Project_PowerPlant-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8afe9ca9",
   "metadata": {},
   "source": [
    "# Problem Statement:\n",
    "\n",
    "The Global Power Plant Database is a comprehensive, open source database of power plants around the world. It centralizes power plant data to make it easier to navigate, compare and draw insights for one’s own analysis. The database covers approximately 35,000 power plants from 167 countries and includes thermal plants (e.g. coal, gas, oil, nuclear, biomass, waste, geothermal) and renewables (e.g. hydro, wind, solar). Each power plant is geolocated and entries contain information on plant capacity, generation, ownership, and fuel type. It will be continuously updated as data becomes available."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2646aebd",
   "metadata": {},
   "source": [
    "# Understanding Dataset :\n",
    "\n",
    "1 country (text): 3 character country code corresponding to the ISO 3166-1 alpha-3 specification \n",
    "\n",
    "\n",
    "2 country_long (text): longer form of the country designation \n",
    "\n",
    "3 name (text): name or title of the power plant, generally in Romanized form \n",
    "\n",
    "4 gppd_idnr (text): 10 or 12 character identifier for the power plant\n",
    "\n",
    "5 6capacity_mw (number): electrical generating capacity in megawatts\n",
    "\n",
    "6 latitude (number): geolocation in decimal degrees; WGS84 (EPSG:4326) \n",
    "\n",
    "7 longitude (number): geolocation in decimal degrees; WGS84 (EPSG:4326) \n",
    "\n",
    "8 primary_fuel (text): energy source used in primary electricity generation or export\n",
    "\n",
    "9 other_fuel1 (text): energy source used in electricity generation or export \n",
    "\n",
    "10 other_fuel2 (text): energy source used in electricity generation or export \n",
    "\n",
    "11 other_fuel3 (text): energy source used in electricity generation or export \n",
    "\n",
    "12 commissioning_year (number): year of plant operation, weighted by unit-capacity when data is available \n",
    "\n",
    "13 owner (text): majority shareholder of the power plant, generally in Romanized form \n",
    "\n",
    "14 source (text): entity reporting the data; could be an organization, report, or document, generally in Romanized form\n",
    "\n",
    "15 url (text): web document corresponding to the source field '\n",
    "\n",
    "16 geolocation_source (text): attribution for geolocation information wepp_id (text): a reference to a unique plant identifier in the widely-used PLATTS-WEPP database. \n",
    "\n",
    "17 year_of_capacity_data (number): year the capacity information was reported\n",
    "\n",
    "18 generation_gwh_2013 (number): electricity generation in gigawatt-hours reported for the year 2013 \n",
    "\n",
    "19 generation_gwh_2014 (number): electricity generation in gigawatt-hours reported for the year 2014 \n",
    "\n",
    "20 generation_gwh_2015 (number): electricity generation in gigawatt-hours reported for the year 2015 \n",
    "\n",
    "21 generation_gwh_2016 (number): electricity generation in gigawatt-hours reported for the year 2016 \n",
    "\n",
    "22 generation_gwh_2017 (number): electricity generation in gigawatt-hours reported for the year 2017 \n",
    "\n",
    "23 generation_gwh_2018 (number): electricity generation in gigawatt-hours reported for the year 2018\n",
    "\n",
    "24 generation_gwh_2019 (number): electricity generation in gigawatt-hours reported for the year 2019 \n",
    "\n",
    "25 generation_data_source (text): attribution for the reported generation information \n",
    "\n",
    "26 estimated_generation_gwh_2013 (number): estimated electricity generation in gigawatt-hours for the year 2013 \n",
    "\n",
    "27 estimated_generation_gwh_2014 (number): estimated electricity generation in gigawatt-hours for the year 2014 \n",
    "\n",
    "28 estimated_generation_gwh_2015 (number): estimated electricity generation in gigawatt-hours for the year 2015 \n",
    "\n",
    "29 estimated_generation_gwh_2016 (number): estimated electricity generation in gigawatt-hours for the year 2016 \n",
    "\n",
    "30 estimated_generation_gwh_2017 (number): estimated electricity generation in gigawatt-hours for the year 2017 \n",
    "\n",
    "31 estimated_generation_note_2013(text): label of the model/method used to estimate generation for the year \n",
    "\n",
    "32 2013estimated_generation_note_2014(text): label of the model/method used to estimate generation for the year \n",
    "\n",
    "33 2014estimated_generation_note_2015(text): label of the model/method used to estimate generation for the year \n",
    "\n",
    "34 2015estimated_generation_note_2016(text): label of the model/method used to estimate generation for the year \n",
    "\n",
    "35 2016estimated_generation_note_2017 (text): label of the model/method used to estimate generation for the year 2017 Fuel \n",
    "\n",
    "Type Aggregation We define the \"Fuel Type\" attribute of our database based on common fuel categories."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b82a6d5",
   "metadata": {},
   "source": [
    "# Key attributes of the database\n",
    "\n",
    "The database includes the following indicators:\n",
    "\n",
    "Plant name\n",
    "\n",
    "Fuel type(s)\n",
    "\n",
    "Generation capacity\n",
    "\n",
    "Country\n",
    "\n",
    "Ownership\n",
    "\n",
    "Latitude/longitude of plant\n",
    "\n",
    "Data source & URL\n",
    "\n",
    "Data source year\n",
    "\n",
    "Annual generation\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91bfee8c",
   "metadata": {},
   "source": [
    "# Target :\n",
    "\n",
    " Need To Predict Primary Fuel And Capacity_mw For Global Power Plant Dataset \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a3a2bb3",
   "metadata": {},
   "source": [
    "# Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e5e2d63f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing required packages.\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy.stats as st\n",
    "\n",
    "#ploting libraries\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "#feature engineering\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
    "\n",
    "#train test split and #cross validation\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score\n",
    "from scipy.stats import zscore\n",
    "\n",
    "#metrics\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import confusion_matrix, classification_report,accuracy_score\n",
    "from sklearn.metrics import r2_score,mean_squared_error\n",
    "\n",
    "#classification \n",
    "import sklearn\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "#regreassion\n",
    "from sklearn.linear_model import LinearRegression, Ridge, Lasso\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.ensemble import AdaBoostRegressor\n",
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b06a636",
   "metadata": {},
   "source": [
    "# importing Dataset and Data Information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c0b04f62",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Loading dataset\n",
    "df=pd.read_csv('power_plant.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "07f810d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(907, 27)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5ca98602",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>country_long</th>\n",
       "      <th>name</th>\n",
       "      <th>gppd_idnr</th>\n",
       "      <th>capacity_mw</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>primary_fuel</th>\n",
       "      <th>other_fuel1</th>\n",
       "      <th>other_fuel2</th>\n",
       "      <th>...</th>\n",
       "      <th>year_of_capacity_data</th>\n",
       "      <th>generation_gwh_2013</th>\n",
       "      <th>generation_gwh_2014</th>\n",
       "      <th>generation_gwh_2015</th>\n",
       "      <th>generation_gwh_2016</th>\n",
       "      <th>generation_gwh_2017</th>\n",
       "      <th>generation_gwh_2018</th>\n",
       "      <th>generation_gwh_2019</th>\n",
       "      <th>generation_data_source</th>\n",
       "      <th>estimated_generation_gwh</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IND</td>\n",
       "      <td>India</td>\n",
       "      <td>ACME Solar Tower</td>\n",
       "      <td>WRI1020239</td>\n",
       "      <td>2.5</td>\n",
       "      <td>28.1839</td>\n",
       "      <td>73.2407</td>\n",
       "      <td>Solar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>IND</td>\n",
       "      <td>India</td>\n",
       "      <td>ADITYA CEMENT WORKS</td>\n",
       "      <td>WRI1019881</td>\n",
       "      <td>98.0</td>\n",
       "      <td>24.7663</td>\n",
       "      <td>74.6090</td>\n",
       "      <td>Coal</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>IND</td>\n",
       "      <td>India</td>\n",
       "      <td>AES Saurashtra Windfarms</td>\n",
       "      <td>WRI1026669</td>\n",
       "      <td>39.2</td>\n",
       "      <td>21.9038</td>\n",
       "      <td>69.3732</td>\n",
       "      <td>Wind</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IND</td>\n",
       "      <td>India</td>\n",
       "      <td>AGARTALA GT</td>\n",
       "      <td>IND0000001</td>\n",
       "      <td>135.0</td>\n",
       "      <td>23.8712</td>\n",
       "      <td>91.3602</td>\n",
       "      <td>Gas</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>617.789264</td>\n",
       "      <td>843.747</td>\n",
       "      <td>886.004428</td>\n",
       "      <td>663.774500</td>\n",
       "      <td>626.239128</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Central Electricity Authority</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IND</td>\n",
       "      <td>India</td>\n",
       "      <td>AKALTARA TPP</td>\n",
       "      <td>IND0000002</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>21.9603</td>\n",
       "      <td>82.4091</td>\n",
       "      <td>Coal</td>\n",
       "      <td>Oil</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3035.550000</td>\n",
       "      <td>5916.370</td>\n",
       "      <td>6243.000000</td>\n",
       "      <td>5385.579736</td>\n",
       "      <td>7279.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Central Electricity Authority</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  country country_long                      name   gppd_idnr  capacity_mw  \\\n",
       "0     IND        India          ACME Solar Tower  WRI1020239          2.5   \n",
       "1     IND        India       ADITYA CEMENT WORKS  WRI1019881         98.0   \n",
       "2     IND        India  AES Saurashtra Windfarms  WRI1026669         39.2   \n",
       "3     IND        India               AGARTALA GT  IND0000001        135.0   \n",
       "4     IND        India              AKALTARA TPP  IND0000002       1800.0   \n",
       "\n",
       "   latitude  longitude primary_fuel other_fuel1 other_fuel2  ...  \\\n",
       "0   28.1839    73.2407        Solar         NaN         NaN  ...   \n",
       "1   24.7663    74.6090         Coal         NaN         NaN  ...   \n",
       "2   21.9038    69.3732         Wind         NaN         NaN  ...   \n",
       "3   23.8712    91.3602          Gas         NaN         NaN  ...   \n",
       "4   21.9603    82.4091         Coal         Oil         NaN  ...   \n",
       "\n",
       "   year_of_capacity_data  generation_gwh_2013 generation_gwh_2014  \\\n",
       "0                    NaN                  NaN                 NaN   \n",
       "1                    NaN                  NaN                 NaN   \n",
       "2                    NaN                  NaN                 NaN   \n",
       "3                 2019.0                  NaN          617.789264   \n",
       "4                 2019.0                  NaN         3035.550000   \n",
       "\n",
       "  generation_gwh_2015 generation_gwh_2016 generation_gwh_2017  \\\n",
       "0                 NaN                 NaN                 NaN   \n",
       "1                 NaN                 NaN                 NaN   \n",
       "2                 NaN                 NaN                 NaN   \n",
       "3             843.747          886.004428          663.774500   \n",
       "4            5916.370         6243.000000         5385.579736   \n",
       "\n",
       "   generation_gwh_2018  generation_gwh_2019         generation_data_source  \\\n",
       "0                  NaN                  NaN                            NaN   \n",
       "1                  NaN                  NaN                            NaN   \n",
       "2                  NaN                  NaN                            NaN   \n",
       "3           626.239128                  NaN  Central Electricity Authority   \n",
       "4          7279.000000                  NaN  Central Electricity Authority   \n",
       "\n",
       "   estimated_generation_gwh  \n",
       "0                       NaN  \n",
       "1                       NaN  \n",
       "2                       NaN  \n",
       "3                       NaN  \n",
       "4                       NaN  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be6f0a51",
   "metadata": {},
   "source": [
    "Checking the first 5  of our entire dataset. We can see that our dataset have total 907 rows and 27 columns.\n",
    "\n",
    "In our problem statement we have  to predict 2 target primary_fuel and capacity_mw. When we take a look at the values present in the column primary_fuel we see that there are categorical data in that column so when we consider it as our label it will be termed as a Classification problem!\n",
    "\n",
    "Similarly, if we take the values present in the column capacity_mw we see that there are continous data in that column so when we consider it as our label it will be termed as a Regression problem!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "597dc2f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['country', 'country_long', 'name', 'gppd_idnr', 'capacity_mw',\n",
       "       'latitude', 'longitude', 'primary_fuel', 'other_fuel1', 'other_fuel2',\n",
       "       'other_fuel3', 'commissioning_year', 'owner', 'source', 'url',\n",
       "       'geolocation_source', 'wepp_id', 'year_of_capacity_data',\n",
       "       'generation_gwh_2013', 'generation_gwh_2014', 'generation_gwh_2015',\n",
       "       'generation_gwh_2016', 'generation_gwh_2017', 'generation_gwh_2018',\n",
       "       'generation_gwh_2019', 'generation_data_source',\n",
       "       'estimated_generation_gwh'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db6045b3",
   "metadata": {},
   "source": [
    "Major Key attributes we need for prediction :-\n",
    "\n",
    "Plant name\n",
    "\n",
    "Fuel type(s)\n",
    "\n",
    "Generation capacity\n",
    "\n",
    "Country\n",
    "\n",
    "Ownership\n",
    "\n",
    "Latitude/longitude of plant\n",
    "\n",
    "Data source & URL\n",
    "\n",
    "Data source year\n",
    "\n",
    "Annual generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a408b5a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 907 entries, 0 to 906\n",
      "Data columns (total 27 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   country                   907 non-null    object \n",
      " 1   country_long              907 non-null    object \n",
      " 2   name                      907 non-null    object \n",
      " 3   gppd_idnr                 907 non-null    object \n",
      " 4   capacity_mw               907 non-null    float64\n",
      " 5   latitude                  861 non-null    float64\n",
      " 6   longitude                 861 non-null    float64\n",
      " 7   primary_fuel              907 non-null    object \n",
      " 8   other_fuel1               198 non-null    object \n",
      " 9   other_fuel2               1 non-null      object \n",
      " 10  other_fuel3               0 non-null      float64\n",
      " 11  commissioning_year        527 non-null    float64\n",
      " 12  owner                     342 non-null    object \n",
      " 13  source                    907 non-null    object \n",
      " 14  url                       907 non-null    object \n",
      " 15  geolocation_source        888 non-null    object \n",
      " 16  wepp_id                   0 non-null      float64\n",
      " 17  year_of_capacity_data     519 non-null    float64\n",
      " 18  generation_gwh_2013       0 non-null      float64\n",
      " 19  generation_gwh_2014       398 non-null    float64\n",
      " 20  generation_gwh_2015       422 non-null    float64\n",
      " 21  generation_gwh_2016       434 non-null    float64\n",
      " 22  generation_gwh_2017       440 non-null    float64\n",
      " 23  generation_gwh_2018       448 non-null    float64\n",
      " 24  generation_gwh_2019       0 non-null      float64\n",
      " 25  generation_data_source    449 non-null    object \n",
      " 26  estimated_generation_gwh  0 non-null      float64\n",
      "dtypes: float64(15), object(12)\n",
      "memory usage: 191.4+ KB\n"
     ]
    }
   ],
   "source": [
    "# check datatypes and memory captured\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36435648",
   "metadata": {},
   "source": [
    "Observations:\n",
    "\n",
    "1. Two types of data are present:\n",
    "    \n",
    "   categorical -12\n",
    "    numerical  -15\n",
    "\n",
    "2. Null value present in most of the column\n",
    "\n",
    "3. No any value present in the column\n",
    "\n",
    "other_fuel3 ,wepp_id,generation_gwh_2013,generation_gwh_2019,estimated_generation_gwh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "665507b9",
   "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>capacity_mw</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>other_fuel3</th>\n",
       "      <th>commissioning_year</th>\n",
       "      <th>wepp_id</th>\n",
       "      <th>year_of_capacity_data</th>\n",
       "      <th>generation_gwh_2013</th>\n",
       "      <th>generation_gwh_2014</th>\n",
       "      <th>generation_gwh_2015</th>\n",
       "      <th>generation_gwh_2016</th>\n",
       "      <th>generation_gwh_2017</th>\n",
       "      <th>generation_gwh_2018</th>\n",
       "      <th>generation_gwh_2019</th>\n",
       "      <th>estimated_generation_gwh</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>907.000000</td>\n",
       "      <td>861.000000</td>\n",
       "      <td>861.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>527.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>519.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>398.000000</td>\n",
       "      <td>422.000000</td>\n",
       "      <td>434.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>448.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>326.223755</td>\n",
       "      <td>21.197918</td>\n",
       "      <td>77.464907</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1997.091082</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2431.823590</td>\n",
       "      <td>2428.226946</td>\n",
       "      <td>2467.936859</td>\n",
       "      <td>2547.759305</td>\n",
       "      <td>2600.804099</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>590.085456</td>\n",
       "      <td>6.239612</td>\n",
       "      <td>4.939316</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.082868</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4026.440035</td>\n",
       "      <td>4194.596959</td>\n",
       "      <td>4162.884308</td>\n",
       "      <td>4196.991169</td>\n",
       "      <td>4314.880456</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.168900</td>\n",
       "      <td>68.644700</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1927.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>16.725000</td>\n",
       "      <td>16.773900</td>\n",
       "      <td>74.256200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1988.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>223.557672</td>\n",
       "      <td>176.381063</td>\n",
       "      <td>188.285252</td>\n",
       "      <td>177.874930</td>\n",
       "      <td>193.378250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>59.200000</td>\n",
       "      <td>21.780000</td>\n",
       "      <td>76.719500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2001.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>801.123775</td>\n",
       "      <td>711.181225</td>\n",
       "      <td>737.205450</td>\n",
       "      <td>817.977250</td>\n",
       "      <td>751.644375</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>385.250000</td>\n",
       "      <td>25.512400</td>\n",
       "      <td>79.440800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3035.306250</td>\n",
       "      <td>3084.121250</td>\n",
       "      <td>3282.861313</td>\n",
       "      <td>3275.690475</td>\n",
       "      <td>3143.535900</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4760.000000</td>\n",
       "      <td>34.649000</td>\n",
       "      <td>95.408000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28127.000000</td>\n",
       "      <td>30539.000000</td>\n",
       "      <td>30015.000000</td>\n",
       "      <td>35116.000000</td>\n",
       "      <td>35136.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       capacity_mw    latitude   longitude  other_fuel3  commissioning_year  \\\n",
       "count   907.000000  861.000000  861.000000          0.0          527.000000   \n",
       "mean    326.223755   21.197918   77.464907          NaN         1997.091082   \n",
       "std     590.085456    6.239612    4.939316          NaN           17.082868   \n",
       "min       0.000000    8.168900   68.644700          NaN         1927.000000   \n",
       "25%      16.725000   16.773900   74.256200          NaN         1988.000000   \n",
       "50%      59.200000   21.780000   76.719500          NaN         2001.000000   \n",
       "75%     385.250000   25.512400   79.440800          NaN         2012.000000   \n",
       "max    4760.000000   34.649000   95.408000          NaN         2018.000000   \n",
       "\n",
       "       wepp_id  year_of_capacity_data  generation_gwh_2013  \\\n",
       "count      0.0                  519.0                  0.0   \n",
       "mean       NaN                 2019.0                  NaN   \n",
       "std        NaN                    0.0                  NaN   \n",
       "min        NaN                 2019.0                  NaN   \n",
       "25%        NaN                 2019.0                  NaN   \n",
       "50%        NaN                 2019.0                  NaN   \n",
       "75%        NaN                 2019.0                  NaN   \n",
       "max        NaN                 2019.0                  NaN   \n",
       "\n",
       "       generation_gwh_2014  generation_gwh_2015  generation_gwh_2016  \\\n",
       "count           398.000000           422.000000           434.000000   \n",
       "mean           2431.823590          2428.226946          2467.936859   \n",
       "std            4026.440035          4194.596959          4162.884308   \n",
       "min               0.000000             0.000000             0.000000   \n",
       "25%             223.557672           176.381063           188.285252   \n",
       "50%             801.123775           711.181225           737.205450   \n",
       "75%            3035.306250          3084.121250          3282.861313   \n",
       "max           28127.000000         30539.000000         30015.000000   \n",
       "\n",
       "       generation_gwh_2017  generation_gwh_2018  generation_gwh_2019  \\\n",
       "count           440.000000           448.000000                  0.0   \n",
       "mean           2547.759305          2600.804099                  NaN   \n",
       "std            4196.991169          4314.880456                  NaN   \n",
       "min               0.000000             0.000000                  NaN   \n",
       "25%             177.874930           193.378250                  NaN   \n",
       "50%             817.977250           751.644375                  NaN   \n",
       "75%            3275.690475          3143.535900                  NaN   \n",
       "max           35116.000000         35136.000000                  NaN   \n",
       "\n",
       "       estimated_generation_gwh  \n",
       "count                       0.0  \n",
       "mean                        NaN  \n",
       "std                         NaN  \n",
       "min                         NaN  \n",
       "25%                         NaN  \n",
       "50%                         NaN  \n",
       "75%                         NaN  \n",
       "max                         NaN  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b606577",
   "metadata": {},
   "source": [
    "# Check for null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5232854f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country                       0\n",
       "country_long                  0\n",
       "name                          0\n",
       "gppd_idnr                     0\n",
       "capacity_mw                   0\n",
       "latitude                     46\n",
       "longitude                    46\n",
       "primary_fuel                  0\n",
       "other_fuel1                 709\n",
       "other_fuel2                 906\n",
       "other_fuel3                 907\n",
       "commissioning_year          380\n",
       "owner                       565\n",
       "source                        0\n",
       "url                           0\n",
       "geolocation_source           19\n",
       "wepp_id                     907\n",
       "year_of_capacity_data       388\n",
       "generation_gwh_2013         907\n",
       "generation_gwh_2014         509\n",
       "generation_gwh_2015         485\n",
       "generation_gwh_2016         473\n",
       "generation_gwh_2017         467\n",
       "generation_gwh_2018         459\n",
       "generation_gwh_2019         907\n",
       "generation_data_source      458\n",
       "estimated_generation_gwh    907\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5a3b789c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.isnull())##check missing value through visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7027b565",
   "metadata": {},
   "source": [
    " Missing values present  in many columns and few even have all the 907 rows empty that will need to be removed since it provides no insights."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "853316aa",
   "metadata": {},
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69d72bed",
   "metadata": {},
   "source": [
    "1. Drop the columns other_fuel3 ,wepp_id,generation_gwh_2013,generation_gwh_2019,estimated_generation_gwh does not contail any value.\n",
    "\n",
    "2. Drop the columns other_fuel2 because it caontain only one value ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3e0635c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['other_fuel2','other_fuel3','wepp_id',\"generation_gwh_2013\",\"estimated_generation_gwh\",\"generation_gwh_2019\"],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3233b83a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(907, 21)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "af1c8160",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['country', 'country_long', 'name', 'gppd_idnr', 'capacity_mw',\n",
       "       'latitude', 'longitude', 'primary_fuel', 'other_fuel1',\n",
       "       'commissioning_year', 'owner', 'source', 'url', 'geolocation_source',\n",
       "       'year_of_capacity_data', 'generation_gwh_2014', 'generation_gwh_2015',\n",
       "       'generation_gwh_2016', 'generation_gwh_2017', 'generation_gwh_2018',\n",
       "       'generation_data_source'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "541e49c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Solar' 'Coal' 'Wind' 'Gas' 'Hydro' 'Biomass' 'Oil' 'Nuclear']\n",
      "[nan 'Oil' 'Cogeneration' 'Gas']\n",
      "['IND']\n",
      "['India']\n"
     ]
    }
   ],
   "source": [
    "print(df['primary_fuel'].unique())\n",
    "print(df['other_fuel1'].unique())\n",
    "print(df['country'].unique())\n",
    "print(df['country_long'].unique())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c953bd6",
   "metadata": {},
   "source": [
    "# Country "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "516be204",
   "metadata": {},
   "source": [
    "But in our study we will be working on the dataset only for INDIA. The data set contains only 907 rows and 27 columns. The data set provides information of all the power plant situated at diffrent loactions in india."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0d07bef",
   "metadata": {},
   "source": [
    "Country and contry long both represent the country name so we can use only one column for reprenting county.\n",
    "\n",
    "Drop the coumn country_long"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "45297b0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['country_long'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "64e03cd9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(907, 20)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7eb17cbc",
   "metadata": {},
   "source": [
    "# Name and gppd_idnr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "eff933fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "907"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['name'].unique().size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5d54b593",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "907"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['gppd_idnr'].unique().size"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75cf507c",
   "metadata": {},
   "source": [
    "We have seen that Name and gppd-idnr have all unique data in all rows of the dataset and being a unique identifier would help to build machine learning model  either and can be deleted too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b8b72898",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['name','gppd_idnr'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79b27297",
   "metadata": {},
   "source": [
    "# With the help of nunique method we were able to check the unique data information present in our dataset columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ebf3f642",
   "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>Unique Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>capacity_mw</th>\n",
       "      <td>361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>latitude</th>\n",
       "      <td>836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>longitude</th>\n",
       "      <td>827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>primary_fuel</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>other_fuel1</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>commissioning_year</th>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>owner</th>\n",
       "      <td>280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>source</th>\n",
       "      <td>191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>url</th>\n",
       "      <td>304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>geolocation_source</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year_of_capacity_data</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>generation_gwh_2014</th>\n",
       "      <td>371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>generation_gwh_2015</th>\n",
       "      <td>396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>generation_gwh_2016</th>\n",
       "      <td>403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>generation_gwh_2017</th>\n",
       "      <td>408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>generation_gwh_2018</th>\n",
       "      <td>410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>generation_data_source</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Unique Values\n",
       "country                             1\n",
       "capacity_mw                       361\n",
       "latitude                          836\n",
       "longitude                         827\n",
       "primary_fuel                        8\n",
       "other_fuel1                         3\n",
       "commissioning_year                 73\n",
       "owner                             280\n",
       "source                            191\n",
       "url                               304\n",
       "geolocation_source                  3\n",
       "year_of_capacity_data               1\n",
       "generation_gwh_2014               371\n",
       "generation_gwh_2015               396\n",
       "generation_gwh_2016               403\n",
       "generation_gwh_2017               408\n",
       "generation_gwh_2018               410\n",
       "generation_data_source              1"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nunique().to_frame(\"Unique Values\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c70cfe5",
   "metadata": {},
   "source": [
    "Observation:\n",
    "    \n",
    "1. In the column \"country\" has only one unique value in all 9087 rows and does not provide any insight so it can be deleted.\n",
    "\n",
    "2. Similarly, the column \"year_of_capacity_data\"  has 1 single value present in it's rows and has missing data as well so it will not provide any useful information for  prediction of  ML models so it can be deleted\n",
    "\n",
    "3.  I observed that there is a column \"url\" which b holds web document links and values such as those or descriptions do not really help in  prediction machine learning modelsso we can it too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6987d86d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop([\"country\", \"year_of_capacity_data\", \"url\"], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c1d6feaf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(907, 15)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52f9274b",
   "metadata": {},
   "source": [
    "We have successfully dropped 3 more columns that were not providing any useful information for our predition models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7701d3b",
   "metadata": {},
   "source": [
    "# Check for column which has above  50%(more than 454) null value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "120778f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "capacity_mw                 0\n",
       "latitude                   46\n",
       "longitude                  46\n",
       "primary_fuel                0\n",
       "other_fuel1               709\n",
       "commissioning_year        380\n",
       "owner                     565\n",
       "source                      0\n",
       "geolocation_source         19\n",
       "generation_gwh_2014       509\n",
       "generation_gwh_2015       485\n",
       "generation_gwh_2016       473\n",
       "generation_gwh_2017       467\n",
       "generation_gwh_2018       459\n",
       "generation_data_source    458\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35e1f440",
   "metadata": {},
   "source": [
    "Observe tha the columns owner,other_fuel1,geolocation_source,generation_gwh_2014,generation_gwh_2015,generation_gwh_2016,generation_gwh_2017,generation_gwh_2018,generation_data_source has  more than 50% (more than 454 rows) of missing data so we can delete it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b045292e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop([\"other_fuel1\", \"owner\", \"generation_gwh_2014\",\n",
    "        \"generation_gwh_2015\", \"generation_gwh_2018\", \"generation_gwh_2016\", \"generation_gwh_2017\",\n",
    "        \"generation_data_source\"], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "924a9999",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(907, 7)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d49ae15",
   "metadata": {},
   "source": [
    "Now we have only 7 columns for making machine learning models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a899a173",
   "metadata": {},
   "source": [
    "# Feature engineering:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3a481fe",
   "metadata": {},
   "source": [
    "# Age of Powerplant "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ff4026f",
   "metadata": {},
   "source": [
    " Convert commissioning_year into PowerPlant_age by subtracting it from 2018"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "cc07c37f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['PowerPlant_age'] = 2019 - df['commissioning_year']\n",
    "df.drop(columns=[\"commissioning_year\"],inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be56fa1b",
   "metadata": {},
   "source": [
    "Create new column PowerPlant Age and Drop the column commissioning_year may Age is more helpfull for prediction as compare to commissioning_year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6dee8220",
   "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",
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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>capacity_mw</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>primary_fuel</th>\n",
       "      <th>source</th>\n",
       "      <th>geolocation_source</th>\n",
       "      <th>PowerPlant_age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.5</td>\n",
       "      <td>28.1839</td>\n",
       "      <td>73.2407</td>\n",
       "      <td>Solar</td>\n",
       "      <td>National Renewable Energy Laboratory</td>\n",
       "      <td>National Renewable Energy Laboratory</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>98.0</td>\n",
       "      <td>24.7663</td>\n",
       "      <td>74.6090</td>\n",
       "      <td>Coal</td>\n",
       "      <td>Ultratech Cement ltd</td>\n",
       "      <td>WRI</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>39.2</td>\n",
       "      <td>21.9038</td>\n",
       "      <td>69.3732</td>\n",
       "      <td>Wind</td>\n",
       "      <td>CDM</td>\n",
       "      <td>WRI</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>135.0</td>\n",
       "      <td>23.8712</td>\n",
       "      <td>91.3602</td>\n",
       "      <td>Gas</td>\n",
       "      <td>Central Electricity Authority</td>\n",
       "      <td>WRI</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1800.0</td>\n",
       "      <td>21.9603</td>\n",
       "      <td>82.4091</td>\n",
       "      <td>Coal</td>\n",
       "      <td>Central Electricity Authority</td>\n",
       "      <td>WRI</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   capacity_mw  latitude  longitude primary_fuel  \\\n",
       "0          2.5   28.1839    73.2407        Solar   \n",
       "1         98.0   24.7663    74.6090         Coal   \n",
       "2         39.2   21.9038    69.3732         Wind   \n",
       "3        135.0   23.8712    91.3602          Gas   \n",
       "4       1800.0   21.9603    82.4091         Coal   \n",
       "\n",
       "                                 source                    geolocation_source  \\\n",
       "0  National Renewable Energy Laboratory  National Renewable Energy Laboratory   \n",
       "1                  Ultratech Cement ltd                                   WRI   \n",
       "2                                   CDM                                   WRI   \n",
       "3         Central Electricity Authority                                   WRI   \n",
       "4         Central Electricity Authority                                   WRI   \n",
       "\n",
       "   PowerPlant_age  \n",
       "0             8.0  \n",
       "1             NaN  \n",
       "2             NaN  \n",
       "3            15.0  \n",
       "4             4.0  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "106ba9b6",
   "metadata": {},
   "source": [
    "# Treating null values "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d77ae9f",
   "metadata": {},
   "source": [
    "\n",
    " We still have missing values present in our dataset columns that will need to be treated now. But before that we will separate our categorical columns and numerical columns so that application of meand and mode will become easier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "665e0756",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "capacity_mw             0\n",
       "latitude               46\n",
       "longitude              46\n",
       "primary_fuel            0\n",
       "source                  0\n",
       "geolocation_source     19\n",
       "PowerPlant_age        380\n",
       "dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86fa39d7",
   "metadata": {},
   "source": [
    "# Segregate the numerical and Categorical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0cce422a",
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>primary_fuel</th>\n",
       "      <th>source</th>\n",
       "      <th>geolocation_source</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>Biomass</td>\n",
       "      <td>Sri Sugars ltd</td>\n",
       "      <td>WRI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>697</th>\n",
       "      <td>Coal</td>\n",
       "      <td>Central Electricity Authority</td>\n",
       "      <td>WRI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>Hydro</td>\n",
       "      <td>Central Electricity Authority</td>\n",
       "      <td>WRI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>634</th>\n",
       "      <td>Hydro</td>\n",
       "      <td>Central Electricity Authority</td>\n",
       "      <td>WRI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Coal</td>\n",
       "      <td>Jk Cement ltd</td>\n",
       "      <td>WRI</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    primary_fuel                         source geolocation_source\n",
       "347      Biomass                 Sri Sugars ltd                WRI\n",
       "697         Coal  Central Electricity Authority                WRI\n",
       "166        Hydro  Central Electricity Authority                WRI\n",
       "634        Hydro  Central Electricity Authority                WRI\n",
       "48          Coal                  Jk Cement ltd                WRI"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical = [feature for feature in df.columns if df[feature].dtypes=='object']\n",
    "df[categorical].sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a1e2c2b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>capacity_mw</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>PowerPlant_age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>98.7</td>\n",
       "      <td>23.6756</td>\n",
       "      <td>75.0565</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>862</th>\n",
       "      <td>90.0</td>\n",
       "      <td>27.3455</td>\n",
       "      <td>82.3166</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>526</th>\n",
       "      <td>1500.0</td>\n",
       "      <td>31.5640</td>\n",
       "      <td>77.9800</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>5.0</td>\n",
       "      <td>11.6117</td>\n",
       "      <td>92.7109</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>30.0</td>\n",
       "      <td>11.3310</td>\n",
       "      <td>77.7580</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     capacity_mw  latitude  longitude  PowerPlant_age\n",
       "128         98.7   23.6756    75.0565             NaN\n",
       "862         90.0   27.3455    82.3166             7.0\n",
       "526       1500.0   31.5640    77.9800            16.0\n",
       "244          5.0   11.6117    92.7109             NaN\n",
       "85          30.0   11.3310    77.7580             6.0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numerical = [feature for feature in df.columns if df[feature].dtypes!='object']\n",
    "df[numerical].sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c430449c",
   "metadata": {},
   "source": [
    "We have  seperated the object datatype column names and numeric data type column names. Since we do not have to worry about outliers and skewness in categorical columns we can use this separated column names to pre process only on  continous columns."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca31c772",
   "metadata": {},
   "source": [
    "# Filling missing data for numerical values with mean and  categorical values with mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7b52de2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# filling missing data for continous values with mean\n",
    "df[\"latitude\"].fillna(df[\"latitude\"].mean(),inplace=True)\n",
    "df[\"longitude\"].fillna(df[\"longitude\"].mean(),inplace=True)\n",
    "\n",
    "\n",
    "# filling missing data for categorical values with mode\n",
    "df[\"PowerPlant_age\"].fillna(df[\"PowerPlant_age\"].mode()[0],inplace=True)\n",
    "df[\"geolocation_source\"].fillna(df[\"geolocation_source\"].mode()[0],inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14d39cd9",
   "metadata": {},
   "source": [
    "# Check for missing value present or not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "2cf940e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "0965b172",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.isnull())##check missing value through visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "385a3cff",
   "metadata": {},
   "source": [
    "Now our data has no any missing values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff304409",
   "metadata": {},
   "source": [
    "# Observation from categorical columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "0e0ade98",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "primary_fuel\n",
      "Coal       258\n",
      "Hydro      251\n",
      "Solar      127\n",
      "Wind       123\n",
      "Gas         69\n",
      "Biomass     50\n",
      "Oil         20\n",
      "Nuclear      9\n",
      "Name: primary_fuel, dtype: int64\n",
      "------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "source\n",
      "Central Electricity Authority                                  519\n",
      "CDM                                                            124\n",
      "Lancosola                                                       10\n",
      "National Renewable Energy Laboratory                             8\n",
      "National Thermal Power Corporation (NTPC)                        6\n",
      "                                                              ... \n",
      "Harsha Engineers Limited                                         1\n",
      "Godawari Energy ltd                                              1\n",
      "Sunkon Energy Private Limited                                    1\n",
      "West Bengal Energy Development Corporation Limited (WBEDCL)      1\n",
      "Yashwantrao Krishna ssk                                          1\n",
      "Name: source, Length: 191, dtype: int64\n",
      "------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "geolocation_source\n",
      "WRI                                     784\n",
      "Industry About                          119\n",
      "National Renewable Energy Laboratory      4\n",
      "Name: geolocation_source, dtype: int64\n",
      "------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "for colum in categorical:\n",
    "    print(colum)\n",
    "    print(df[colum].value_counts())\n",
    "    print(\"--\"*120)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5e1f262",
   "metadata": {},
   "source": [
    "1. Coal and Hydra fluel type is majorly used in power plant.\n",
    "\n",
    "2. The organization  Central Electricity Authority is higher on demand.\n",
    "\n",
    "3. the geolocation_source  WRI is maximum used in power plant."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87811ff0",
   "metadata": {},
   "source": [
    "# Univariate  Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d495bd12",
   "metadata": {},
   "source": [
    "# Primary_fuel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "60d2d197",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Fluel type')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize=(15,10))\n",
    "values = df['primary_fuel'].value_counts()\n",
    "index=0\n",
    "ax=sns.countplot('primary_fuel', data=df)\n",
    "\n",
    "for i in ax.get_xticklabels():\n",
    "        ax.text(index, values[i.get_text()]/2, values[i.get_text()], \n",
    "                horizontalalignment=\"center\", fontweight='bold', color='w')\n",
    "        index += 1\n",
    "        \n",
    "plt.title('Count of primary_fuel', fontsize=15)\n",
    "plt.ylabel(\"Number of rows\",fontsize=15)\n",
    "plt.xlabel(\"Fluel type\",fontsize=15)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff179bd9",
   "metadata": {},
   "source": [
    "In the above plot  for \"primary_fuel\" column we can see that the highest number of values have been covered by coal  and hydro fuel  types then comes solar and wind. Finally we see that gas, biomass, oil and nuclear have very low data counts\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1bf1f8b",
   "metadata": {},
   "source": [
    "when we will be considering \"primary_fuel\" as our target label then this is impose a class imbalance issue while trying to create a classification model and therefore will need to be treated accordingly."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc43161e",
   "metadata": {},
   "source": [
    "# Geolocation_source"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d3d1e0fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Geolocation_source')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LgfOrakE7vyDJRgDt4/Vt+Xxg0571NgGu7d9YVR1RVbOravaMGTPGMWxJkqSJMVlJ2utY0tUJcCKwT/t8H+CHPeV7J1k9yRbALODcCYtSkiRpkqw60TtMsibwQuAtPcWHAscl2Q+4CtgToKouTnIccAlwP3BAVS2e4JAlSZIm3IQnaVV1J7B+X9mNNKM9h6t/CHDIBIQmSZLUGd5xQJIkqYNM0iRJkjrIJE2SJKmDTNIkSZI6yCRNkiSpg0zSJEmSOsgkTZIkqYNM0iRJkjrIJE2SJKmDTNIkSZI6yCRNkiSpg0zSJEmSOsgkTZIkqYNM0iRJkjrIJE2SJKmDTNIkSZI6yCRNkiSpg0zSJEmSOsgkTZIkqYNM0iRJkjrIJE2SJKmDTNIkSZI6yCRNkiSpg0zSJEmSOsgkTZIkqYNM0iRJkjrIJE2SJKmDTNIkSZI6yCRNkiSpg0zSJEmSOsgkTZIkqYNM0iRJkjrIJE2SJKmDTNIkSZI6yCRNkiSpg0zSJEmSOsgkTZIkqYMmPElLsm6S45NcmuQPSXZMMj3JqUkubx/X66l/UJJ5SS5LsttExytJkjQZJqMl7d+Bk6rqScDTgT8ABwJzqmoWMKedJ8lWwN7A1sDuwGFJpk1CzJIkSRNqQpO0JI8Gngv8F0BV3VtVNwN7AEe11Y4CXtk+3wM4pqruqaorgHnADhMZsyRJ0mSY6Ja0LYGFwH8nuSDJ15OsBcysqusA2scN2/obA1f3rD+/LZMkSVqpTXSStirwDOCrVbUtcAdt1+YIMkxZLVMp2T/J3CRzFy5cODaRSpIkTaKJTtLmA/Or6px2/niapG1Bko0A2sfre+pv2rP+JsC1/RutqiOqanZVzZ4xY8a4BS9JkjRRJjRJq6q/AFcneWJbtCtwCXAisE9btg/ww/b5icDeSVZPsgUwCzh3AkOWJEmaFKtOwj7fAXw7ySOAPwFvokkWj0uyH3AVsCdAVV2c5DiaRO5+4ICqWjwJMUuSJE2oCU/SqupCYPYwi3Ydof4hwCHjGZMkSVLXeMcBSZKkDjJJkyRJ6iCTNEmSpA4ySZMkSeogkzRJkqQOMkmTJEnqIJM0SZKkDjJJkyRJ6iCTNEmSpA4ySZMkSeogkzRJkqQOMkmTJEnqIJM0SZKkDjJJkyRJ6iCTNEmSpA4ySZMkSeogkzRJkqQOMkmTJEnqIJM0SZKkDjJJkyRJ6iCTNEmSpA4ySZMkSeogkzRJkqQOekhJWpJ1xygOSZIk9RgoSUvytiQf6JnfJsl84MYk5yXZZNwilCRJmoIGbUl7B3Brz/yXgGuBN7TbOHSM45IkSZrSVh2w3mbAZQBJZgDPAnatqtOT3At8eZzikyRJmpIGbUm7B3hE+3wX4E7gl+38ImDdsQ1LkiRpahu0Je1c4ID2PLR3AidV1eJ22ZY0XZ+SJEkaI4O2pL0X2Aq4CNgU+HDPstcCZ41xXJIkSVPaQC1pVXUJ8Pgk6wOLqqp6Fr8P+Mt4BCdJkjRVDXoJjl2TPLKqbuxL0Kiqi6pq4fiEJ0mSNDUNek7aKcDiJBfQDBj4JXBmVd04bpFJkiRNYYMmaRsCzwWeDTwPeBewSpJLaZO2qvr2+IQoSZI09QzU3dl2c55QVe+tqu1pLrnxSmAhsD/wzXGLUJIkaQoatCWNJGsDOwHPaacdgLuB/2XJNdMkSZI0BgZK0pL8Bng6cD1NQvZdmuulXdQ/kECSJEkP3aDXSdsGuB84G/gVzXXRTNAkSZLGyaBJ2jrAK4BLgFfTJGmLkvw4yQeS/O2gO0xyZZKLklyYZG5bNj3JqUkubx/X66l/UJJ5SS5LstvgL02SJOnha9CBA3dW1c+q6mNVtQvNwIHXAWsDh7LidxzYpaq2qarZ7fyBwJyqmgXMaedJshWwN7A1sDtwWJJpK7gvSZKkh50VGTgwgyWDBp5Dc47aKsDFPPSBA3sAO7fPjwJOBz7Ylh9TVfcAVySZRzNg4eyHuD9JkqROG3TgwKXALGAxcAFwGvAJmgvaLlrBfRZwSpICDq+qI4CZVXUdQFVdl2TDtu7GwK971p3flvXHtz/NpUDYbLPNVjAcSZKk7hm0Je1Y4BfA2VV150Pc57Oq6to2ETu1TQBHkmHKlhms0CZ6RwDMnj3bwQySJOlhb9AbrH9srHZYVde2j9cnOYGm+3JBko3aVrSNaC71AU3L2aY9q28CXDtWsUiSJHXVoKM7SbJlkq+2IzOvaR8PS7LlCmxjrSSPGnoOvAj4PXAisE9bbR/gh+3zE4G9k6yeZAuaLtdzB92fJEnSw9Wg56RtR3Me2t3Aj4EFwEzg/wFvSLJLVZ0/wKZmAickGdr3d6rqpPZiuccl2Q+4CtgToKouTnIczaU/7gcOqKrFK/ICJUmSHo4GPSftszQDBl7ce05akjWBn7TLn7+8jVTVn2hGhfaX3wjsOsI6hwCHDBinJEnSSmHQ7s4dgE/3Dxpo5z8LPHOsA5MkSZrKBk3S7gLWH2HZdJpuUEmSJI2RQZO0/wUOTfLs3sJ2/t+AH411YJIkSVPZoOek/TPNiMszkiykGTiwYTv9Cnjv+IQnSZI0NQ16nbQbgWcn2R3YHtgIuA44p6pOGcf4JEmSpqTlJmlJ1gB+B7yzqk4CThr3qCRJkqa45Z6TVlV3A+sCD4x7NJIkSQIGHzjwbeBN4xmIJEmSlhh04MBVwF5J5tJcvHYBS9/ovKrqq2MdnCRJ0lQ1aJL2ufZxI+AZwywvwCRNkiRpjAw6unPgG7FLkiTpoTP5kiRJ6iCTNEmSpA4ySZMkSeogkzRJkqQOGjFJS7JZktUmMhhJkiQ1RmtJuwLYFiDJz5M8aWJCkiRJ0mhJ2l3Amu3znYFHj3s0kiRJAka/TtoFwL8nObWdf0eS60aoW1X1wbENTZIkaeoaLUn7R+AzwB40dxTYFbhnhLoFmKRJkiSNkRGTtKq6FHg5QJIHgFdW1bkTFZgkSdJUNui9O7cARurqlCRJ0hgb9N6df06yapLXAs8GpgOLgF8C36+q+8cxRkmSpClnoCQtyYbAKcDTgCuBBcCOwAHAb5O8qKoWjleQkiRJU82gdxz4PLA+8Myq2rKqdqyqLYFntuWfH68AJUmSpqJBk7SXAB+sqt/0FrbzBwEvHevAJEmSprJBk7TVgdtGWHYb8IixCUeSJEkweJL2a+CDSdbqLWznP9gulyRJ0hgZ9BIc7wVOA65OcgrNwIENgd2A0Nw2SpIkSWNkoJa0qroQmAUcAcwAXkiTpH0NmFVVvx2vACVJkqaiQVvSqKobgAPHMRZJkiS1Bj0nTZIkSRPIJE2SJKmDTNIkSZI6yCRNkiSpg5abpCVZPcmHkzx9IgKSJEnSAElaVd0DfBhYd9yjkSRJEjD4JTjOAbYDzhiLnSaZBswFrqmqlyWZDhwLbA5cCexVVTe1dQ8C9gMWA++sqpPHIgZJAljr6Xuw/h7/skz5Nf++G2s+6fk86pl/x7S1Z7D4tuu59df/w+2/+c6SStMewUZvOZ7VNtiC2849mptO+tcJjFzSym7Qc9I+ALwtyduTbJlkrSRr9k4ruN93AX/omT8QmFNVs4A57TxJtgL2BrYGdgcOaxM8SRoTd/95Ljd87/3NdMJB1P33svj2G8i0VVlvtw9CFTed8hmYthrTX3wQ0x4986/rrvO8ty41L0ljadAk7RzgccCXgMuBW2lurN47DSTJJsBLga/3FO8BHNU+Pwp4ZU/5MVV1T1VdAcwDdhh0X5K0PItvvoY7Lz6JOy8+ibr/HrLqI7j9whMgzdfj/bct4O4rfs3i22+g7r+Huv9eAFbb8Ak86pl/zy1nfHUyw5e0Ehu0u/MfgBqjfX6RpmXuUT1lM6vqOoCqui7Jhm35xix98/b5bZkkjbm1t9uTemAxt593PItvuZabfvYF1t31XTzmgB9RDyxm0Yn/Hw/ceRMQpr/8YG6fewz3Xvv7yQ5b0kpqoCStqo4ci50leRlwfVWdl2TnQVYZLpxhtrs/sD/AZptt9lBClDRFrbreJqyxxTO5e96ZLL7lWlZZcz0etcPrue8vl3LLGV9jnee9lfVe/CHuvuJc1njcTqy67mNY9KODWW3DWQCsssbarLLmem0SJ0kP3cD37oS/niO2HbAp8I2q+kuSxwMLqmqQLs9nAa9I8hJgDeDRSb4FLEiyUduKthFwfVt/fruvIZsA1/ZvtKqOoLn5O7Nnzx6rFj9JU8ja2+1Jsgq3zT0WgDU234FVHz2Tm887jrv+eBqrzZzFuru8g0ds8nRWXedvmLbW+mz01u/9df21nvZy6v57WfTjgyfpFUha2QyUpCVZG/gG8Brgvna9k4C/AP8KXAW8b3nbqaqDgIPabe4MvK+q/i7JZ4B9gEPbxx+2q5wIfCfJ54HHALOAcwd7aZI0oFVWZa2n78H9N1/L3Zf/EoD7b7oagLWe+jIW37aQtZ7y0qZ80ZXct3Ae914/D4DVZjyOdXc+gLvmnclt5x03OfFLWikNOnDg88BOwK4055L1dkP+hGbk5UNxKPDCJJcDL2znqaqLgeOAS2iSwgOqavFD3JckLWXNJ7+AaWutz+0XfI+hMyruve4SbjrlM2Taakx/yYfJqo9g0U8O4b4Ff+T+G/7EXX84lbv+cCr3/HkuAPcvupr7rrtkEl+FpJVNqpbfO5jkBuBdVfXt9hIY9wGzq+r8JLsAJ1bVo0bfysSYPXt2zZ07d7LDkB42rvrEUyc7BK1kNvvoRZMdgvSwkeS8qpo93LJBW9IeCdw4wrJH0VxoVpIkSWNk0CTtN8AbR1j2GuBXYxOOJEmSYPDRnR8BfpbkZ8B3aU7aeEmS99Akac8dp/gkSZKmpIFa0qrqTJpBA6sDX6YZOPBxYEvgBVX1m3GLUJIkaQoa+DppVXUW8JwkjwTWA26uqjvHLTJJkqQpbNBz0nrdTTO6864xjkWSJEmtgZO0JC9J8iuaJO0vwN1JfpXkpeMWnSRJ0hQ1UJKW5C3Aj4DbgXcBe7aPtwMntsslSZI0RgY9J+1DwBFV9ba+8q8l+RrwYeDwMY1MkiRpChu0u3N94PsjLPseMH1swpEkSRIMnqSdBjxvhGXPA34xNuFIkiQJRunuTLJVz+yXgK8nWR/4AXA9sCHwKuDFwJvHMUZJkqQpZ7Rz0n5Pc2eBIQHe0k7Vzg85CZg25tFJkiRNUaMlabtMWBSSJElayohJWlWdMZGBSJIkaYmBbws1JMmqwCP6y71FlCRJ0tgZ9GK26yQ5LMl1NHccuG2YSZIkSWNk0Ja0I2kutfGfwDzg3vEKSJIkSYMnabsCb6mqo8czGEmSJDUGvZjtVYDnnEmSJE2QQZO0DwAfSbLZeAYjSZKkxkDdnVX1kyQvAOYluRK4eZg6O4xtaJIkSVPXQElaks8C7wZ+gwMHJEmSxt2gAwfeDHy4qv5tPIORJElSY9Bz0u4EzhvPQCRJkrTEoEnavwP7J8lya0qSJOkhG7S7cwPgmcBlSU5n2YEDVVUfHMO4JEmSprRBk7TXAPcDqwEvHGZ5ASZpkiRJY2TQS3BsMd6BSJIkaYlBz0mTJEnSBBr0Omn/tLw6VXXYQw9HkiRJMPg5aV8eZVm1jyZpkiRJY2Sg7s6qWqV/AqYDrwN+C2w1nkFKkiRNNYO2pC2jqm4Gjk2yDnA4sPMYxSRJkjTljcXAgSuA2WOwHUmSJLUeUpKWZCPgvTSJmiRJksbIoKM7F7JkgMCQRwCPAu4GXj3GcUmSJE1pg56T9hWWTdLuBuYDJ1XVjYNsJMkawC+A1dt9H19VH0syHTgW2By4Etirqm5q1zkI2A9YDLyzqk4eMGZJkqSHrUHvOHDwGO3vHuD5VXV7ktWAM5P8lKYlbk5VHZrkQOBA4INJtgL2BrYGHgP8LMkTqmrxGMUjSZLUSRN6x4Fq3N7OrtZOBewBHNWWHwW8sn2+B3BMVd1TVVcA84AdJi5iSZKkyTFiS1qSn6/Adqqqdh2kYpJpwHnA44GvVNU5SWZW1XXthq5LsmFbfWPg1z2rz2/LJEmSVmqjdXcOcp7ZRsBOLHu+2ojarsptkqwLnJDkKaNUz3CbWKZSsj+wP8Bmm202aCiSJEmdNWKSVlV7jrQsyWbAB4GXATcAX1jRHVfVzUlOB3YHFiTZqG1F2wi4vq02H9i0Z7VNgGuH2dYRwBEAs2fPHjhhlCRJ6qoVOictyeOT/BdwOfAK4CDgsVX1bwOuP6NtQSPJI4EXAJcCJwL7tNX2AX7YPj8R2DvJ6km2AGYB565IzJIkSQ9Hg14nbWvgw8CewNXAu4BvVNW9K7i/jYCj2vPSVgGOq6ofJzkbOC7JfsBV7X6oqouTHAdcAtwPHODITkmSNBWMmqQl2Y4mOdsD+CPwZuBbDzZRqqrfAdsOU34jMOzAg6o6BDjkwexPkiTp4Wq00Z0/BV4E/A7Yu6q+O2FRSZIkTXGjtaTt1j5uCnwlyVdG21BVbTjackmSJA1utCTt4xMWhSRJkpYy2iU4TNIkSZImyYTeFkqSJEmDMUmTJEnqIJM0SZKkDjJJkyRJ6iCTNEmSpA4ySZMkSeogkzRJkqQOMkmTJEnqIJM0SZKkDjJJkyRJ6iCTNEmSpA4ySZMkSeogkzRJkqQOMkmTJEnqIJM0SZKkDjJJkyRJ6iCTNEmSpA4ySZMkSeogkzRJkqQOMkmTJEnqIJM0SZKkDjJJkyRJ6iCTNEmSpA4ySZMkSeogkzRJkqQOMkmTJEnqIJM0SZKkDjJJkyRJ6iCTNEmSpA4ySZMkSeogkzRJkqQOMkmTJEnqIJM0SZKkDprQJC3JpklOS/KHJBcneVdbPj3JqUkubx/X61nnoCTzklyWZLeJjFeSJGmyTHRL2v3Ae6vqycDfAgck2Qo4EJhTVbOAOe087bK9ga2B3YHDkkyb4JglSZIm3IQmaVV1XVWd3z6/DfgDsDGwB3BUW+0o4JXt8z2AY6rqnqq6ApgH7DCRMUuSJE2GSTsnLcnmwLbAOcDMqroOmkQO2LCttjFwdc9q89sySZKkldqkJGlJ1ga+B7y7qm4dreowZTXM9vZPMjfJ3IULF45VmJIkSZNmwpO0JKvRJGjfrqrvt8ULkmzULt8IuL4tnw9s2rP6JsC1/dusqiOqanZVzZ4xY8b4BS9JkjRBJnp0Z4D/Av5QVZ/vWXQisE/7fB/ghz3leydZPckWwCzg3ImKV5IkabKsOsH7exbw98BFSS5syz4EHAocl2Q/4CpgT4CqujjJccAlNCNDD6iqxRMcsyRJ0oSb0CStqs5k+PPMAHYdYZ1DgEPGLShJkqQO8o4DkiRJHWSSJkmS1EEmaZIkSR1kkiZJktRBJmmSJEkdZJImSZLUQSZpkiRJHWSSJkmS1EEmaZIkSR1kkiZJktRBJmmSJEkdZJImSZLUQSZpkiRJHWSSJkmS1EEmaZIkSR1kkiZJktRBJmmSJEkdZJImSZLUQSZpkiRJHWSSJkmS1EEmaZIkSR1kkiZJktRBJmmSJEkdZJImSZLUQSZpkiRJHWSSJkmS1EEmaZIkSR1kkiZJktRBJmmSJEkdZJImSZLUQSZpkiRJHWSSJkmS1EEmaZIkSR206mQHIEnSeHvWfzxrskPQSuasd5w17vuwJU2SJKmDTNIkSZI6yCRNkiSpgyY0SUvyjSTXJ/l9T9n0JKcmubx9XK9n2UFJ5iW5LMluExmrJEnSZJrolrQjgd37yg4E5lTVLGBOO0+SrYC9ga3bdQ5LMm3iQpUkSZo8E5qkVdUvgEV9xXsAR7XPjwJe2VN+TFXdU1VXAPOAHSYiTkmSpMnWhXPSZlbVdQDt44Zt+cbA1T315rdlkiRJK70uJGkjyTBlNWzFZP8kc5PMXbhw4TiHJUmSNP66kKQtSLIRQPt4fVs+H9i0p94mwLXDbaCqjqiq2VU1e8aMGeMarCRJ0kToQpJ2IrBP+3wf4Ic95XsnWT3JFsAs4NxJiE+SJGnCTehtoZIcDewMbJBkPvAx4FDguCT7AVcBewJU1cVJjgMuAe4HDqiqxRMZryRJ0mSZ0CStql43wqJdR6h/CHDI+EUkSZLUTV3o7pQkSVIfkzRJkqQOMkmTJEnqIJM0SZKkDjJJkyRJ6iCTNEmSpA6a0EtwSJKkyfHu576bXWftyvQ1p3PWFWfxgR9/YNRygJc++aXss/0+bLDWBlx4zYX865x/5YY7bpislzDl2JImSdIU8bM//mzg8idt+CQO3PVAFt6+kMPOOoxtN9mW9+/8/vEOUT1M0iRJmgK++IsvcuyFxw5cvs3G27BKVuGHv/8hx//ueP54/R/ZaYudePQaj56IcIVJmiRJGsZNd94EwNMe8zQ2W28zNll3E1bJKmz0qI0mObKpwyRNkiQt4+eX/5zfXvtbXvXUV3H03x3NatNWA+DexfdOcmRThwMHJEnSMu574D4O+N4BPG6Dx7H4gcW86znv4mmPeRrX3HLNZIc2ZZikSZI0Bey4+Y5sOX1LADZ81Ia8fKuXc8E1F7DpepsOW37trdfyjme/g8sXXs6TZj6J7TfbnqMvONqWtAlkkiZJ0hTw+m1fzzM2eQYAszaYxYG7Hsghpx7Ci5/84mHLr7nlGrbdeFv2eMoe3H3f3Rz/2+M5/FeHT+ZLmHJM0iRJmgLeccI7hi3/yaU/GXGdfY/Zd5yi0SAcOCBJktRBJmmSJEkdZJImSZLUQSZpkiRJHWSSJkmS1EEmaZIkSR1kkiZJktRBJmmSJEkdZJImSZLUQSZpkiRJHWSSJkmS1EEmaZIkSR1kkiZJktRBJmmSJEkdZJImSZLUQSZpkiRJHWSSJkmS1EEmaZIkSR1kkiZJktRBJmmSJEkdZJImSZLUQSZpkiRJHWSSJkmS1EEPiyQtye5JLksyL8mBkx2PJEnSeOt8kpZkGvAV4MXAVsDrkmw1uVFJkiSNr1UnO4AB7ADMq6o/ASQ5BtgDuGS8d7zd+7853rtYKWy35UyOeNtuHHvWpXz6B+dOdjiddt5n3jjZIUiSHiYeDknaxsDVPfPzgWdOUiwaxnl/WmBCK0nSGHs4JGkZpqyWqpDsD+zfzt6e5LJxj0q9NgBumOwgHg7y2X0mOwQ9eH7OB/Wx4b629TDh53xAeeeYfc4fO9KCh0OSNh/YtGd+E+Da3gpVdQRwxEQGpSWSzK2q2ZMdhzSe/JxrKvBz3i2dHzgA/AaYlWSLJI8A9gZOnOSYJEmSxlXnW9Kq6v4kbwdOBqYB36iqiyc5LEmSpHHV+SQNoKp+AvxksuPQiOxq1lTg51xTgZ/zDklVLb+WJEmSJtTD4Zw0SZKkKWelSNKSVJLP9cy/L8nBy1ln5yQ79cy/NcmYXmk0yeZJfj9C+V1JLkxySZJvJlltLPf9UCS5fYTyI5O8ZgW2c3CSa9rXOTStO2aBPkhJ9k3y5RWof2WSDcYhjm2SvGSst6sVl+QLSd7dM39ykq/3zH8uyT+P9Hfbfp/8eBJC10pgpO/cUeo/6M9bkncnWfPBrNu3nRlJ7kvylr7yFXoto2zf70dWkiQNuAd49Qr+kO4M/DVJq6qvVdVEXpH1/6pqG+CpNJcV2WsC9z2RvlBV2/RMNz/UDba3CntYSDLaeZ/bACv0JbSc7enB+xXt90GSVWiuFbV1z/KdgLOYOn+3Wnm9Gxg2SVvB79Y9gV8DrxuDmIazDSv4/bgyWlmStPtpTnZ8T/+CJC9Pck6SC5L8LMnMJJsDbwXe0/5X/Jy21ed97TrbJPl1kt8lOSHJem356Uk+leTcJH9M8py2fPMkv0xyfjvt1B/HSKpqMXAuzZ0VSLJdkjOSnNf+N7/RcvY9LclnkvymjfctbflhSV7RPj8hyTfa5/sl+Zf2+Q/a/VzcXhC497h9rn0tc5LMGOa4DhvnINqWrO8nOSnJ5Uk+3bPsRUnObvf93SRrt+VXJvlokjOBPZO8JMmlSc5M8qUkP06ySru9Ge06qySZN2jynuSrSea2x+PjfYvf3x77c5M8vq3/2Pb4/K593KwtPzLJ55OcBnwqyQ5JftV+Bn+V5IlpLifzCeC17WfwtUmmt+/J79rP39Pa7R2c5IgkpwDfbD9r2/TEfdZQXT1oZ7Hkn7atgd8DtyVZL8nqwJOBm4Yq9//dSmMhTQvZ6UmOb7/fvp0k7bLdh77zgFf3rPPX3652/vftb9JaSf43yW/bstcmeSfwGOC09vuJJLcn+USSc4CPJDmhZ1svTPL9EcJ9HfBeYJMkS/0dDPf7kdF/V2e3zzdov+uX+X58iIf2YWtlSdKguQn7G5Ks01d+JvC3VbUtcAzwgaq6EvgaS1p5ftm3zjeBD1bV04CLgI/1LFu1qnag+W9kqPx64IVV9QzgtcCXBg06yRo0t7k6KU3XyX8Ar6mq7YBvAIcsZ9/7AbdU1fbA9sA/JtkC+AXwnLbOxjQ3pwd4NjD0ev+h3c9s4J1J1m/L1wLOb1/PGX2vnwHi7PWeLOnqPK2nfBuaY/VUmj/ETdtk6iPAC9p9zwX+uWedu6vq2cAPgMOBF7fzMwCq6gHgW8Ab2vovAH5bVYNePfvD7UUcnwY8ry/xubU99l8GvtiWfRn4Zvs5+TZLv+9PaF/He4FLgee2n8GPAv9aVfe2z49tP4PHAh8HLmi39yGaz+GQ7YA9qur1wNeBfQGSPAFYvap+N+Br1DCq6lrg/jbR3gk4GzgH2JHm7+N3wL1D9Xv/bic+Wq3ktqX5jt8K2BJ4Vvt5+0/g5TTf638zwHZ2B66tqqdX1VOAk6rqSzQXg9+lqnZp660F/L6qnkmTGD05S/4xfxPw3/0bTrIp8DdVdS5wHM13+ZCRfj9G+11dygjfj1PSSpOkVdWtNB+Cd/Yt2gQ4OclFwPtZugtjGW2St25VndEWHQU8t6fK0H8V5wGbt89XA/6z3cd3WZIQjeZxSS4EbgSuan9knwg8BTi1XfaRNv7R9v0i4I1t/XOA9YFZNInYc5JsRXMz+gVpWrt2pOnagSYx+y1Nk/Wm7XoADwBDfxTfoknsei0vzl693Z279JTPqapbquruNr7HAn9Lc+zOare7D0vfLmMopicBf6qqK9r5o3vqfAMYOrfwHxjmC2YUeyU5H7iA5nPS+z4e3fO4Y/t8R+A77fP/Yenj9N22tQVgHeC7ac5P/AIjfwaf3W6Hqvo5sH7PPx0nVtVdQ9sGXtYmy/8AHLkCr1EjG2pNG0rSzu6ZH/qbGe7vVhpL51bV/PafzgtpvuufBFxRVZdXc0mGbw2wnYuAF6TpgXlOVd0yQr3FwPcA2m3/D/B3ac4f3hH46TDr7E2TnEHT+NHb5bnM78cAv6sawcp2fssXgfNZ+of5P4DPV9WJSXYGDn6I+7infVzMkuP3HmAB8HSaxPfuAbbzf1W1TZs4nZ6ma/IK4OKq2nGEdYbbd4B3VNXJ/ZXb5uTdaVrVptOcP3N7Vd3WHosXADtW1Z1JTgfWGGG//ddpyXLiHMQ9Pc+HXk+AU6tqpHMc7ujZ/7Cq6uokC5I8n6al4w0j1e3Vtj6+D9i+qm5KciRLH48a4TkjlN/R8/yTwGlV9ao0Xe2njxTGKNv86/ba9+tUYA+a99RbuIyNofPSnkrT3Xk1TXfOrTTJPwzzd1tV3gFFY2m470YY+XvnfpZucFkDoKr+mGQ7mvO6/i3JKVX1iWHWv7vnH0pofj9/RPM79t2qun+YdV4HzEwy9P36mCSzquryYeou7zpfvfGP9Bs0Za00LWkAVbWIJrvfr6d4HeCa9nnv3a1vAx41zDZuAW5Ke84X8Pc0TbajWQe4rv3P5+9p7owwaMzXAQcCBwGXATOS7AhNt2KSUVv+aO7E8LYsGWX2hCRrtcvOpmk2/wVNy9r7WNLVuQ5wU/uD/ySaVqwhqwBDozhfT9Nl3OvBxDmIX9M07Q+d87Vm253X71JgyzbhgaWb2qHpDvwWcFzfl89oHk2TCN2SZCbw4r7lr+15PLt9/iua/yihSQb7j9OQ3s/gvj3l/Z/BX7TboU2ib2hbiIfzdZru1d+0n3s9dGcBLwMWVdXi9riuS9OacHZvxb6/W2m8XQpskeRx7XzvP7JXAs8ASPIMYIv2+WOAO6vqW8Bnh+owwm/fkLbr/1qaHpIj+5cneSKwVlVtXFWbV9XmwL+x5Ltwmd+P5fyuXklzOgc96y03zqlipUrSWp+jGZk15GCarqZfAr3nJv0IeFV7rtRzWNo+wGeS/I7m3Knh/vvodRiwT5Jf05yLdMdy6vf7Ac1om2fSfEg/1XZDXkjPCNQRfJ2mu/D8tjvtcJb85/VLmvPY5tG0ME5nSZJ2ErBq+xo/SZMgDbkD2DrJecDz6Xv97fkCg8bZe07ahT2J1TKqaiFNEnN0G9evaZr5++vdBfwTzXl8Z9K0YvY25Z8IrM3oXZ37Jpk/NNF0X10AXEzTanJWX/3V2xNr38WSASrvBN7Uxvr37bLhfJrmP9mzWDqBPw3YqufE2IOB2e32DmXpfyr6j8F5NC08K9Kdq9FdRPPd8eu+sltGOK/xB8Caw3x/SGOqPS1kf+B/2++8P/cs/h4wve2Gfxvwx7b8qcC5bfmHgX9py48Aftp3jnC/bwNXV9Ulwyx7HXBCX9n3WJI4jvT7MdLv6mdpGhp+xdK/3f3fj1OSdxzQw1KStavq9iShGTRyeVV9oV02m+ZcuJX2x7P9L/l04EltC64kjYk015G8oKr+a7JjmepWxpY0TQ3/2P6HeDFNd+LhAEkOpPmvbqXthkpz0eVzaEajmqBJGjNtC9jTGGxwgsaZLWmSJEkdZEuaJElSB5mkSZIkdZBJmiRJUgeZpElaSpJXJjklyY1J7k1yTZJjkjxrHPZ1cJJBb9s11vt+UZJ3D1N+ZJK5kxCSJC3FJE3SXyX5As3o2GuAN9PcleJAmotKntlzMc2VwYtoLvbc75MsfdFhSZoUK9ttoSQ9SEn2oEla3lRVR/Yt/p8kLwfu6l9vZVNV/zfZMUyGJGu0F02V1BG2pEka8m6a20wdOdzCqvpRe8sYkqyS5MAk85Lck+SPSZa5Q0KStye5vK0zL8l7lt3yMutskeQHSW5NcluSHw3dKqynzrQkB7X7vae9c8SRPctfmuTUJNe32/l1khf1LD+Y5r6cj01S7XRku2yZ7s4k2ySZk+TOJDcl+XZ7+7Ch5Zu329gryeFJbmlj+niSgb9nk+yX5OIkdyW5IckZ6bnlWpINkhzVdkXfmeT09uLNvduoJG/vK1uqWznJvm29Hdpt3AW8v132tPaY35zk9iTnJnlhz7rT29e4IMndSX6V5JmDvkZJg7MlTRJJVqW5R+VnB1zlP2hu8/IJmluOvRD4RpIbq+rH7Tb/sa33eZp7zO4CfC7J6lV16AhxrA7MAe4D/pHm5ssfB85I8tSe+5QeDryR5pZbZ9Dc8qz3vn9b0Nz67bPAAzT3Yv1pkudW1Vk0t1ObRXPbmle16ywcIaYZNHd3+APNvQjXprlt16lJZre3SRvyaZru4tcAuwIfpbng8nHDHsWl9/Nc4GvtOmfT3E92R5qLNQ/5AfB4mvvw3kCTWJ2WZNv29m8r6mjgqzTH+OY09/E9i+b+vG+luV3abGDTNsbVgZ/R3NP0/cD1NLci+lmaG2z/5UHEIGkkVeXk5DTFJ2AmUMBb+spD88/c0BSaJOEBYJ++ut+kaYmDppX+GuC/++ocRnOf1TXa+YNpbiQ/tPytNInZlj1lmwD3Age1809qY33ngK9tlTb2k4Fv9JR/FrhymPpHAnN75g8FbgYe3VO2QxvD69r5zdv5b/Zt60LgmAHjfB9w3ijLd2/38byesrVoksvDe8oKeHvfuv3Hed+23rv66h0NzAceOUIM+7XvxayeslWB/wM+M9mfYyenlW2yu1MSNMkXND/cvd5L06o1NB1A00L0AHBCklWHJpoWsG2STKNJrB4DfLdve8fStBA9dYQ4dgDOr6o/DRVU1Xya1p1nt0W7tI9Hjvhikk3absFraJK++2gGCjxhpHVGsQNwSlXd2hPTucCVPTENOaVv/hKaYzGIC4Ftk3whyXOTPGKYOBZW1Rk9cdwB/HiYOAb1v33zzweOraqRzj18AXAecEXP+w5Na+bsEdaR9CDZ3SkJmq6ze1g2ofgfmq4+gN+0jxsA02haxIazUTsBLOhbNjQ/fZR1+9cZWu+x7fP1gTt6k6Ze7TlgJ9KMSP0oMA+4g6ZrdsMR9juajWi6LIeLqf913Nw3fy+wxiA7qaqfJXkT8E7gXcDtSb4FvL9NxkY7NiMdz+Xp3976wHWj1N8A+FuapLfflBxwIY0nkzRJVNX9Sc6maW36aE/5Atof8mSosY1FNK1Tz6JpUet3PUsGJfUnRUMn2y9ieNcBWw9TPrNnnRuBtZI8eoRE7fHAtsCLq+qkocIkjxxhn8tzHcMndzNpWpXGTFUdBRzVngf3auALwK00l0EZLY7e43kP0N8KN1IS199yeiNLEuzhLALm0pyH1u+eUdaT9CDY3SlpyBeBZyb5++XU+zlNS9o6VTV3mOlemvOargX27Ft3L5qk46IRtn0OsF2SLYYKkmwM7ASc2bN/aAYODGcoGftr0pDksTRJZa9BW7nOAXZL8qie7W1Pcx7amSOt9FBU1cKqOhz4JbBVTxwbtgMMhuJYE3hpXxzzgSf31FmFphtzEHOAvZKMdFzm0CTBVw3zvo/0nkp6kGxJkwRAVf0wyReBI5PsQjM68gaaLrChSzDcXlWXJfkacEyST9O0rKxB0wL2hKp6c1U90F7m4vAkNwKnAs+jaYH5UI18Pa4jgQ/SjMT8KLCY9qR3mhGdtPs/gmak6IbAL2hGG76mqvYGLqVJVD6X5P+j6fb8OM1Ahl6XAjOT7Av8nubE+iuHienzbdwnJ/kUS0Z3XkQzknNMJPk4TYvX6TSvd1uaY3YgQFWdnOQs4NgkB9K0er2PJin9TM+mTgAOSHIB8CeaixI/esAwPk7Trf2LJJ9r97EtcGNVfYNmcMhbgdOTfLbd/vo058v9paq+8OBevaRhTfbIBScnp25NNJekOJWma+s+mhax79F0Hw7VCc111S6mabFaSHPy+Bv7tvV2mnPC7qX5QX9P3/KD6Rl12JZtSXOpiduA22lOjJ/VV2ca8KF2m0Mtd//ds3x74Fyai+9eTjOa8UiWHrW5BvDfNN2zBRzZli9Vry3blqYF706a886+A8zsWb55u42X9a23zLZGOe4vo2mpWgjcTXMZjAOB9NSZQZMo3dS+tjOA7fu2szZwVPv+/QX4SP9xZsnozrWHieNpwE/a438bTQverj3L1wH+Hbi659h/H3jWZH92nZxWtilV/ackSJIkabJ5TpokSVIHeU6aJE2AnmuKDaeqavGEBSPpYcGWNEmaGPeNMs2ZxLgkdZQtaZI0MbYfZdltExaFpIcNBw5IkiR1kN2dkiRJHWSSJkmS1EEmaZIkSR1kkiZJktRBJmmSJEkd9P8Dm8uPyx3uFSIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,7))\n",
    "values = df['geolocation_source'].value_counts()\n",
    "index=0\n",
    "ax=sns.countplot('geolocation_source', data=df)\n",
    "\n",
    "for i in ax.get_xticklabels():\n",
    "        ax.text(index, values[i.get_text()]/2, values[i.get_text()], \n",
    "                horizontalalignment=\"center\", fontweight='bold', color='w')\n",
    "        index += 1\n",
    "        \n",
    "plt.title('Count of Geolocation_source', fontsize=15)\n",
    "plt.ylabel(\"Number of rows\",fontsize=15)\n",
    "plt.xlabel(\"Geolocation_source\",fontsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fec6e07",
   "metadata": {},
   "source": [
    "In above count plot we have seen that the highest no. of value covered by WRI and then Industry About and negligible value present in National Renewable Energy Laboratory "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "713f629c",
   "metadata": {},
   "source": [
    "# Box plot for checking outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "5f0cb136",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(ncols=4, nrows=1, figsize=(15,5))\n",
    "index = 0\n",
    "ax = ax.flatten()\n",
    "for col, value in df[numerical].items():\n",
    "    sns.boxplot(y=col, data=df, ax=ax[index])\n",
    "    index += 1\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17f30d61",
   "metadata": {},
   "source": [
    "Outlies present in tree columns\n",
    "\n",
    "1.capacity_mw\n",
    "2. longitude\n",
    "3.powerplant_age\n",
    "\n",
    "we have to remove outliers using z score or IQR method"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d98fc3a3",
   "metadata": {},
   "source": [
    "# Distribution plot for all numerical columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "aad8dc7a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(ncols=4, nrows=1, figsize=(15,5))\n",
    "index = 0\n",
    "ax = ax.flatten()\n",
    "for col, value in df[numerical].items():\n",
    "    sns.distplot(value, ax=ax[index], hist=False, color=\"b\",kde_kws={\"shade\": True})\n",
    "    index += 1\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f5a75eb",
   "metadata": {},
   "source": [
    "Skewness present in Capacity_mw and PowerPlant_age we have remove it later "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30057d59",
   "metadata": {},
   "source": [
    "# Bivariate Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5ddbfd1",
   "metadata": {},
   "source": [
    "# Scatter plot for capacity_mw with another variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "d310b3a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='PowerPlant_age', ylabel='capacity_mw'>"
      ]
     },
     "execution_count": 38,
     "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": [
    "sns.scatterplot(x = \"PowerPlant_age\", y = \"capacity_mw\", data = df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e498fbb8",
   "metadata": {},
   "source": [
    "\n",
    "The the electricity generating capacity measured in mega watts of older powerplant is lower as compared to recent year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "4430c3fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='latitude', ylabel='capacity_mw'>"
      ]
     },
     "execution_count": 39,
     "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": [
    "sns.scatterplot(x = \"latitude\", y = \"capacity_mw\", data = df)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95763cd4",
   "metadata": {},
   "source": [
    "The latitude geolocation for electricty generation is highest measuring the mega watts between 20 to 25 decimal degrees."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "b975eef6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='longitude', ylabel='capacity_mw'>"
      ]
     },
     "execution_count": 40,
     "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": [
    "sns.scatterplot(x = \"longitude\", y = \"capacity_mw\", data = df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f8a3f6a",
   "metadata": {},
   "source": [
    "The longitude geolocation for electricty generation is highest measuring the mega watts around 70 and then again between 80-85"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "27a962d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['capacity_mw', 'latitude', 'longitude', 'primary_fuel', 'source',\n",
       "       'geolocation_source', 'PowerPlant_age'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e23c90b1",
   "metadata": {},
   "source": [
    "# BAR PLOT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "8e321279",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BbwO/MoKaJEmS1BvmpzvuTLIEOALYCHymPywqSZKkERnmatDfAi4GXgi8CLgoyStGVZgkSZKGO2ftD4FDp3vTkjwW+ArgYO6SJEkjMsw5a+uAOwam7wBumttyJEmSNGiYnrX1wFeTnA0U3QUHFyf5fYCqescI6pMkSdquDRPWvt3/m3Z2f/vIuStHkiRJg4a5GvQtoyxEkiRJD7bVYS3JYmAlcCD9QO4AVXXkCOqSJEkSw11gcAZwLbAf8Ba6gdwvGUFNkiRJ6g0T1h5bVR+gGyP0/Kp6Bd3A7pIkSRqRYS4wuKe/vSXJc4GbgaVzX5IkSZKmDRPW/jzJo4A/AN4L7E43TqgkSZJGZJjDoMcCqaorq+pZwC8BLxhNWZIkSYLhwtohVXX79ERVbQQOnfOKJEmS9GPDhLUdkjxmeiLJHgx3GFWSJElDGiZsvR34SpKP0Q039avAqpFUJUmSJGC4EQxOT7IGOBII8MKqunpklUmSJGm4w5h9ODOgSZIkjckw56zNqSSPTvKxJNcmuSbJ05LskeTcJN/qbwfPkTspyXVJvpHk2ZOqW5IkaZwmFtaAdwOfrqonAU8GrgFeD5xXVfsD5/XTJDkAOI5uXNKjgZOTLJpI1ZIkSWM0kbCWZHfgmcAHAKrqR/3PgqwATusXOw04pr+/Ajizqu6uquuB64AjxlmzJEnSJEyqZ+2ngA3Ah5JcluT9SR4B7FVVtwD0t4/rl98HuGlg/XV924MkOSHJmiRrNmzYMLpnIEmSNAaTCms7AocB76uqQ4E76Q95bkZmaavZFqyqU6pqeVUtX7x48U9eqSRJ0gRNKqytA9ZV1Vf76Y/RhbfvJdkboL+9dWD5fQfWX0o3kLwkSdKCNpGwVlVTwE1Jntg3HUX3kyDnAMf3bccDZ/f3zwGOS7Jzkv2A/YGLx1iyJEnSRExyuKjfBc5IshPwHeDldOHxrCSvBG6kGzyeqroqyVl0gW4TcGJV3TuZsiVJksZnYmGtqi4Hls8y66jNLL8Kh7eSJEnbmUn+zpokSZK2wLAmSZLUMMOaJElSwwxrkiRJDTOsSZIkNcywJkmS1DDDmiRJUsMMa5IkSQ2b5AgGkiRJE7Pzzjs/4LZVhjVJkrRdOvjggyddwlbxMKgkSVLDDGuSJEkNM6xJkiQ1zLAmSZLUMMOaJElSwwxrkiRJDTOsSZIkNcywJkmS1DDDmiRJUsMMa5IkSQ0zrEmSJDXMsCZJktQww5okSVLDDGuSJEkNM6xJkiQ1zLAmSZLUMMOaJElSwwxrkiRJDTOsSZIkNWzHSRcgSVuycuVKpqamWLJkCatXr550OZI0VoY1Sc2bmppi/fr1ky5DkibCw6CSJEkNM6xJkiQ1zLAmSZLUMMOaJElSwwxrkiRJDTOsSZIkNcywJkmS1DDDmiRJUsMMa5IkSQ0zrEmSJDVsomEtyaIklyX5p356jyTnJvlWf/uYgWVPSnJdkm8kefbkqpYkSRqfSfes/Q/gmoHp1wPnVdX+wHn9NEkOAI4DDgSOBk5OsmjMtUqSJI3dxMJakqXAc4H3DzSvAE7r758GHDPQfmZV3V1V1wPXAUeMqVRJkqSJmWTP2ruAlcB9A217VdUtAP3t4/r2fYCbBpZb17dJkiQtaBMJa0meB9xaVZdu7SqztNVmHvuEJGuSrNmwYcM21yhJktSCSfWsPR14fpIbgDOBI5P8HfC9JHsD9Le39suvA/YdWH8pcPNsD1xVp1TV8qpavnjx4lHVL0mSNBYTCWtVdVJVLa2qZXQXDnyuql4CnAMc3y92PHB2f/8c4LgkOyfZD9gfuHjMZUuSJI3djpMuYIa3AmcleSVwI3AsQFVdleQs4GpgE3BiVd07uTIlSZLGY+Jhraq+AHyhv/9vwFGbWW4VsGpshUmSJDVg0r+zJkmSpIdgWJMkSWqYYU2SJKlhhjVJkqSGTfwCA0nz0/nP/PmxbeuuHRdBwl3r1o1tuz9/wflj2Y4kbYk9a5IkSQ0zrEmSJDXMsCZJktQww5okSVLDDGuSJEkNM6xJkiQ1zLAmSZLUMMOaJElSwwxrkiRJDTOsSZIkNcywJkmS1DDDmiRJUsMMa5IkSQ0zrEmSJDXMsCZJktQww5okSVLDDGuSJEkNM6xJkiQ1zLAmSZLUMMOaJElSw3acdAGStCWPrnrArSRtTwxrkpr3knvvm3QJkjQxHgaVJElqmGFNkiSpYYY1SZKkhhnWJEmSGmZYkyRJaphhTZIkqWGGNUmSpIYZ1iRJkhpmWJMkSWqYYU2SJKlhhjVJkqSGGdYkSZIa5kDukjQPrVy5kqmpKZYsWcLq1asnXY6kETKsSdI8NDU1xfr16yddhqQx8DCoJElSwyYS1pLsm+TzSa5JclWS/9G375Hk3CTf6m8fM7DOSUmuS/KNJM+eRN2SJEnjNqmetU3AH1TVzwJPBU5McgDweuC8qtofOK+fpp93HHAgcDRwcpJFE6lckiRpjCYS1qrqlqr6Wn//DuAaYB9gBXBav9hpwDH9/RXAmVV1d1VdD1wHHDHWoiVJkiZg4uesJVkGHAp8Fdirqm6BLtABj+sX2we4aWC1dX3bbI93QpI1SdZs2LBhZHVLkiSNw0TDWpLdgI8Dv1dV33+oRWdpq9kWrKpTqmp5VS1fvHjxXJQpSZI0MRMLa0keRhfUzqiqT/TN30uydz9/b+DWvn0dsO/A6kuBm8dVqyRJ0qRM6mrQAB8ArqmqdwzMOgc4vr9/PHD2QPtxSXZOsh+wP3DxuOqVJEmalEn9KO7Tgd8E1ia5vG/7Y+CtwFlJXgncCBwLUFVXJTkLuJruStITq+resVctSVvwl3/wybFs5/bb7vzx7bi2+eq3/8pYtiPpgSYS1qrqS8x+HhrAUZtZZxWwamRFSZIkNWjiV4NKkiRp8wxrkiRJDTOsSZIkNcywJkmS1DDDmiRJUsMMa5IkSQ0zrEmSJDVsUj+KK0n6CTxip90fcCtp4TKsSdI89PSffuGkS5A0JoY1SZIWuJUrVzI1NcWSJUtYvXr1pMvRkAxrkiQtcFNTU6xfv37SZWgbeYGBJElSwwxrkiRJDTOsSZIkNcywJkmS1DDDmiRJUsMMa5IkSQ0zrEmSJDXMsCZJktQww5okSVLDDGuSJEkNM6xJkiQ1zLFBJUmagGtWfW5s2/rRxrt+fDuu7f7sG44cy3a2B/asSZIkNcywJkmS1DDDmiRJUsMMa5IkSQ0zrEmSJDXMsCZJktQww5okSVLDDGuSJEkNM6xJkiQ1zBEMJEla4B67y6MecKv5xbAmSdIC9+pDf33SJegnYFiTGrJy5UqmpqZYsmQJq1evnnQ5kqQGGNZGzA9fDWNqaor169dPugxJUkMMayPmh+/89/T3Pn1s29rp9p3YgR246fabxrbdL//ul8eyHUnStvFqUEmSpIbZsyZJWjA89UQL0XYb1g7/w9PHsp1H3nYHi4Abb7tjbNu89G0vHct2NPdq1+I+7qN2rUmXIs1LnnqihWi7DWtSi+55+j2TLkGac6te8qKxbWvjrf/R3U7dMrbtvuHvPjaW7Wj7ZViTJC0Yuyza4QG30kIwr8JakqOBdwOLgPdX1VsnXJIkqSGHPvaRky5BmnPz5qtHkkXAXwHPAQ4AXpzkgMlWtWX37fQI7t15d+7b6RGTLkWSJM1D86ln7Qjguqr6DkCSM4EVwNUTrWoL7tz/lyddgiRJmsdSNT+uOkvyIuDoqvqtfvo3gf9aVa+esdwJwAn95BOBb4y10NntCdw26SIa5H6Znftldu6XB3OfzM79Mjv3y+xa2i9PqKrFMxvnU89aZml7UNKsqlOAU0ZfztZLsqaqlk+6jta4X2bnfpmd++XB3Cezc7/Mzv0yu/mwX+bNOWvAOmDfgemlwM0TqkWSJGks5lNYuwTYP8l+SXYCjgPOmXBNkiRJIzVvDoNW1aYkrwY+Q/fTHR+sqqsmXNbWauqwbEPcL7Nzv8zO/fJg7pPZuV9m536ZXfP7Zd5cYCBJkrQ9mk+HQSVJkrY7hjVJkqSGGda2UZI3JLkqyRVJLk/yXx9i2S8kafqy4LmWZEmSM5N8O8nVSf4lyc9sw+P85yjqG6ck70zyewPTn0ny/oHptyd5Y5LXD/m4p/a/P7ggJNkryd8n+U6SS5NcmOQFk65rXGa+1pO8LMlfDrH+siRXzn1lo5fk3v599OtJvpbk5/r2/yOJo6RvgyRLk5yd5Fv9+/C7k+yUZHmS9/TLDPUaa0mSSvL2genXJXnzNj5W8++lhrVtkORpwPOAw6rqEOAXgZvm8PEXzdVjTUKSAP8AfKGqfrqqDgD+GNhrspVNzFeA6Q+fHeh+gPHAgfk/B3xmex7rtn/N/CNwQVX9VFUdTnfF99KJFrYAJJkPF5LdVVVPqaonAycB/x9AVd1cVU1/iLao/3v6BPCPVbU/8DPAbsCqqlpTVa+ZaIFz427ghUn2nHQh00b5t2ZY2zZ7A7dV1d0AVXVbVd2c5KgklyVZm+SDSXaeuWKS9yVZ0/fKvWWg/Ya+d+VLwLHjeyoj8Szgnqr66+mGqroc+FKStyW5st9HvwaQZLck5/XfqNcmWTGhukfly/RhjS6kXQnckeQx/WvkZ4EnT3/D7b/lvSfJV/pephf17Unyl31P5T8Dj5vAcxmVI4EfzXjNfLeq3tv3GH2xf30M9rrsneSCvkfmyiT/bWLVj1CSRya5PsnD+und+/eLhyU5vO+NuhA4cWCdlyX5aJJPAp9NskeSf0x3JOCiJIdM6vlshd2Bf4cH9hYm2SXJh/r3iMuSPKtvf1n/3D7Z76dXJ/n9fpmLkuzRL/fbSS7p99fHk+zatx/bv36+nuSCvu3AJBf3r60rkuw/kT2x7Y4EflhVHwKoqnuB1wKvSPLfk/zTRKubG5voruJ87cwZM3vKMtBrnWRl/xr6epIHfUHu/6bOT9e7/5kke/ftm3v9nJrkHUk+D/zPETxPYB79dEdjPgu8Mck3gX8FPgJ8FTgVOKqqvpnkdOBVwLtmrPuGqtqYrvfsvCSHVNUV/bwfVtUzxvIMRusg4NJZ2l8IPAV4Ml3v0iX9m+MG4AVV9f1035IuSnJOLZBLlfsgvynJ4+lC24XAPsDTgP8ArgB+NGO1vYFnAE+i+z3BjwEvoBtC7WC6XsqrgQ+O4zmMwYHA1zYz71bgl6rqh/2H5oeB5cCv0/VIrur/nnYdT6kj8/Aklw9M7wGcU1V3JPkC8Fy63sfjgI9X1T1JPgT8blWdn+RtMx7vacAh/fvNe4HLquqYJEcCp9P9LbZi+rnvQvfaP3KWZU4EqKqDkzyJLoROn1pxEHBov/51wB9V1aFJ3gm8lO59+BNV9TcASf4ceCXwXuCNwLOran2SR/eP9zvAu6vqjHS/6znfjnYcyIz34P799Ubg/5xMSSPxV8AVSVZvzcJJngMcQzdU5Q+mg/zA/IfRvSZWVNWGdB0Kq4BXsPnXD3Q9l7/Yh+KRsGdtG1TVfwKH041BuoEurP3fwPVV9c1+sdOAZ86y+q8m+RpwGd0f1AED8z4ysqLb8Azgw1V1b1V9Dzgf+C90Q4n9RZIr6MLvPiy8Q6bTvWvTYe3CgemvzLL8P1bVfVV1Nffvi2dy//67Gfjc6MuejCR/1X+DvQR4GPA3SdYCH+X+v5lLgJenO0/l4Kq6YzLVzpnpQ4FPqaqn0IWIae8HXt7ffznwoSSPAh5dVef37X874/HOraqN/f1nTM+vqs8Bj+3Xb8X0c38ScDRwepKZQwwOPodrge/SfUgCfL6q7qiqDXRfgD7Zt68FlvX3D0rXQ7sW+A3uPxXhy8CpSX6b+0PZhcAfJ/kjurEa75rD5zoOYZbhGB+ifV6qqu/TffHY2sO6vwh8qKp+0K+/ccb8J9IF/3P7Lw9/wv2nYmzu9QPw0VEGNTCsbbP+A/MLVfUm4NV0L4KHlGQ/4HV0vW+HAP9M901w2p0jKXb8rqILszPNNr4rdC/8xcDh/YfU93jgflkIps9bO5juMOhFdD0fP0f3YTHT3QP3B/fbgnmjneEq4LDpiao6ETiK7nXxWrrXxJPpetR26pe5gC7Argf+NslLx1zz2FTVl4FlSX4eWFRVV7LlD97B95OtGlu5BVV1IV3P+8zBrDf3/gEP/Hu5b2D6Pu4/gnQq8OqqOhh4C/17TFX9Dt2H8r7A5UkeW1V/DzwfuAv4TN8bOZ9cRfe38mNJdqd7jt+eSEWj8y66Xq5HDLRtos83fejfqW/f0t9MgKsGvjQdXFW/3M87lVleP72Rf3Yb1rZBkifOOIfhKXQfJsuSTHcx/yZdz9Gg3en+U/8jyV7Ac0Zd64R8Dti5/6YKQJL/Qnceyq8lWZRkMd0H7cXAo4Bb+8M6zwKeMImiR+zLdBelbOyD/kbg0XSB7cKtfIwLgOP6/bc33bmBC8XngF2SvGqgbfqw5qOAW6rqPrq/q0UASZ5A97r5G+ADDIS9Bep0ukPA0+ch3U73XjJ96sRvPMS6F0zPT/ILdOfcfn9Uhf4k+kOci4B/mzFr8Dn8DPB44BtDPPQjgVv6Q10/3ldJfrqqvlpVbwRuA/ZN8lPAd6rqPXSnIbR8jt9szgN2nf4C058m8Ha6wPGDCdY15/r30rPoAtu0G7i/w2AFXe88dKcwvWLgfLMHHAalez0tTncRIenOC53uQZv19TMunrO2bXYD3tuf37CJ7hyJE+jeSD+a7oqQS4C/Hlypqr6e5DK6bz3fYfYelXmvqirdTy68K93PUfyQ7o/n9+j23dfpvt2srKqpJGcAn0yyBrgcuHYSdY/YWrregr+f0bZbVd324CM+s/oHunN51gLf5MFfBuat/jVzDPDOJCvpTi+4E/gjunPZPp7kWODz3P8t9heAP0xyD/CfdOcmLWRnAH9O9z4z7eXAB5P8gG4ovs15M92h0yvoPqyPH1WR22jwfL0Ax1fVvTP+Lk4G/ro/DLUJeFlV3b2VfzsAf0p3bvF36f6GHtm3v63/8h26kPN14PXAS/rX1hTwZ9v6xCZh4D345CR/Stcx8y90V+U/baLFjcbb6Y5wTfsb4OwkF9P9n94JUFWfTvIUYE2SH3H/PqGf/6N0Fya8pz9NYEe6nrur2PzrZywcbkqS5oH+Q2RFVf3mpGuRNF72rElS4/qrOZ8D/PdJ1yJp/OxZkyRJapgXGEiSJDXMsCZJktQww5okSVLDDGuSJEkNM6xJWtCS/FmSLY4wMoY6XpPkmv53Bbdl/Rv6sXMlbWe8GlTSgpVk0SjG7OuHsEk/qsLWrnMt8Jyqun4bt3kDsLyqbtuW9SXNX/asSZqXkixLcm2S05JckeRjSXbte6DemORLwLFJTu1/UHa6d+ovklyYZE2Sw5J8Jsm3k/xOv8xuSc5L8rUka5OsGNjeNUlOphtV4U+TvHOgnt9O8o7N1PrXwE8B5yR5bZI3J3ndwPwrkyzr778kycVJLk/yv/uhgiRtxwxrkuazJwKnVNUhwPeB/6dv/2FVPaOqzpxlnZuq6mnAF+nGSnwR8FTuH1Loh8ALquowuvFX3577xzR6InB6VR0K/C/g+f1YgdAN/fSh2YrsBwu/GXhWVb1ztmUAkvws8GvA06vqKcC9TGAcQkltcQQDSfPZTVU1Pcbu3wGv6e9/5CHWOae/nR6b9Q7gjiQ/7Mf7vRP4iyTPBO4D9gH26tf5blVdBFBVdyb5HPC8JNcAD6uqtT/h8zmKbgDqS/p8+HDg1p/wMSXNc4Y1SfPZzJNup6fvnLnggLv72/sG7k9P70jXk7UYOLyq7unPFdtlM4/7frqBoK9lM71qm7GJBx7ZmH78AKdV1UlDPJakBc7DoJLms8cneVp//8XAl+bgMR8F3NoHtWcBT9jcglX1VWBf4NeBDw+xjRuAwwCSHAbs17efB7woyeP6eXsk2ez2JW0fDGuS5rNrgOOTXAHsAbxvDh7zDGB5kjV0vWzXbmH5s4AvV9W/D7GNjwN7JLkceBXwTYCquhr4E+Cz/XM6F9h7uPIlLTT+dIekeam/evKfquqgCdfxT8A7q+q8SdYhaeGyZ02StkGSRyf5JnCXQU3SKNmzJklzJMlj6c47m+moqvq3cdcjaWEwrEmSJDXMw6CSJEkNM6xJkiQ1zLAmSZLUMMOaJElSw/5/af7JLHskt8cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,7))\n",
    "sns.barplot(df['primary_fuel'],df['capacity_mw']); "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea3c9258",
   "metadata": {},
   "source": [
    "For the fluel type Nuclear and Coal the capacity of power plant is maximum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "04042159",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,7))\n",
    "sns.barplot(df['geolocation_source'],df['capacity_mw']);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ef4c6d3",
   "metadata": {},
   "source": [
    "The capacity of power plant is maximum for WRI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "dfb53c97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,7))\n",
    "sns.barplot(df['primary_fuel'],df['latitude']);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40fffafe",
   "metadata": {},
   "source": [
    "The longitude geolocation for electricty generation is maximum  for fluel type  Solar and Hydra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "a88166ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,7))\n",
    "sns.barplot(df['primary_fuel'],df['PowerPlant_age']);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33ffae8b",
   "metadata": {},
   "source": [
    "1. The fluel type Hydre and Nuclear are used in older power Plant .\n",
    "\n",
    "2. Recently The PowerPlant is highrly used Solar ,Coal and Biomass as a fluel type."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86249304",
   "metadata": {},
   "source": [
    "# Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "3efd0ac0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr(),annot=True, linewidth=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "746edc9b",
   "metadata": {},
   "source": [
    "Observation:\n",
    "    \n",
    "1. Capacity of powerplant is negatively correlated to age of power plant.\n",
    "\n",
    "2. langitude and longitude is also correlated is also negative correlated with capacity of powerplant.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "0bdd489c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Correlation with sales'}>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Correlation with capacity_mw with respect to other columns\n",
    "df.corrwith(df.capacity_mw).plot.bar(\n",
    "        figsize = (10, 5), title = \"Correlation with sales\", fontsize = 15,\n",
    "        rot = 45, grid = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adf69d5e",
   "metadata": {},
   "source": [
    "Langitude ,longitude ,powerplant_age is very low positive correlated with capacity of powerplant capacity. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93fc9250",
   "metadata": {},
   "source": [
    "# Lable Encoding\n",
    "\n",
    "Dealing with categorical variable\n",
    "\n",
    "change categorical value into numerical value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "d9ce0735",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "le=LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "c8de570f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#for changing all catigorical value into numeric value\n",
    "\n",
    "df=df.apply(le.fit_transform)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "6af64c40",
   "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>capacity_mw</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>primary_fuel</th>\n",
       "      <th>source</th>\n",
       "      <th>geolocation_source</th>\n",
       "      <th>PowerPlant_age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9</td>\n",
       "      <td>731</td>\n",
       "      <td>143</td>\n",
       "      <td>6</td>\n",
       "      <td>109</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>177</td>\n",
       "      <td>594</td>\n",
       "      <td>234</td>\n",
       "      <td>1</td>\n",
       "      <td>174</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>121</td>\n",
       "      <td>420</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>200</td>\n",
       "      <td>547</td>\n",
       "      <td>806</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>340</td>\n",
       "      <td>423</td>\n",
       "      <td>702</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   capacity_mw  latitude  longitude  primary_fuel  source  geolocation_source  \\\n",
       "0            9       731        143             6     109                   1   \n",
       "1          177       594        234             1     174                   2   \n",
       "2          121       420          8             7      21                   2   \n",
       "3          200       547        806             2      22                   2   \n",
       "4          340       423        702             1      22                   2   \n",
       "\n",
       "   PowerPlant_age  \n",
       "0               7  \n",
       "1               3  \n",
       "2               3  \n",
       "3              14  \n",
       "4               3  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a01d7a24",
   "metadata": {},
   "source": [
    "# Remove outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c7a8dc5",
   "metadata": {},
   "source": [
    " there are very less  outliers present no need to remove if we want to remove those outliers then use z score "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "67d95aed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 71,  80, 191, 222, 299, 321, 342, 345, 371, 444, 454, 478, 484,\n",
       "        538, 541, 573, 583, 592, 601, 621, 661, 669, 689, 706, 709, 728,\n",
       "        807, 832, 848, 870, 871, 879, 895, 900], dtype=int64),\n",
       " array([4, 4, 4, 6, 4, 4, 6, 4, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 4, 4,\n",
       "        4, 6, 6, 6, 6, 4, 6, 4, 4, 4, 4, 4], dtype=int64))"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import zscore\n",
    "import numpy as np\n",
    "z=np.abs(zscore(df))  #abs is absolute function used to make all data positive \n",
    "\n",
    "#in z score value is both value -3 and +3 is present but abs function convert -3 value to positive for better calculation\n",
    "thresold=3\n",
    "   \n",
    "np.where(z>3)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2e36ec5",
   "metadata": {},
   "source": [
    "show the index value of row and column where outliers lies\n",
    "\n",
    "mostly in column no. 6 and 4 the outlies are precent means phoneservices\n",
    "\n",
    "now we have remove outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "c4bb49da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of the dataframe before removing outliers:  (907, 7)\n",
      "Shape of the dataframe after removing outliers:  (873, 7)\n",
      "Percentage of data loss post outlier removal:  3.7486218302094816\n"
     ]
    }
   ],
   "source": [
    "df_new=df[(z<3).all(axis=1)]   # axis=1 means all rows for each column\n",
    "print (\"Shape of the dataframe before removing outliers: \", df.shape)\n",
    "print (\"Shape of the dataframe after removing outliers: \", df_new.shape)\n",
    "print (\"Percentage of data loss post outlier removal: \", (df.shape[0]-df_new.shape[0])/df.shape[0]*100)\n",
    "\n",
    "df=df_new.copy() # reassigning the changed dataframe name to our original dataframe name                       "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8770c445",
   "metadata": {},
   "source": [
    "# Check outlier removed or not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "99584dfe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(ncols=4, nrows=1, figsize=(15,5))\n",
    "index = 0\n",
    "ax = ax.flatten()\n",
    "for col, value in df[numerical].items():\n",
    "    sns.boxplot(y=col, data=df, ax=ax[index])\n",
    "    index += 1\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5fd4bde",
   "metadata": {},
   "source": [
    "Now the outliers are removed from most of columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53efad09",
   "metadata": {},
   "source": [
    "# check for Skewness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "a0134a69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "capacity_mw           0.196081\n",
       "latitude              0.032857\n",
       "longitude            -0.037962\n",
       "primary_fuel          0.465975\n",
       "source                1.848104\n",
       "geolocation_source   -2.166766\n",
       "PowerPlant_age        1.469080\n",
       "dtype: float64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.skew()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4a86bb9",
   "metadata": {},
   "source": [
    "# In this perticular problem i have two targets i have to find out predictions for Fuel type and capacity_mw.\n",
    "\n",
    "If i consider Fuel type as target then i have to take classification models to predict.\n",
    "\n",
    "If i consider capacity_mw as target then i have to take Regression models to predict."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3dd7b648",
   "metadata": {},
   "source": [
    "# Model building For Fluel_type prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec55a8eb",
   "metadata": {},
   "source": [
    "# Feature and Label Seperation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "4d3cc27d",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=df.drop(\"primary_fuel\",axis=1)\n",
    "y=df[\"primary_fuel\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "4aae5e05",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(873, 6)\n",
      "(873,)\n"
     ]
    }
   ],
   "source": [
    "print(x.shape)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6d2c8cd",
   "metadata": {},
   "source": [
    "# Balancing the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "bf077953",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    254\n",
       "3    232\n",
       "7    123\n",
       "6    122\n",
       "2     68\n",
       "0     45\n",
       "5     20\n",
       "4      9\n",
       "Name: primary_fuel, dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "9b3adb60",
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "\n",
    "smt=SMOTE()# instance of smote"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "566d904b",
   "metadata": {},
   "outputs": [],
   "source": [
    "trainx,trainy=smt.fit_resample(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "7218a9d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6    254\n",
       "1    254\n",
       "7    254\n",
       "2    254\n",
       "3    254\n",
       "0    254\n",
       "5    254\n",
       "4    254\n",
       "Name: primary_fuel, dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainy.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77cbe93f",
   "metadata": {},
   "source": [
    "# check for Skewness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "16004830",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "capacity_mw           0.196081\n",
       "latitude              0.032857\n",
       "longitude            -0.037962\n",
       "source                1.848104\n",
       "geolocation_source   -2.166766\n",
       "PowerPlant_age        1.469080\n",
       "dtype: float64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.skew()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc8b1b56",
   "metadata": {},
   "source": [
    "# Remove Skewness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "87ff5584",
   "metadata": {},
   "outputs": [],
   "source": [
    "#remove skewness using power transform\n",
    "\n",
    "from sklearn.preprocessing import power_transform\n",
    "df_skew=power_transform(x)\n",
    "\n",
    "df_skew=pd.DataFrame(df_skew,columns=x.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d2f2d7d",
   "metadata": {},
   "source": [
    "# check skewness are removed or not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "73c3e888",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "capacity_mw          -0.261236\n",
       "latitude             -0.268671\n",
       "longitude            -0.298838\n",
       "source               -0.206270\n",
       "geolocation_source   -2.138453\n",
       "PowerPlant_age        0.110412\n",
       "dtype: float64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_skew.skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "bdfba0b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df_skew\n",
    "x=df_skew"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7306ed90",
   "metadata": {},
   "source": [
    "# check skewness are removed or not"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc6d7f03",
   "metadata": {},
   "source": [
    "# Multicollinerity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "ff6ea969",
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "def cal_vif(x):\n",
    "    vif=pd.DataFrame()\n",
    "    vif['variables']=x.columns\n",
    "    vif[\"vif_factor\"]=[variance_inflation_factor(x.values,i) for i in range (x.shape[1])]\n",
    "    return(vif)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "6ed373d8",
   "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>variables</th>\n",
       "      <th>vif_factor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>capacity_mw</td>\n",
       "      <td>1.493969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>latitude</td>\n",
       "      <td>1.089844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>longitude</td>\n",
       "      <td>1.194407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>source</td>\n",
       "      <td>1.272926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>geolocation_source</td>\n",
       "      <td>1.615803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>PowerPlant_age</td>\n",
       "      <td>1.167359</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            variables  vif_factor\n",
       "0         capacity_mw    1.493969\n",
       "1            latitude    1.089844\n",
       "2           longitude    1.194407\n",
       "3              source    1.272926\n",
       "4  geolocation_source    1.615803\n",
       "5      PowerPlant_age    1.167359"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cal_vif(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0396daf9",
   "metadata": {},
   "source": [
    "no any column are suffering from multicollinerity"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5306fd7",
   "metadata": {},
   "source": [
    "# Data Standardization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "d1706212",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.71802499,  1.25999699, -1.14246013,  1.65301375, -2.35219189,\n",
       "         0.10416636],\n",
       "       [ 0.32603511,  0.77959109, -0.69197566,  2.12757924,  0.39530765,\n",
       "        -0.77526269],\n",
       "       [-0.15533512,  0.12070507, -2.03957276, -0.4581729 ,  0.39530765,\n",
       "        -0.77526269],\n",
       "       ...,\n",
       "       [-0.56491772, -1.09280605, -0.34601615, -0.4581729 ,  0.39530765,\n",
       "        -0.77526269],\n",
       "       [ 0.24575513,  0.69965376, -1.0178819 ,  0.95006865,  0.39530765,\n",
       "        -0.77526269],\n",
       "       [-0.8692452 , -1.84376757,  0.40272661, -0.4581729 ,  0.39530765,\n",
       "        -0.77526269]])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Scaling the Feature data\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "SC=StandardScaler()\n",
    "x=SC.fit_transform(x)\n",
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38766def",
   "metadata": {},
   "source": [
    "# Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "927fda06",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x,y, test_size = 0.20, random_state=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "a7ab6181",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of x_train=  (698, 6)\n",
      "shape of y_train=  (698,)\n",
      "shape of x_test=  (175, 6)\n",
      "shape of y_test=  (175,)\n"
     ]
    }
   ],
   "source": [
    "print(\"shape of x_train= \",x_train.shape)\n",
    "print(\"shape of y_train= \",y_train.shape)\n",
    "print(\"shape of x_test= \",x_test.shape)\n",
    "print(\"shape of y_test= \",y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81c11e58",
   "metadata": {},
   "source": [
    "# Find best Random state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "049d1647",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "testing accuracy  0.730593607305936  random state = 0\n",
      " max accuracy score 0.730593607305936  max random state = 0\n",
      "testing accuracy  0.7214611872146118  random state = 1\n",
      "testing accuracy  0.7168949771689498  random state = 2\n",
      "testing accuracy  0.7351598173515982  random state = 3\n",
      " max accuracy score 0.7351598173515982  max random state = 3\n",
      "testing accuracy  0.7214611872146118  random state = 4\n",
      "testing accuracy  0.7214611872146118  random state = 5\n",
      "testing accuracy  0.7397260273972602  random state = 6\n",
      " max accuracy score 0.7397260273972602  max random state = 6\n",
      "testing accuracy  0.7625570776255708  random state = 7\n",
      " max accuracy score 0.7625570776255708  max random state = 7\n",
      "testing accuracy  0.7123287671232876  random state = 8\n",
      "testing accuracy  0.7442922374429224  random state = 9\n",
      "testing accuracy  0.7168949771689498  random state = 10\n",
      "testing accuracy  0.6666666666666666  random state = 11\n",
      "testing accuracy  0.7351598173515982  random state = 12\n",
      "testing accuracy  0.726027397260274  random state = 13\n",
      "testing accuracy  0.6940639269406392  random state = 14\n",
      "testing accuracy  0.726027397260274  random state = 15\n",
      "testing accuracy  0.7351598173515982  random state = 16\n",
      "testing accuracy  0.7031963470319634  random state = 17\n",
      "testing accuracy  0.7351598173515982  random state = 18\n",
      "testing accuracy  0.7214611872146118  random state = 19\n",
      "testing accuracy  0.7442922374429224  random state = 20\n",
      "testing accuracy  0.7168949771689498  random state = 21\n",
      "testing accuracy  0.7214611872146118  random state = 22\n",
      "testing accuracy  0.7397260273972602  random state = 23\n",
      "testing accuracy  0.6940639269406392  random state = 24\n",
      "testing accuracy  0.7077625570776256  random state = 25\n",
      "testing accuracy  0.7123287671232876  random state = 26\n",
      "testing accuracy  0.7214611872146118  random state = 27\n",
      "testing accuracy  0.684931506849315  random state = 28\n",
      "testing accuracy  0.7397260273972602  random state = 29\n",
      "testing accuracy  0.7534246575342466  random state = 30\n",
      "testing accuracy  0.6986301369863014  random state = 31\n",
      "testing accuracy  0.7397260273972602  random state = 32\n",
      "testing accuracy  0.6940639269406392  random state = 33\n",
      "testing accuracy  0.7168949771689498  random state = 34\n",
      "testing accuracy  0.6712328767123288  random state = 35\n",
      "testing accuracy  0.7214611872146118  random state = 36\n",
      "testing accuracy  0.7077625570776256  random state = 37\n",
      "testing accuracy  0.7077625570776256  random state = 38\n",
      "testing accuracy  0.6986301369863014  random state = 39\n",
      "testing accuracy  0.7442922374429224  random state = 40\n",
      "testing accuracy  0.7488584474885844  random state = 41\n",
      "testing accuracy  0.7168949771689498  random state = 42\n",
      "testing accuracy  0.7168949771689498  random state = 43\n",
      "testing accuracy  0.7351598173515982  random state = 44\n",
      "testing accuracy  0.7534246575342466  random state = 45\n",
      "testing accuracy  0.7397260273972602  random state = 46\n",
      "testing accuracy  0.7442922374429224  random state = 47\n",
      "testing accuracy  0.7397260273972602  random state = 48\n",
      "testing accuracy  0.730593607305936  random state = 49\n",
      "testing accuracy  0.730593607305936  random state = 50\n",
      "testing accuracy  0.7579908675799086  random state = 51\n",
      "testing accuracy  0.7488584474885844  random state = 52\n",
      "testing accuracy  0.7488584474885844  random state = 53\n",
      "testing accuracy  0.7579908675799086  random state = 54\n",
      "testing accuracy  0.7031963470319634  random state = 55\n",
      "testing accuracy  0.7351598173515982  random state = 56\n",
      "testing accuracy  0.730593607305936  random state = 57\n",
      "testing accuracy  0.7077625570776256  random state = 58\n",
      "testing accuracy  0.7123287671232876  random state = 59\n",
      "testing accuracy  0.776255707762557  random state = 60\n",
      " max accuracy score 0.776255707762557  max random state = 60\n",
      "testing accuracy  0.726027397260274  random state = 61\n",
      "testing accuracy  0.6666666666666666  random state = 62\n",
      "testing accuracy  0.726027397260274  random state = 63\n",
      "testing accuracy  0.7214611872146118  random state = 64\n",
      "testing accuracy  0.726027397260274  random state = 65\n",
      "testing accuracy  0.7351598173515982  random state = 66\n",
      "testing accuracy  0.7442922374429224  random state = 67\n",
      "testing accuracy  0.684931506849315  random state = 68\n",
      "testing accuracy  0.7579908675799086  random state = 69\n",
      "testing accuracy  0.771689497716895  random state = 70\n",
      "testing accuracy  0.7397260273972602  random state = 71\n",
      "testing accuracy  0.6894977168949772  random state = 72\n",
      "testing accuracy  0.726027397260274  random state = 73\n",
      "testing accuracy  0.6986301369863014  random state = 74\n",
      "testing accuracy  0.771689497716895  random state = 75\n",
      "testing accuracy  0.7397260273972602  random state = 76\n",
      "testing accuracy  0.7077625570776256  random state = 77\n",
      "testing accuracy  0.7031963470319634  random state = 78\n",
      "testing accuracy  0.7214611872146118  random state = 79\n",
      "testing accuracy  0.7077625570776256  random state = 80\n",
      "testing accuracy  0.7077625570776256  random state = 81\n",
      "testing accuracy  0.7397260273972602  random state = 82\n",
      "testing accuracy  0.6894977168949772  random state = 83\n",
      "testing accuracy  0.7534246575342466  random state = 84\n",
      "testing accuracy  0.726027397260274  random state = 85\n",
      "testing accuracy  0.7214611872146118  random state = 86\n",
      "testing accuracy  0.7351598173515982  random state = 87\n",
      "testing accuracy  0.7031963470319634  random state = 88\n",
      "testing accuracy  0.7488584474885844  random state = 89\n",
      "testing accuracy  0.6894977168949772  random state = 90\n",
      "testing accuracy  0.7442922374429224  random state = 91\n",
      "testing accuracy  0.7397260273972602  random state = 92\n",
      "testing accuracy  0.7031963470319634  random state = 93\n",
      "testing accuracy  0.730593607305936  random state = 94\n",
      "testing accuracy  0.7488584474885844  random state = 95\n",
      "testing accuracy  0.7488584474885844  random state = 96\n",
      "testing accuracy  0.7488584474885844  random state = 97\n",
      "testing accuracy  0.7214611872146118  random state = 98\n",
      "testing accuracy  0.7214611872146118  random state = 99\n",
      "testing accuracy  0.6894977168949772  random state = 100\n",
      "testing accuracy  0.7214611872146118  random state = 101\n",
      "testing accuracy  0.7488584474885844  random state = 102\n",
      "testing accuracy  0.7214611872146118  random state = 103\n",
      "testing accuracy  0.6986301369863014  random state = 104\n",
      "testing accuracy  0.7579908675799086  random state = 105\n",
      "testing accuracy  0.7214611872146118  random state = 106\n",
      "testing accuracy  0.6986301369863014  random state = 107\n",
      "testing accuracy  0.771689497716895  random state = 108\n",
      "testing accuracy  0.7168949771689498  random state = 109\n",
      "testing accuracy  0.7579908675799086  random state = 110\n",
      "testing accuracy  0.6940639269406392  random state = 111\n",
      "testing accuracy  0.7625570776255708  random state = 112\n",
      "testing accuracy  0.730593607305936  random state = 113\n",
      "testing accuracy  0.7442922374429224  random state = 114\n",
      "testing accuracy  0.7077625570776256  random state = 115\n",
      "testing accuracy  0.7671232876712328  random state = 116\n",
      "testing accuracy  0.726027397260274  random state = 117\n",
      "testing accuracy  0.7397260273972602  random state = 118\n",
      "testing accuracy  0.7123287671232876  random state = 119\n",
      "testing accuracy  0.7351598173515982  random state = 120\n",
      "testing accuracy  0.7579908675799086  random state = 121\n",
      "testing accuracy  0.726027397260274  random state = 122\n",
      "testing accuracy  0.7579908675799086  random state = 123\n",
      "testing accuracy  0.7031963470319634  random state = 124\n",
      "testing accuracy  0.7397260273972602  random state = 125\n",
      "testing accuracy  0.6712328767123288  random state = 126\n",
      "testing accuracy  0.6986301369863014  random state = 127\n",
      "testing accuracy  0.7488584474885844  random state = 128\n",
      "testing accuracy  0.7168949771689498  random state = 129\n",
      "testing accuracy  0.726027397260274  random state = 130\n",
      "testing accuracy  0.730593607305936  random state = 131\n",
      "testing accuracy  0.6712328767123288  random state = 132\n",
      "testing accuracy  0.7351598173515982  random state = 133\n",
      "testing accuracy  0.726027397260274  random state = 134\n",
      "testing accuracy  0.7534246575342466  random state = 135\n",
      "testing accuracy  0.7214611872146118  random state = 136\n",
      "testing accuracy  0.684931506849315  random state = 137\n",
      "testing accuracy  0.7351598173515982  random state = 138\n",
      "testing accuracy  0.7625570776255708  random state = 139\n",
      "testing accuracy  0.771689497716895  random state = 140\n",
      "testing accuracy  0.730593607305936  random state = 141\n",
      "testing accuracy  0.7534246575342466  random state = 142\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "testing accuracy  0.7442922374429224  random state = 143\n",
      "testing accuracy  0.7488584474885844  random state = 144\n",
      "testing accuracy  0.7077625570776256  random state = 145\n",
      "testing accuracy  0.7214611872146118  random state = 146\n",
      "testing accuracy  0.7579908675799086  random state = 147\n",
      "testing accuracy  0.7168949771689498  random state = 148\n",
      "testing accuracy  0.7442922374429224  random state = 149\n",
      "testing accuracy  0.7214611872146118  random state = 150\n",
      "testing accuracy  0.7625570776255708  random state = 151\n",
      "testing accuracy  0.7123287671232876  random state = 152\n",
      "testing accuracy  0.726027397260274  random state = 153\n",
      "testing accuracy  0.7442922374429224  random state = 154\n",
      "testing accuracy  0.7031963470319634  random state = 155\n",
      "testing accuracy  0.7351598173515982  random state = 156\n",
      "testing accuracy  0.7168949771689498  random state = 157\n",
      "testing accuracy  0.7168949771689498  random state = 158\n",
      "testing accuracy  0.7123287671232876  random state = 159\n",
      "testing accuracy  0.6894977168949772  random state = 160\n",
      "testing accuracy  0.7077625570776256  random state = 161\n",
      "testing accuracy  0.7351598173515982  random state = 162\n",
      "testing accuracy  0.6986301369863014  random state = 163\n",
      "testing accuracy  0.7488584474885844  random state = 164\n",
      "testing accuracy  0.7351598173515982  random state = 165\n",
      "testing accuracy  0.7351598173515982  random state = 166\n",
      "testing accuracy  0.7123287671232876  random state = 167\n",
      "testing accuracy  0.7397260273972602  random state = 168\n",
      "testing accuracy  0.6940639269406392  random state = 169\n",
      "testing accuracy  0.7488584474885844  random state = 170\n",
      "testing accuracy  0.7351598173515982  random state = 171\n",
      "testing accuracy  0.7077625570776256  random state = 172\n",
      "testing accuracy  0.7351598173515982  random state = 173\n",
      "testing accuracy  0.684931506849315  random state = 174\n",
      "testing accuracy  0.7123287671232876  random state = 175\n",
      "testing accuracy  0.730593607305936  random state = 176\n",
      "testing accuracy  0.680365296803653  random state = 177\n",
      "testing accuracy  0.7123287671232876  random state = 178\n",
      "testing accuracy  0.726027397260274  random state = 179\n",
      "testing accuracy  0.726027397260274  random state = 180\n",
      "testing accuracy  0.7077625570776256  random state = 181\n",
      "testing accuracy  0.7351598173515982  random state = 182\n",
      "testing accuracy  0.7488584474885844  random state = 183\n",
      "testing accuracy  0.7625570776255708  random state = 184\n",
      "testing accuracy  0.7488584474885844  random state = 185\n",
      "testing accuracy  0.7488584474885844  random state = 186\n",
      "testing accuracy  0.7214611872146118  random state = 187\n",
      "testing accuracy  0.7351598173515982  random state = 188\n",
      "testing accuracy  0.7214611872146118  random state = 189\n",
      "testing accuracy  0.7031963470319634  random state = 190\n",
      "testing accuracy  0.7351598173515982  random state = 191\n",
      "testing accuracy  0.6757990867579908  random state = 192\n",
      "testing accuracy  0.7123287671232876  random state = 193\n",
      "testing accuracy  0.771689497716895  random state = 194\n",
      "testing accuracy  0.7123287671232876  random state = 195\n",
      "testing accuracy  0.7031963470319634  random state = 196\n",
      "testing accuracy  0.7442922374429224  random state = 197\n",
      "testing accuracy  0.6894977168949772  random state = 198\n",
      "testing accuracy  0.7808219178082192  random state = 199\n",
      " max accuracy score 0.7808219178082192  max random state = 199\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "max_accu=0 #maxi accuracy define as 0\n",
    "max_rs=0# best random state for which max accurecy achived\n",
    "\n",
    "lr=LogisticRegression()\n",
    " \n",
    "for i in range(0,200):\n",
    "    x_train, x_test, y_train, y_test = train_test_split( x, y, test_size=.25, random_state=i)\n",
    "    \n",
    "    lr.fit(x_train,y_train)\n",
    "\n",
    "    pred=lr.predict(x_test)# predicted target variable\n",
    "    \n",
    "    acc=accuracy_score(y_test,pred)# accuracy score\n",
    "    \n",
    "    print(\"testing accuracy \",acc, \" random state =\", i)\n",
    "    if acc>max_accu:\n",
    "        max_accu=acc\n",
    "        max_rs=i\n",
    "        print(\" max accuracy score\",acc, \" max random state =\",i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "af8788cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " max accuracy score 0.7808219178082192  max random state = 199\n"
     ]
    }
   ],
   "source": [
    "print(\" max accuracy score\",max_accu, \" max random state =\", max_rs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24cc5030",
   "metadata": {},
   "source": [
    "# Classification model_selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "beaf4d4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# model_selection is function  created bcz when we want to use another model then only call this function\n",
    "\n",
    "def model_selection(algorithm_instance,x_train, x_test, y_train, y_test):\n",
    "    x_train, x_test, y_train, y_test = train_test_split(x,y, test_size = 0.24, random_state=199)\n",
    "    \n",
    "    algorithm_instance.fit(x_train,y_train)\n",
    "    \n",
    "    model1_pred=algorithm_instance.predict(x_test)\n",
    "    \n",
    "    print(\"Accuracy Score\",accuracy_score(y_test,model1_pred)*100)\n",
    "    \n",
    "    print(\"confusion_matrix\",confusion_matrix(y_test,model1_pred))\n",
    "    \n",
    "    print(\"classification_report\",classification_report(y_test,model1_pred))\n",
    "\n",
    "def model_cv(algorithm_instance,trainx,trainy):\n",
    "    for j in range(4,10):\n",
    "        lsscore=cross_val_score(algorithm_instance,trainx,trainy,cv=j)\n",
    "        print(lsscore)\n",
    "        lsc=lsscore.mean()\n",
    "        print(\" At cv =\",j)\n",
    "        print(\"cross validation score \",lsc*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "6af98de8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score 80.0\n",
      "confusion_matrix [[ 8  1  0  0  0  0  0  1]\n",
      " [ 4 48  3  4  0  0  0  3]\n",
      " [ 0  2  6  3  0  1  0  2]\n",
      " [ 0  9  4 48  0  0  0  0]\n",
      " [ 0  2  0  1  0  0  0  0]\n",
      " [ 1  0  0  1  0  0  0  0]\n",
      " [ 0  0  0  0  0  0 31  0]\n",
      " [ 0  0  0  0  0  0  0 27]]\n",
      "classification_report               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.62      0.80      0.70        10\n",
      "           1       0.77      0.77      0.77        62\n",
      "           2       0.46      0.43      0.44        14\n",
      "           3       0.84      0.79      0.81        61\n",
      "           4       0.00      0.00      0.00         3\n",
      "           5       0.00      0.00      0.00         2\n",
      "           6       1.00      1.00      1.00        31\n",
      "           7       0.82      1.00      0.90        27\n",
      "\n",
      "    accuracy                           0.80       210\n",
      "   macro avg       0.56      0.60      0.58       210\n",
      "weighted avg       0.79      0.80      0.79       210\n",
      "\n",
      "[0.77755906 0.79330709 0.83858268 0.84645669]\n",
      " At cv = 4\n",
      "cross validation score  81.39763779527559\n",
      "[0.81081081 0.80835381 0.81527094 0.85960591 0.82758621]\n",
      " At cv = 5\n",
      "cross validation score  82.43255346703624\n",
      "[0.820059   0.79351032 0.79351032 0.81120944 0.86982249 0.83136095]\n",
      " At cv = 6\n",
      "cross validation score  81.99120862497308\n",
      "[0.80412371 0.78694158 0.82413793 0.83448276 0.82413793 0.88275862\n",
      " 0.82758621]\n",
      " At cv = 7\n",
      "cross validation score  82.63098200531546\n",
      "[0.7992126  0.78346457 0.83464567 0.79527559 0.80314961 0.87401575\n",
      " 0.87401575 0.82677165]\n",
      " At cv = 8\n",
      "cross validation score  82.38188976377954\n",
      "[0.80088496 0.80973451 0.81415929 0.80088496 0.84955752 0.79646018\n",
      " 0.86725664 0.86222222 0.81333333]\n",
      " At cv = 9\n",
      "cross validation score  82.38326231836557\n"
     ]
    }
   ],
   "source": [
    "knn=KNeighborsClassifier()\n",
    "model_selection(knn,x_train, x_test, y_train, y_test)\n",
    "model_cv(knn,trainx,trainy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "5199c5de",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score 89.04761904761904\n",
      "confusion_matrix [[ 9  1  0  0  0  0  0  0]\n",
      " [ 1 55  1  5  0  0  0  0]\n",
      " [ 0  4  6  3  0  1  0  0]\n",
      " [ 0  4  0 57  0  0  0  0]\n",
      " [ 0  2  0  1  0  0  0  0]\n",
      " [ 0  0  0  0  0  2  0  0]\n",
      " [ 0  0  0  0  0  0 31  0]\n",
      " [ 0  0  0  0  0  0  0 27]]\n",
      "classification_report               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.90      0.90        10\n",
      "           1       0.83      0.89      0.86        62\n",
      "           2       0.86      0.43      0.57        14\n",
      "           3       0.86      0.93      0.90        61\n",
      "           4       0.00      0.00      0.00         3\n",
      "           5       0.67      1.00      0.80         2\n",
      "           6       1.00      1.00      1.00        31\n",
      "           7       1.00      1.00      1.00        27\n",
      "\n",
      "    accuracy                           0.89       210\n",
      "   macro avg       0.77      0.77      0.75       210\n",
      "weighted avg       0.88      0.89      0.88       210\n",
      "\n",
      "[0.91535433 0.92716535 0.90551181 0.93897638]\n",
      " At cv = 4\n",
      "cross validation score  92.1751968503937\n",
      "[0.91400491 0.90909091 0.93596059 0.9137931  0.92610837]\n",
      " At cv = 5\n",
      "cross validation score  91.9791578412268\n",
      "[0.91740413 0.90265487 0.94690265 0.91740413 0.92307692 0.93786982]\n",
      " At cv = 6\n",
      "cross validation score  92.42187545455074\n",
      "[0.90034364 0.90378007 0.93103448 0.94137931 0.9        0.93448276\n",
      " 0.93448276]\n",
      " At cv = 7\n",
      "cross validation score  92.0786145955005\n",
      "[0.90551181 0.91338583 0.91732283 0.94488189 0.91732283 0.9015748\n",
      " 0.93700787 0.93700787]\n",
      " At cv = 8\n",
      "cross validation score  92.17519685039369\n",
      "[0.90707965 0.9380531  0.88938053 0.92035398 0.94690265 0.89823009\n",
      " 0.91150442 0.93333333 0.93333333]\n",
      " At cv = 9\n",
      "cross validation score  91.97967879383808\n"
     ]
    }
   ],
   "source": [
    "rf= RandomForestClassifier(random_state=199)\n",
    "model_selection(rf,x_train, x_test, y_train, y_test)\n",
    "model_cv(rf,trainx,trainy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "138d80aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score 80.0\n",
      "confusion_matrix [[ 8  1  0  0  0  0  0  1]\n",
      " [ 2 53  0  4  0  0  0  3]\n",
      " [ 0  8  0  4  0  0  0  2]\n",
      " [ 0 10  0 51  0  0  0  0]\n",
      " [ 0  2  0  1  0  0  0  0]\n",
      " [ 1  0  0  1  0  0  0  0]\n",
      " [ 0  1  0  0  0  0 30  0]\n",
      " [ 0  1  0  0  0  0  0 26]]\n",
      "classification_report               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.73      0.80      0.76        10\n",
      "           1       0.70      0.85      0.77        62\n",
      "           2       0.00      0.00      0.00        14\n",
      "           3       0.84      0.84      0.84        61\n",
      "           4       0.00      0.00      0.00         3\n",
      "           5       0.00      0.00      0.00         2\n",
      "           6       1.00      0.97      0.98        31\n",
      "           7       0.81      0.96      0.88        27\n",
      "\n",
      "    accuracy                           0.80       210\n",
      "   macro avg       0.51      0.55      0.53       210\n",
      "weighted avg       0.74      0.80      0.76       210\n",
      "\n",
      "[0.67322835 0.65748031 0.63385827 0.70275591]\n",
      " At cv = 4\n",
      "cross validation score  66.68307086614173\n",
      "[0.68550369 0.68550369 0.63793103 0.65763547 0.72660099]\n",
      " At cv = 5\n",
      "cross validation score  67.863497173842\n",
      "[0.68436578 0.65486726 0.69026549 0.63126844 0.69230769 0.70118343]\n",
      " At cv = 6\n",
      "cross validation score  67.57096809853788\n",
      "[0.67697595 0.67697595 0.70344828 0.67241379 0.65172414 0.72758621\n",
      " 0.68275862]\n",
      " At cv = 7\n",
      "cross validation score  68.45547035024461\n",
      "[0.66535433 0.7007874  0.68897638 0.69291339 0.61811024 0.66929134\n",
      " 0.7480315  0.68897638]\n",
      " At cv = 8\n",
      "cross validation score  68.40551181102363\n",
      "[0.66814159 0.7079646  0.67256637 0.69469027 0.67699115 0.67699115\n",
      " 0.64159292 0.76888889 0.67555556]\n",
      " At cv = 9\n",
      "cross validation score  68.70424997268655\n"
     ]
    }
   ],
   "source": [
    "sv=SVC()\n",
    "model_selection(sv,x_train, x_test, y_train, y_test)\n",
    "model_cv(sv,trainx,trainy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "ccc207a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score 79.52380952380952\n",
      "confusion_matrix [[ 9  1  0  0  0  0  0  0]\n",
      " [ 1 45  8  6  1  0  1  0]\n",
      " [ 0  4  6  2  1  1  0  0]\n",
      " [ 0  7  4 47  0  3  0  0]\n",
      " [ 0  1  0  1  1  0  0  0]\n",
      " [ 0  0  1  0  0  1  0  0]\n",
      " [ 0  0  0  0  0  0 31  0]\n",
      " [ 0  0  0  0  0  0  0 27]]\n",
      "classification_report               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.90      0.90        10\n",
      "           1       0.78      0.73      0.75        62\n",
      "           2       0.32      0.43      0.36        14\n",
      "           3       0.84      0.77      0.80        61\n",
      "           4       0.33      0.33      0.33         3\n",
      "           5       0.20      0.50      0.29         2\n",
      "           6       0.97      1.00      0.98        31\n",
      "           7       1.00      1.00      1.00        27\n",
      "\n",
      "    accuracy                           0.80       210\n",
      "   macro avg       0.67      0.71      0.68       210\n",
      "weighted avg       0.82      0.80      0.80       210\n",
      "\n",
      "[0.86811024 0.8976378  0.88976378 0.87598425]\n",
      " At cv = 4\n",
      "cross validation score  88.28740157480314\n",
      "[0.89189189 0.87714988 0.89162562 0.88916256 0.85714286]\n",
      " At cv = 5\n",
      "cross validation score  88.13945607049055\n",
      "[0.86135693 0.87020649 0.90855457 0.90560472 0.89940828 0.87278107]\n",
      " At cv = 6\n",
      "cross validation score  88.63186771627888\n",
      "[0.87972509 0.88316151 0.87241379 0.91724138 0.89655172 0.88965517\n",
      " 0.85862069]\n",
      " At cv = 7\n",
      "cross validation score  88.53384795084047\n",
      "[0.86614173 0.89370079 0.89370079 0.90551181 0.88582677 0.87795276\n",
      " 0.8976378  0.8503937 ]\n",
      " At cv = 8\n",
      "cross validation score  88.38582677165354\n",
      "[0.88053097 0.88053097 0.88495575 0.87610619 0.93362832 0.91150442\n",
      " 0.88938053 0.91111111 0.86666667]\n",
      " At cv = 9\n",
      "cross validation score  89.27127717688191\n"
     ]
    }
   ],
   "source": [
    "# model dtc\n",
    "dtc=DecisionTreeClassifier(random_state=199, max_depth=15)\n",
    "model_selection(dtc,x_train, x_test, y_train, y_test)\n",
    "model_cv(dtc,trainx,trainy)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c06562f6",
   "metadata": {},
   "source": [
    "# Compare All Model Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "816f14e9",
   "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>Model</th>\n",
       "      <th>Model accuracy</th>\n",
       "      <th>Cross validation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RandomForest classifier</td>\n",
       "      <td>90</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DecisionTree classifier</td>\n",
       "      <td>79</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KNeighbour classifier</td>\n",
       "      <td>80</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>svm</td>\n",
       "      <td>80</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Model  Model accuracy  Cross validation\n",
       "0  RandomForest classifier              90                92\n",
       "1  DecisionTree classifier              79                88\n",
       "2    KNeighbour classifier              80                82\n",
       "3                      svm              80                66"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = pd.DataFrame({\n",
    "\n",
    "    'Model': [\"RandomForest classifier\",\"DecisionTree classifier\",\"KNeighbour classifier\",\"svm\"],\n",
    "\n",
    "    'Model accuracy': [90,79,80,80],\n",
    "\n",
    "     \"Cross validation\":[92,88,82,66]\n",
    "    })\n",
    "\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7800af9",
   "metadata": {},
   "source": [
    "# Hyper Parameter Tuning For Best Mode\n",
    "Choosing best model as\n",
    "Random Forest classification Model \n",
    "\n",
    "minimum difference between cross validation and Accuracy score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "3031305a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'max_depth': 10, 'max_features': 1, 'n_estimators': 100}\n"
     ]
    }
   ],
   "source": [
    "# Create the parameter grid based on the results of random search \n",
    "param_grid = {'max_depth':[3,5,10,None],\n",
    "              'n_estimators':[10,100,200,300,400],\n",
    "              'max_features':[1,3,5,7],\n",
    "           }\n",
    "# Create a based model\n",
    "rf = RandomForestClassifier()\n",
    "# Instantiate the grid search model\n",
    "grid_search = GridSearchCV(estimator = rf, param_grid = param_grid,cv = 9)\n",
    "\n",
    "# Fit the grid search to the data\n",
    "grid_search.fit(x_train,y_train)\n",
    "print(grid_search.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7ae4f93",
   "metadata": {},
   "source": [
    "# Obtaining the Best Parameters for Hyper parameters tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "c81ea108",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy_score 0.8949771689497716\n",
      "cross validation score  90.64874904402926\n",
      "confusion_matrix [[10  1  0  0  0  0  0  0]\n",
      " [ 2 58  0  4  0  0  0  0]\n",
      " [ 0  5  6  2  0  0  0  1]\n",
      " [ 0  4  0 60  0  0  0  0]\n",
      " [ 0  2  0  1  0  0  0  0]\n",
      " [ 0  0  0  1  0  1  0  0]\n",
      " [ 0  0  0  0  0  0 31  0]\n",
      " [ 0  0  0  0  0  0  0 30]]\n",
      "classification_report               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.91      0.87        11\n",
      "           1       0.83      0.91      0.87        64\n",
      "           2       1.00      0.43      0.60        14\n",
      "           3       0.88      0.94      0.91        64\n",
      "           4       0.00      0.00      0.00         3\n",
      "           5       1.00      0.50      0.67         2\n",
      "           6       1.00      1.00      1.00        31\n",
      "           7       0.97      1.00      0.98        30\n",
      "\n",
      "    accuracy                           0.89       219\n",
      "   macro avg       0.81      0.71      0.74       219\n",
      "weighted avg       0.89      0.89      0.88       219\n",
      "\n"
     ]
    }
   ],
   "source": [
    "rf= RandomForestClassifier(max_depth = 10, n_estimators=100, max_features= 1)\n",
    "#n_estimatorsint, default=100 The number of trees in the forest.\n",
    "rf.fit(x_train,y_train)\n",
    "pred_rf=rf.predict(x_test)\n",
    "print(\"accuracy_score\",accuracy_score(y_test,pred_rf))\n",
    "\n",
    "cr_vl=cross_val_score(rf,trainx,trainy,cv=9)\n",
    "\n",
    "print(\"cross validation score \",(cr_vl.mean())*100)\n",
    "\n",
    "print(\"confusion_matrix\",confusion_matrix(y_test,pred_rf))\n",
    "\n",
    "print(\"classification_report\",classification_report(y_test,pred_rf))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91b383c8",
   "metadata": {},
   "source": [
    "# Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "89f6f727",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "class_names = df.columns\n",
    "metrics.plot_confusion_matrix(rf, x_test, y_test, cmap='mako')\n",
    "plt.title('\\t Confusion Matrix for random Farest classification \\n')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0f92607",
   "metadata": {},
   "source": [
    "With the help of above confusion matrix I am able to understand the number of times I got the correct outputs and the number of times my model missed to provide the correct prediction (depicting in the black boxes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e087c4d3",
   "metadata": {},
   "source": [
    "# Saving ,Loading and Conclusion the best Classification ML model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "3442e328",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "filename_1=\"Fluel_type.pkl\"\n",
    "pickle.dump(rf,open(filename_1,\"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "924afb69",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "89.49771689497716\n"
     ]
    }
   ],
   "source": [
    "loaded_model=pickle.load(open(\"Fluel_type.pkl\",\"rb\"))\n",
    "result=loaded_model.score(x_test,y_test)\n",
    "print(result*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "a2a7ff43",
   "metadata": {},
   "outputs": [],
   "source": [
    "conclusion=pd.DataFrame([loaded_model.predict(x_test)[:],pred_rf[:]],index=[\"predicted\",\"original\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "2705d8b0",
   "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>predicted</th>\n",
       "      <th>original</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>219 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     predicted  original\n",
       "0            1         1\n",
       "1            3         3\n",
       "2            1         1\n",
       "3            3         3\n",
       "4            3         3\n",
       "..         ...       ...\n",
       "214          3         3\n",
       "215          3         3\n",
       "216          7         7\n",
       "217          1         1\n",
       "218          0         0\n",
       "\n",
       "[219 rows x 2 columns]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conclusion.T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "904eff3e",
   "metadata": {},
   "source": [
    "# Model building For capacity_mw prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "75f4e06a",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = df.drop('capacity_mw', axis=1)\n",
    "b = df['capacity_mw']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3583d02",
   "metadata": {},
   "source": [
    "# Feature Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "0dc29f45",
   "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>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>source</th>\n",
       "      <th>geolocation_source</th>\n",
       "      <th>PowerPlant_age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.259997</td>\n",
       "      <td>-1.142460</td>\n",
       "      <td>1.653014</td>\n",
       "      <td>-2.352192</td>\n",
       "      <td>0.104166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.779591</td>\n",
       "      <td>-0.691976</td>\n",
       "      <td>2.127579</td>\n",
       "      <td>0.395308</td>\n",
       "      <td>-0.775263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.120705</td>\n",
       "      <td>-2.039573</td>\n",
       "      <td>-0.458173</td>\n",
       "      <td>0.395308</td>\n",
       "      <td>-0.775263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.607751</td>\n",
       "      <td>1.521537</td>\n",
       "      <td>-0.388489</td>\n",
       "      <td>0.395308</td>\n",
       "      <td>0.741160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.132653</td>\n",
       "      <td>1.162432</td>\n",
       "      <td>-0.388489</td>\n",
       "      <td>0.395308</td>\n",
       "      <td>-0.775263</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   latitude  longitude    source  geolocation_source  PowerPlant_age\n",
       "0  1.259997  -1.142460  1.653014           -2.352192        0.104166\n",
       "1  0.779591  -0.691976  2.127579            0.395308       -0.775263\n",
       "2  0.120705  -2.039573 -0.458173            0.395308       -0.775263\n",
       "3  0.607751   1.521537 -0.388489            0.395308        0.741160\n",
       "4  0.132653   1.162432 -0.388489            0.395308       -0.775263"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "a = pd.DataFrame(scaler.fit_transform(a), columns=a.columns)\n",
    "a.head() # Displaying all the features after applying scaling technique to avoid bias output"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0f7181e",
   "metadata": {},
   "source": [
    "# Finding the best random state for building Regression Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "f8aa5eaa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "testing accuracy  0.34694083336504145  random state = 0\n",
      " max accuracy score 0.34694083336504145  max random state = 0\n",
      "testing accuracy  0.30816076025265604  random state = 1\n",
      "testing accuracy  0.31850939796483657  random state = 2\n",
      "testing accuracy  0.33860271105280515  random state = 3\n",
      "testing accuracy  0.3236967268676286  random state = 4\n",
      "testing accuracy  0.30735285412252966  random state = 5\n",
      "testing accuracy  0.34043637061603516  random state = 6\n",
      "testing accuracy  0.26141138997569036  random state = 7\n",
      "testing accuracy  0.3753523866573596  random state = 8\n",
      " max accuracy score 0.3753523866573596  max random state = 8\n",
      "testing accuracy  0.23156694191732896  random state = 9\n",
      "testing accuracy  0.2921124457038933  random state = 10\n",
      "testing accuracy  0.28942897520092103  random state = 11\n",
      "testing accuracy  0.2402012334017426  random state = 12\n",
      "testing accuracy  0.3329809114915795  random state = 13\n",
      "testing accuracy  0.3770322284165971  random state = 14\n",
      " max accuracy score 0.3770322284165971  max random state = 14\n",
      "testing accuracy  0.3225385633604657  random state = 15\n",
      "testing accuracy  0.2782784323990084  random state = 16\n",
      "testing accuracy  0.30535069850323704  random state = 17\n",
      "testing accuracy  0.3712978111722951  random state = 18\n",
      "testing accuracy  0.3757488630453858  random state = 19\n",
      "testing accuracy  0.34572873131208914  random state = 20\n",
      "testing accuracy  0.3301712351548778  random state = 21\n",
      "testing accuracy  0.34236754143868753  random state = 22\n",
      "testing accuracy  0.2851555677150902  random state = 23\n",
      "testing accuracy  0.42017811220711343  random state = 24\n",
      " max accuracy score 0.42017811220711343  max random state = 24\n",
      "testing accuracy  0.32915060070680735  random state = 25\n",
      "testing accuracy  0.3297899389655118  random state = 26\n",
      "testing accuracy  0.34487033089598007  random state = 27\n",
      "testing accuracy  0.37885733840943747  random state = 28\n",
      "testing accuracy  0.28614399244570965  random state = 29\n",
      "testing accuracy  0.3158988583955926  random state = 30\n",
      "testing accuracy  0.3212798576642424  random state = 31\n",
      "testing accuracy  0.3107771333481125  random state = 32\n",
      "testing accuracy  0.3417419329882895  random state = 33\n",
      "testing accuracy  0.18165628136527168  random state = 34\n",
      "testing accuracy  0.2755457353917974  random state = 35\n",
      "testing accuracy  0.347101204991062  random state = 36\n",
      "testing accuracy  0.30873563110871327  random state = 37\n",
      "testing accuracy  0.27134273908631057  random state = 38\n",
      "testing accuracy  0.2785634858216043  random state = 39\n",
      "testing accuracy  0.32603242010636235  random state = 40\n",
      "testing accuracy  0.2370704025195528  random state = 41\n",
      "testing accuracy  0.2607038231625124  random state = 42\n",
      "testing accuracy  0.34680726813905594  random state = 43\n",
      "testing accuracy  0.36764349167783206  random state = 44\n",
      "testing accuracy  0.3051048684740225  random state = 45\n",
      "testing accuracy  0.2746749639382776  random state = 46\n",
      "testing accuracy  0.28744533101081415  random state = 47\n",
      "testing accuracy  0.3446750965665869  random state = 48\n",
      "testing accuracy  0.1598583820015692  random state = 49\n",
      "testing accuracy  0.3193065855069195  random state = 50\n",
      "testing accuracy  0.23992688541440965  random state = 51\n",
      "testing accuracy  0.33408956045455074  random state = 52\n",
      "testing accuracy  0.3050523871310262  random state = 53\n",
      "testing accuracy  0.3216425766474159  random state = 54\n",
      "testing accuracy  0.3576049567811883  random state = 55\n",
      "testing accuracy  0.3487740965680838  random state = 56\n",
      "testing accuracy  0.3367591995625351  random state = 57\n",
      "testing accuracy  0.32336927660825543  random state = 58\n",
      "testing accuracy  0.35263993944016525  random state = 59\n",
      "testing accuracy  0.23973672739600393  random state = 60\n",
      "testing accuracy  0.4070309273772913  random state = 61\n",
      "testing accuracy  0.2969680254870831  random state = 62\n",
      "testing accuracy  0.25781514788598947  random state = 63\n",
      "testing accuracy  0.26828842085397575  random state = 64\n",
      "testing accuracy  0.23098811848035117  random state = 65\n",
      "testing accuracy  0.19553702152910035  random state = 66\n",
      "testing accuracy  0.34381155445318246  random state = 67\n",
      "testing accuracy  0.34925706290827396  random state = 68\n",
      "testing accuracy  0.2724612129808818  random state = 69\n",
      "testing accuracy  0.38883899107147  random state = 70\n",
      "testing accuracy  0.3899317271281051  random state = 71\n",
      "testing accuracy  0.3274586548462053  random state = 72\n",
      "testing accuracy  0.36637364380170634  random state = 73\n",
      "testing accuracy  0.3476703157825711  random state = 74\n",
      "testing accuracy  0.30591892260809195  random state = 75\n",
      "testing accuracy  0.30475520885144414  random state = 76\n",
      "testing accuracy  0.3786605060134032  random state = 77\n",
      "testing accuracy  0.32903931633754435  random state = 78\n",
      "testing accuracy  0.3760610714902656  random state = 79\n",
      "testing accuracy  0.27237215207392984  random state = 80\n",
      "testing accuracy  0.3378978396360588  random state = 81\n",
      "testing accuracy  0.29173520161459987  random state = 82\n",
      "testing accuracy  0.336743445727529  random state = 83\n",
      "testing accuracy  0.33037348987409165  random state = 84\n",
      "testing accuracy  0.256203411043222  random state = 85\n",
      "testing accuracy  0.29254377390990094  random state = 86\n",
      "testing accuracy  0.33806613402640695  random state = 87\n",
      "testing accuracy  0.31929138744344654  random state = 88\n",
      "testing accuracy  0.3137453722818484  random state = 89\n",
      "testing accuracy  0.3002167662767764  random state = 90\n",
      "testing accuracy  0.2857296862288118  random state = 91\n",
      "testing accuracy  0.2820987632589811  random state = 92\n",
      "testing accuracy  0.30706940289100915  random state = 93\n",
      "testing accuracy  0.26608739430327044  random state = 94\n",
      "testing accuracy  0.33045711282777346  random state = 95\n",
      "testing accuracy  0.2814979478634213  random state = 96\n",
      "testing accuracy  0.3642769319677026  random state = 97\n",
      "testing accuracy  0.37294829627855464  random state = 98\n",
      "testing accuracy  0.3536997587468931  random state = 99\n"
     ]
    }
   ],
   "source": [
    "max_accu=0 #maxi accuracy define as 0\n",
    "max_rs=0# best random state for which max accurecy achived\n",
    "\n",
    "lr=LinearRegression()\n",
    " \n",
    "for i in range(0,100):\n",
    "    a_train, a_test, b_train, b_test = train_test_split( a, b, test_size=.25, random_state=i)\n",
    "    \n",
    "    lr.fit(a_train,b_train)\n",
    "\n",
    "    predlr=lr.predict(a_test)# predicted target variable\n",
    "    \n",
    "    acc=r2_score(b_test,predlr)# accuracy score\n",
    "    \n",
    "    print(\"testing accuracy \",acc, \" random state =\", i)\n",
    "    if acc>max_accu:\n",
    "        max_accu=acc\n",
    "        max_rs=i\n",
    "        print(\" max accuracy score\",acc, \" max random state =\",i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "bafc44cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Regression Model Function\n",
    "\n",
    "def reg(model, a, b):\n",
    "    a_train, a_test, b_train, b_test = train_test_split( a, b, test_size=.25, random_state=i)\n",
    "    \n",
    "    # Training the model\n",
    "    model.fit(a_train, b_train)\n",
    "    \n",
    "    # Predicting Y_test\n",
    "    pred = model.predict(a_test)\n",
    "    \n",
    "    # RMSE - a lower RMSE score is better than a higher one\n",
    "    rmse = mean_squared_error(b_test, pred, squared=False)\n",
    "    print(\"RMSE Score is:\", rmse)\n",
    "    \n",
    "    # R2 score\n",
    "    r2 = r2_score(b_test, pred, multioutput='variance_weighted')*100\n",
    "    print(\"R2 Score is:\", r2)\n",
    "    \n",
    "    # Cross Validation Score\n",
    "    cv_score = (cross_val_score(model, a, b, cv=9).mean())*100\n",
    "    print(\"Cross Validation Score:\", cv_score)\n",
    "    \n",
    "    # Result of r2 score minus cv score\n",
    "    result = r2 - cv_score\n",
    "    print(\"R2 Score - Cross Validation Score is\", result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "0236769a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE Score is: 0.7832776408437937\n",
      "R2 Score is: 35.36997587468931\n",
      "Cross Validation Score: 30.626049879018076\n",
      "R2 Score - Cross Validation Score is 4.743925995671233\n"
     ]
    }
   ],
   "source": [
    "# Linear Regression Model\n",
    "\n",
    "model=LinearRegression()\n",
    "reg(model, a, b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "00ad2599",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE Score is: 0.7832496891317489\n",
      "R2 Score is: 35.37458851137458\n",
      "Cross Validation Score: 30.635648284663976\n",
      "R2 Score - Cross Validation Score is 4.738940226710607\n"
     ]
    }
   ],
   "source": [
    "# Ridge Regression\n",
    "\n",
    "model=Ridge(alpha=1e-2, normalize=True)\n",
    "reg(model, a, b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "ed22b53e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE Score is: 0.8821640530495926\n",
      "R2 Score is: 18.02119698217688\n",
      "Cross Validation Score: 13.846252140050902\n",
      "R2 Score - Cross Validation Score is 4.174944842125976\n"
     ]
    }
   ],
   "source": [
    "# Lasso Regression\n",
    "\n",
    "model=Lasso(alpha=1e-2, normalize=True)\n",
    "reg(model, a, b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "b23e4bec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE Score is: 0.8480706399799659\n",
      "R2 Score is: 24.235298661516524\n",
      "Cross Validation Score: 25.52365536097077\n",
      "R2 Score - Cross Validation Score is -1.2883566994542477\n"
     ]
    }
   ],
   "source": [
    "# Decision Tree Regressor\n",
    "\n",
    "model=DecisionTreeRegressor()\n",
    "reg(model, a, b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "659ffd8a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE Score is: 0.7244505911419071\n",
      "R2 Score is: 44.71333397380081\n",
      "Cross Validation Score: 41.25914954760421\n",
      "R2 Score - Cross Validation Score is 3.4541844261965977\n"
     ]
    }
   ],
   "source": [
    "# Random Forest Regressor\n",
    "\n",
    "model=RandomForestRegressor(max_depth=2, max_features=\"sqrt\")\n",
    "reg(model, a, b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "1f4c5383",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE Score is: 0.7108749896794594\n",
      "R2 Score is: 46.76597197095427\n",
      "Cross Validation Score: 41.710627948787845\n",
      "R2 Score - Cross Validation Score is 5.055344022166423\n"
     ]
    }
   ],
   "source": [
    "# K Neighbors Regressor\n",
    "\n",
    "KNeighborsRegressor(n_neighbors=2, algorithm='kd_tree')\n",
    "reg(model, a, b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "a2de40bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE Score is: 0.644871485777078\n",
      "R2 Score is: 56.19242557820677\n",
      "Cross Validation Score: 54.73476531377517\n",
      "R2 Score - Cross Validation Score is 1.4576602644316026\n"
     ]
    }
   ],
   "source": [
    "# Gradient Boosting Regressor\n",
    "\n",
    "model=GradientBoostingRegressor()\n",
    "reg(model, a, b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "d9a57cf9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE Score is: 0.659250870526919\n",
      "R2 Score is: 54.216996222308225\n",
      "Cross Validation Score: 47.31527233514678\n",
      "R2 Score - Cross Validation Score is 6.901723887161445\n"
     ]
    }
   ],
   "source": [
    "# Ada Boost Regressor\n",
    "\n",
    "model=AdaBoostRegressor(n_estimators=300, learning_rate=1.05, random_state=42)\n",
    "reg(model, a, b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "ea8b64e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE Score is: 0.6563370241180059\n",
      "R2 Score is: 54.6208176721835\n",
      "Cross Validation Score: 54.14118230202298\n",
      "R2 Score - Cross Validation Score is 0.4796353701605156\n"
     ]
    }
   ],
   "source": [
    "# Extra Trees Regressor\n",
    "\n",
    "model=ExtraTreesRegressor(n_estimators=400, max_features='sqrt', n_jobs=6)\n",
    "reg(model, a, b)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18cd3d7f",
   "metadata": {},
   "source": [
    "# Hyper parameter tuning on the best Regression ML Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "e92ff6d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Choosing Extra Trees Regressor\n",
    "\n",
    "param = {'criterion' : ['mse', 'mae'],\n",
    "              'n_estimators' : [100, 200],\n",
    "              'min_samples_split' : [2, 3],\n",
    "              'random_state' : [42, 135],\n",
    "              'n_jobs' : [-1, 1]\n",
    "             }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "0c2b5678",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GridSearchCV(cv=9, estimator=ExtraTreesRegressor(),\n",
      "             param_grid={'criterion': ['mse', 'mae'],\n",
      "                         'min_samples_split': [2, 3],\n",
      "                         'n_estimators': [100, 200], 'n_jobs': [-1, 1],\n",
      "                         'random_state': [42, 135]})\n",
      "{'criterion': 'mse', 'min_samples_split': 3, 'n_estimators': 100, 'n_jobs': -1, 'random_state': 135}\n"
     ]
    }
   ],
   "source": [
    "GSCV = GridSearchCV(ExtraTreesRegressor(),param, cv=9)\n",
    "print(GSCV.fit(a_train,b_train))\n",
    "print(GSCV.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b03d0b20",
   "metadata": {},
   "source": [
    "# Obtaining the Best Parameters for Hyper parameters tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c28de8db",
   "metadata": {},
   "source": [
    "ExtraTreesRegressor is our best model and difference between CV and R2 score is minimum for this perticuler model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "f2842729",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE Score is: 0.656337024118006\n",
      "R2 Score is: 54.6208176721835\n",
      "Cross Validation Score: 54.83235911300521\n",
      "R2 Score - Cross Validation Score is -0.211541440821712\n"
     ]
    }
   ],
   "source": [
    "et= ExtraTreesRegressor(criterion='mse', min_samples_split= 3, n_estimators= 100, n_jobs= -1, random_state= 135)\n",
    "#n_estimatorsint, default=100 The number of trees in the forest.\n",
    "\n",
    "# Training the model\n",
    "et.fit(a_train, b_train)\n",
    "    \n",
    "# Predicting b_test\n",
    "pred_et= model.predict(a_test)\n",
    "    \n",
    "# RMSE - a lower RMSE score is better than a higher one\n",
    "rmse = mean_squared_error(b_test, pred_et, squared=False)\n",
    "print(\"RMSE Score is:\", rmse)\n",
    "    \n",
    "# R2 score\n",
    "r2 = r2_score(b_test, pred_et, multioutput='variance_weighted')*100\n",
    "print(\"R2 Score is:\", r2)\n",
    "    \n",
    " # Cross Validation Score\n",
    "cv_score = (cross_val_score(et, a, b, cv=9).mean())*100\n",
    "print(\"Cross Validation Score:\", cv_score)\n",
    "    \n",
    "# Result of r2 score minus cv score\n",
    "result = r2 - cv_score\n",
    "print(\"R2 Score - Cross Validation Score is\", result)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3e21f57",
   "metadata": {},
   "source": [
    "I have successfully incorporated the Hyper Parameter Tuning on my Final Regression Model and received the R2 score for it."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44aefbd1",
   "metadata": {},
   "source": [
    "# Saving ,Loading and Conclusion the best Classification ML model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "9a00bb0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "filename_1=\"capacity_mw.pkl\"\n",
    "pickle.dump(et,open(filename_1,\"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "a543ab3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "52.946300852091746\n"
     ]
    }
   ],
   "source": [
    "loaded_model=pickle.load(open(\"capacity_mw.pkl\",\"rb\"))\n",
    "result=loaded_model.score(a_test,b_test)\n",
    "print(result*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "1eb62d7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "conclusion=pd.DataFrame([loaded_model.predict(a_test)[:],pred_et[:]],index=[\"predicted\",\"original\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "67acf7ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>predicted</th>\n",
       "      <th>original</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.844375</td>\n",
       "      <td>0.970493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.468030</td>\n",
       "      <td>0.531302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.127463</td>\n",
       "      <td>0.108273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.912965</td>\n",
       "      <td>-0.993679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.335599</td>\n",
       "      <td>0.389161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>1.155862</td>\n",
       "      <td>1.120062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>-0.530792</td>\n",
       "      <td>-0.473228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>-0.688422</td>\n",
       "      <td>-0.970880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>-0.321167</td>\n",
       "      <td>-0.343643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>-0.084045</td>\n",
       "      <td>-0.171583</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>219 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     predicted  original\n",
       "0     0.844375  0.970493\n",
       "1     0.468030  0.531302\n",
       "2    -0.127463  0.108273\n",
       "3    -0.912965 -0.993679\n",
       "4     0.335599  0.389161\n",
       "..         ...       ...\n",
       "214   1.155862  1.120062\n",
       "215  -0.530792 -0.473228\n",
       "216  -0.688422 -0.970880\n",
       "217  -0.321167 -0.343643\n",
       "218  -0.084045 -0.171583\n",
       "\n",
       "[219 rows x 2 columns]"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conclusion.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b39dc840",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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