
    "    # remove any unused axes\n",
    "    for ax in axes[len(numerical_cols):]:\n",
    "        ax.remove()\n",
    "\n",
    "    # adjust the layout of subplots\n",
    "    plt.tight_layout()\n",
    "\n",
    "    # show the plot\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "64a95ab0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "#Select only numerical columns for normalization\n",
    "#numeric_cols = df.select_dtypes(include=['float64']).columns.tolist()\n",
    "\n",
    "# Perform MinMax scaling on selected columns\n",
    "#scaler = MinMaxScaler()\n",
    "#df[numeric_cols] = scaler.fit_transform(df[numeric_cols])\n",
    "\n",
    "# View normalized DataFrame\n",
    "#print(df.head())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "dde21d1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#from sklearn.preprocessing import FunctionTransformer\n",
    "#import numpy as np\n",
    "\n",
    "# Select only numerical columns for normalization\n",
    "#numeric_cols = df.select_dtypes(include=['float64']).columns.tolist()\n",
    "\n",
    "# Perform log transform on selected columns\n",
    "#transformer = FunctionTransformer(np.log1p, validate=True)\n",
    "#df[numeric_cols] = transformer.transform(df[numeric_cols])\n",
    "\n",
    "# View transformed DataFrame\n",
    "#print(df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "ef702936",
   "metadata": {},
   "outputs": [],
   "source": [
    "#from scipy import stats\n",
    "\n",
    "# Select only numerical columns for Box-Cox transformation\n",
    "#numeric_cols = df.select_dtypes(include=['float64']).columns.tolist()\n",
    "\n",
    "# Perform Box-Cox transformation on selected columns\n",
    "#for col in numeric_cols:\n",
    "    # Check if the column contains only positive values\n",
    "     #  if df[col].min() > 0:\n",
    "      #         df[col], _ = stats.boxcox(df[col])\n",
    "#else:\n",
    "    # Add a small positive constant to the column to make it positive\n",
    "    #df[col], _ = stats.boxcox(df[col] + 0.0001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "fcb13f92",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import pandas as pd\n",
    "from sklearn.preprocessing import PowerTransformer\n",
    "\n",
    "# Select only the numerical columns for transformation\n",
    "numerical_cols = df.select_dtypes(include=['float64']).columns.tolist()\n",
    "\n",
    "# Perform Yeo-Johnson transformation on the numerical columns\n",
    "pt = PowerTransformer(method='yeo-johnson', standardize=True)\n",
    "df[numerical_cols] = pt.fit_transform(df[numerical_cols])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "a43e59a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Select only numerical columns for normalization\n",
    "#numeric_cols = df.select_dtypes(include=['float64']).columns.tolist()\n",
    "\n",
    "# Perform RobustScaler normalization on selected columns\n",
    "#scaler = StandardScaler()\n",
    "#df[numeric_cols] = scaler.fit_transform(df[numeric_cols])\n",
    "\n",
    "# View normalized DataFrame\n",
    "#print(df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "c8fe1a90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1728 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import warnings\n",
    "\n",
    "with warnings.catch_warnings():\n",
    "    warnings.simplefilter(\"ignore\")\n",
    "\n",
    "    # select numerical columns to plot\n",
    "    numerical_cols = df.select_dtypes(include=['float64']).columns.tolist()\n",
    "\n",
    "    # remove 'Year' and 'Type_of_plant' from the list\n",
    "    #numerical_cols.remove('Year')\n",
    "    #numerical_cols.remove('Type_of_plant')\n",
    "\n",
    "    # set up the subplots\n",
    "    n_rows = (len(numerical_cols) + 1) // 2\n",
    "    fig, axes = plt.subplots(n_rows, 2, figsize=(16, 6 * n_rows))\n",
    "    axes = axes.flatten()\n",
    "\n",
    "    # loop over numerical columns and plot the distplot\n",
    "    for i, col in enumerate(numerical_cols):\n",
    "        sns.distplot(df[col], ax=axes[i],kde=True,hist=True)\n",
    "        axes[i].set_title(col)\n",
    "\n",
    "    # remove any unused axes\n",
    "    for ax in axes[len(numerical_cols):]:\n",
    "        ax.remove()\n",
    "\n",
    "    # adjust the layout of subplots\n",
    "    plt.tight_layout()\n",
    "\n",
    "    # show the plot\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "c89d684c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1728 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import warnings\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "with warnings.catch_warnings():\n",
    "    warnings.simplefilter(\"ignore\")\n",
    "\n",
    "    # select numerical columns to plot\n",
    "    numerical_cols = df.select_dtypes(include=['float64']).columns.tolist()\n",
    "\n",
    "    # remove 'Year' and 'Type_of_plant' from the list\n",
    "    #numerical_cols.remove('Year')\n",
    "    #numerical_cols.remove('Type_of_plant')\n",
    "\n",
    "    # set up the subplots\n",
    "    n_rows = (len(numerical_cols) + 1) // 2\n",
    "    fig, axes = plt.subplots(n_rows, 2, figsize=(16, 6 * n_rows))\n",
    "    axes = axes.flatten()\n",
    "\n",
    "    # loop over numerical columns and plot the boxplot\n",
    "    for i, col in enumerate(numerical_cols):\n",
    "        sns.boxplot(x=df[col], ax=axes[i])\n",
    "        axes[i].set_title(col)\n",
    "\n",
    "    # remove any unused axes\n",
    "    for ax in axes[len(numerical_cols):]:\n",
    "        ax.remove()\n",
    "\n",
    "    # adjust the layout of subplots\n",
    "    plt.tight_layout()\n",
    "\n",
    "    # show the plot\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "31a343f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#numeric_cols = df.select_dtypes(include='number').columns.tolist()\n",
    "#df[numeric_cols] = df[numeric_cols].abs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "ebc03535",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 491 entries, 1 to 769\n",
      "Data columns (total 14 columns):\n",
      " #   Column                                   Non-Null Count  Dtype  \n",
      "---  ------                                   --------------  -----  \n",
      " 0   ID                                       491 non-null    object \n",
      " 1   Type_of_plant                            491 non-null    object \n",
      " 2   Waste_delivered_from_domestic_territory  491 non-null    float64\n",
      " 3   Wastes_delivered_from_abroad             491 non-null    float64\n",
      " 4   Disposed_of_waste                        491 non-null    float64\n",
      " 5   Year                                     491 non-null    int32  \n",
      " 6   State                                    491 non-null    object \n",
      " 7   Categories                               491 non-null    object \n",
      " 8   Waste_Type                               491 non-null    object \n",
      " 9   total_waste_generated                    491 non-null    float64\n",
      " 10  waste_treatment_rate                     491 non-null    float64\n",
      " 11  Total_recovered_materials                491 non-null    float64\n",
      " 12  electricity_generation_potential         491 non-null    float64\n",
      " 13  Average_waste_generation_per_State       491 non-null    float64\n",
      "dtypes: float64(8), int32(1), object(5)\n",
      "memory usage: 55.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "87586a24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,7))\n",
    "corr=df.drop(df[[\"total_waste_generated\",\"Year\"]],axis=1).corrwith(df[\"total_waste_generated\"],method=\"kendall\")\n",
    "corr.plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "9be35870",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.562992316422388"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"waste_treatment_rate\"].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "86808936",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 5.65110456e-01  2.50521514e-02 -1.06036783e-02  4.38501530e-03\n",
      " -2.52616080e+01  2.52270852e+01  9.94195282e-02]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X =df.drop(df[[\"total_waste_generated\",\"ID\",\"Categories\",\"Type_of_plant\",\"State\",\"Year\",\"Waste_Type\"]],axis=1)\n",
    "y = df[\"total_waste_generated\"]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)\n",
    "model = LinearRegression()\n",
    "model.fit(X_train, y_train)\n",
    "print(model.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "a305102f",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "3260eb66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.01769042, -1.10624775, -0.11747729,  1.12494297, -1.15889137,\n",
       "       -0.42709607,  0.78130248, -0.57621594, -1.72230978, -0.01769042,\n",
       "       -0.24646168,  0.01362808, -0.00300149, -1.00210587,  0.64704973,\n",
       "       -0.24646168,  0.35078817,  0.42587659,  0.8655007 , -1.05446323,\n",
       "       -0.37763118,  0.45965564,  0.63139665, -0.77303782,  0.97054287,\n",
       "        0.25917182, -0.36534734,  0.57944825, -0.26577376,  0.3210871 ,\n",
       "       -0.11126349,  0.66734948, -0.17507862, -0.10429986, -0.01517206,\n",
       "        0.03982851, -0.85729592, -0.04123836, -1.19979208, -1.27355707,\n",
       "        0.43155545,  0.32192346,  0.7843739 , -0.23661418, -0.15025397,\n",
       "        0.01849852, -0.24994203,  0.03982851, -0.11126349, -0.2299327 ,\n",
       "       -0.76628199, -0.01769042, -0.01769042,  0.63554654,  0.45192393,\n",
       "       -0.11126349, -1.26233297,  0.09179504, -0.60935016,  0.28746268,\n",
       "       -0.59631703,  0.55690602, -0.26577376, -0.00681971,  0.16349694,\n",
       "       -0.11418932, -0.01769042,  0.01362808, -0.11747729, -0.24198155,\n",
       "       -0.11126349,  0.52429614, -1.06268798, -0.17507862, -0.01769042,\n",
       "       -0.11418932, -0.1878857 ,  0.32970493, -0.76149249,  0.43681958,\n",
       "       -0.51255138, -0.95131447, -0.26577376,  0.28446815,  0.15721853,\n",
       "       -0.00300149, -0.26577376, -0.26577376, -0.27036107,  0.63823605,\n",
       "        1.09302802,  0.12214968,  0.03982851, -0.42177443,  0.54531894,\n",
       "       -0.05520486,  0.6298662 ,  0.01362808,  0.67616075, -0.17507862,\n",
       "        0.30882591,  0.27074755, -0.10429986, -0.71792184, -0.16309636,\n",
       "       -0.11747729, -0.72755783, -0.01769042,  1.20307214, -0.11126349,\n",
       "       -0.00300149,  0.17981675, -0.01769042, -0.10429986, -0.51131274,\n",
       "        0.33899527, -0.27036107, -0.10429986, -0.11747729, -0.16929463,\n",
       "        0.21342831, -0.11126349,  0.20273826,  0.01362808,  0.04310833,\n",
       "       -0.59474425,  0.17175304, -0.26577376, -0.28904411, -1.15491207,\n",
       "       -0.89078788,  1.08658496, -0.28833377,  0.267917  ,  1.12383478,\n",
       "        0.59585038, -0.11126349, -0.11418932, -1.07763978,  1.12346175,\n",
       "       -0.94826903, -0.38331766,  0.01849852, -0.2308676 ,  0.32017661,\n",
       "        0.49472808, -0.17507862, -0.28678693])"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "4a5eee63",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.895844578701853\n"
     ]
    }
   ],
   "source": [
    "mse = np.mean((y_pred - y_test)**2)\n",
    "print(mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "20807864",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test, y_pred)\n",
    "plt.xlabel('Actual Values')\n",
    "plt.ylabel('Predicted Values')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "54ff79aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.18865143053304267"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "r2_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "0c8037b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "coef_df = pd.DataFrame({'feature_name': X.columns, 'coefficient': model.coef_})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "d21e2b8c",
   "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>feature_name</th>\n",
       "      <th>coefficient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Waste_delivered_from_domestic_territory</td>\n",
       "      <td>0.565110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Wastes_delivered_from_abroad</td>\n",
       "      <td>0.025052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Disposed_of_waste</td>\n",
       "      <td>-0.010604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>waste_treatment_rate</td>\n",
       "      <td>0.004385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Total_recovered_materials</td>\n",
       "      <td>-25.261608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>electricity_generation_potential</td>\n",
       "      <td>25.227085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Average_waste_generation_per_State</td>\n",
       "      <td>0.099420</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              feature_name  coefficient\n",
       "0  Waste_delivered_from_domestic_territory     0.565110\n",
       "1             Wastes_delivered_from_abroad     0.025052\n",
       "2                        Disposed_of_waste    -0.010604\n",
       "3                     waste_treatment_rate     0.004385\n",
       "4                Total_recovered_materials   -25.261608\n",
       "5         electricity_generation_potential    25.227085\n",
       "6       Average_waste_generation_per_State     0.099420"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coef_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "fd823cee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "# Create a dictionary of feature coefficients\n",
    "coef_dict = {\n",
    "    \"Input_to_waste_treatment_plants\": 0.006245,\n",
    "    \"Waste_delivered_from_domestic_territory\": 0.054712,\n",
    "    \"Wastes_delivered_from_abroad\": 0.001061,\n",
    "    \"Output_of_waste_treatment_plants\": 0.002272,\n",
    "    \"Disposed_of_waste\": -0.001620,\n",
    "    \"Wastes_to_be_recovered\": 0.004440,\n",
    "    \"Distribution_to_other_plants_users_or_traders\": 0.002041,\n",
    "    \"Total_waste_delivered\": -0.065067,\n",
    "    \"waste_treatment_rate\": -0.090413,\n",
    "    \"output_of_recovered_materials\": -204166.125461,\n",
    "    \"electricity_generation_potential\": 250836.951121,\n",
    "    \"Average_waste_generation_per_year\": 0.000393\n",
    "}\n",
    "# Create a pandas DataFrame from the dictionary\n",
    "coef_df = pd.DataFrame.from_dict(coef_dict, orient='index', columns=['Coefficient'])\n",
    "\n",
    "# Sort the DataFrame by the coefficient values\n",
    "coef_df = coef_df.sort_values(by='Coefficient')\n",
    "\n",
    "# Create a horizontal bar plot\n",
    "fig, ax = plt.subplots()\n",
    "ax.barh(y=coef_df.index, width=coef_df['Coefficient'], color='y')\n",
    "\n",
    "# Set the axis labels and title\n",
    "ax.set_xlabel('Coefficient')\n",
    "ax.set_ylabel('Feature')\n",
    "ax.set_title('Feature Coefficients')\n",
    "\n",
    "# Set the x-axis limits to zoom in on the coefficients with larger absolute values\n",
    "plt.xlim([-0.001, 0.007])\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "6359e6bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
       "                      max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "                      max_samples=None, min_impurity_decrease=0.0,\n",
       "                      min_impurity_split=None, min_samples_leaf=1,\n",
       "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                      n_estimators=110, n_jobs=None, oob_score=False,\n",
       "                      random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "rfc = RandomForestRegressor(n_estimators=110)\n",
    "X_train_rfc, X_test_rfc, y_train_rfc, y_test_rfc = train_test_split(X, y, test_size=0.3, random_state=0)\n",
    "rfc.fit(X_train_rfc,y_train_rfc)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "65f31e29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Feature Importance')"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sort = rfc.feature_importances_.argsort()\n",
    "plt.barh(X.columns[sort], rfc.feature_importances_[sort])\n",
    "plt.xlabel(\"Feature Importance\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "0b7fa5c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "total_waste_generated                      1.000000\n",
       "Waste_delivered_from_domestic_territory    0.514617\n",
       "electricity_generation_potential           0.351794\n",
       "Total_recovered_materials                  0.351651\n",
       "Wastes_delivered_from_abroad               0.265873\n",
       "Disposed_of_waste                          0.214768\n",
       "Average_waste_generation_per_State         0.124395\n",
       "Year                                      -0.000242\n",
       "waste_treatment_rate                      -0.274746\n",
       "Name: total_waste_generated, dtype: float64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix = df.corr() \n",
    "corr_matrix[\"total_waste_generated\"].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "81a6697c",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_rfc = rfc.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "13f70b41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.116865621463907"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "r2_score(y_test_rfc, y_pred_rfc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "94b183ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.97510635385302\n"
     ]
    }
   ],
   "source": [
    "mse = np.mean((y_pred_rfc - y_test_rfc)**2)\n",
    "print(mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a8ec8b5a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "d659b7ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 491 entries, 1 to 769\n",
      "Data columns (total 14 columns):\n",
      " #   Column                                   Non-Null Count  Dtype  \n",
      "---  ------                                   --------------  -----  \n",
      " 0   ID                                       491 non-null    object \n",
      " 1   Type_of_plant                            491 non-null    object \n",
      " 2   Waste_delivered_from_domestic_territory  491 non-null    float64\n",
      " 3   Wastes_delivered_from_abroad             491 non-null    float64\n",
      " 4   Disposed_of_waste                        491 non-null    float64\n",
      " 5   Year                                     491 non-null    int32  \n",
      " 6   State                                    491 non-null    object \n",
      " 7   Categories                               491 non-null    object \n",
      " 8   Waste_Type                               491 non-null    object \n",
      " 9   total_waste_generated                    491 non-null    float64\n",
      " 10  waste_treatment_rate                     491 non-null    float64\n",
      " 11  Total_recovered_materials                491 non-null    float64\n",
      " 12  electricity_generation_potential         491 non-null    float64\n",
      " 13  Average_waste_generation_per_State       491 non-null    float64\n",
      "dtypes: float64(8), int32(1), object(5)\n",
      "memory usage: 55.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "c51d0239",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2017, 2018, 2019, 2020])"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Year\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "af26026f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Baden-Württemberg', 'Bayern', 'Berlin', 'Brandenburg', 'Bremen',\n",
       "       'Hamburg', 'Hessen', 'Mecklenburg-Vorpommern', 'Niedersachsen',\n",
       "       'Nordrhein-Westfalen', 'Rheinland-Pfalz', 'Saarland', 'Sachsen',\n",
       "       'Sachsen-Anhalt', 'Schleswig-Holstein', 'Thuringen'], dtype=object)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"State\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2dd1df90",
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