ime672 project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2c3794cc",
   "metadata": {},
   "source": [
    "# Crop Production In india "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "405e1099",
   "metadata": {},
   "outputs": [],
   "source": [
    "##Importing important libraries.\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "from sklearn.model_selection import cross_val_score, KFold\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import model_selection\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.ensemble import AdaBoostRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.ensemble import BaggingRegressor\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "498ab736",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State_Name</th>\n",
       "      <th>District_Name</th>\n",
       "      <th>Crop_Year</th>\n",
       "      <th>Season</th>\n",
       "      <th>Crop</th>\n",
       "      <th>Area</th>\n",
       "      <th>Production</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>NICOBARS</td>\n",
       "      <td>2000</td>\n",
       "      <td>Kharif</td>\n",
       "      <td>Arecanut</td>\n",
       "      <td>1254.0</td>\n",
       "      <td>2000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>NICOBARS</td>\n",
       "      <td>2000</td>\n",
       "      <td>Kharif</td>\n",
       "      <td>Other Kharif pulses</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>NICOBARS</td>\n",
       "      <td>2000</td>\n",
       "      <td>Kharif</td>\n",
       "      <td>Rice</td>\n",
       "      <td>102.0</td>\n",
       "      <td>321.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>NICOBARS</td>\n",
       "      <td>2000</td>\n",
       "      <td>Whole Year</td>\n",
       "      <td>Banana</td>\n",
       "      <td>176.0</td>\n",
       "      <td>641.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>NICOBARS</td>\n",
       "      <td>2000</td>\n",
       "      <td>Whole Year</td>\n",
       "      <td>Cashewnut</td>\n",
       "      <td>720.0</td>\n",
       "      <td>165.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    State_Name District_Name  Crop_Year       Season  \\\n",
       "0  Andaman and Nicobar Islands      NICOBARS       2000  Kharif        \n",
       "1  Andaman and Nicobar Islands      NICOBARS       2000  Kharif        \n",
       "2  Andaman and Nicobar Islands      NICOBARS       2000  Kharif        \n",
       "3  Andaman and Nicobar Islands      NICOBARS       2000  Whole Year    \n",
       "4  Andaman and Nicobar Islands      NICOBARS       2000  Whole Year    \n",
       "\n",
       "                  Crop    Area  Production  \n",
       "0             Arecanut  1254.0      2000.0  \n",
       "1  Other Kharif pulses     2.0         1.0  \n",
       "2                 Rice   102.0       321.0  \n",
       "3               Banana   176.0       641.0  \n",
       "4            Cashewnut   720.0       165.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Import Data\n",
    "df=pd.read_csv(\"crop_production.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0304fa3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(246091, 7)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape      "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64b246e6",
   "metadata": {},
   "source": [
    "#There are 246091 rows and 7 Columns of attribute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bbf03c02",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 246091 entries, 0 to 246090\n",
      "Data columns (total 7 columns):\n",
      " #   Column         Non-Null Count   Dtype  \n",
      "---  ------         --------------   -----  \n",
      " 0   State_Name     246091 non-null  object \n",
      " 1   District_Name  246091 non-null  object \n",
      " 2   Crop_Year      246091 non-null  int64  \n",
      " 3   Season         246091 non-null  object \n",
      " 4   Crop           246091 non-null  object \n",
      " 5   Area           246091 non-null  float64\n",
      " 6   Production     242361 non-null  float64\n",
      "dtypes: float64(2), int64(1), object(4)\n",
      "memory usage: 13.1+ MB\n"
     ]
    }
   ],
   "source": [
    " df.info()       "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d82f4e74",
   "metadata": {},
   "source": [
    "#There is 7 attributes, in which four (state_Name,District_Name,Season,Crop) are categorical and three are numerical attributes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bb186cdc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "State_Name        object\n",
       "District_Name     object\n",
       "Crop_Year          int64\n",
       "Season            object\n",
       "Crop              object\n",
       "Area             float64\n",
       "Production       float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2321decd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.State_Name.nunique() "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0f03a45",
   "metadata": {},
   "source": [
    " ##Total number of state is 33 including union territory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "01897a61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Andaman and Nicobar Islands', 'Andhra Pradesh',\n",
       "       'Arunachal Pradesh', 'Assam', 'Bihar', 'Chandigarh',\n",
       "       'Chhattisgarh', 'Dadra and Nagar Haveli', 'Goa', 'Gujarat',\n",
       "       'Haryana', 'Himachal Pradesh', 'Jammu and Kashmir ', 'Jharkhand',\n",
       "       'Karnataka', 'Kerala', 'Madhya Pradesh', 'Maharashtra', 'Manipur',\n",
       "       'Meghalaya', 'Mizoram', 'Nagaland', 'Odisha', 'Puducherry',\n",
       "       'Punjab', 'Rajasthan', 'Sikkim', 'Tamil Nadu', 'Telangana ',\n",
       "       'Tripura', 'Uttar Pradesh', 'Uttarakhand', 'West Bengal'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.State_Name.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "75681c19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Kharif     ', 'Whole Year ', 'Autumn     ', 'Rabi       ',\n",
       "       'Summer     ', 'Winter     '], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Season.unique() ##Total no of season "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f4036bd",
   "metadata": {},
   "source": [
    "##There are trailing white spaces in some entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9fe5e0e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "124"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Crop.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "78c27c22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Kharif', 'Whole Year', 'Autumn', 'Rabi', 'Summer', 'Winter'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To remove white space at the end of strings\n",
    "df[\"Season\"] = df[\"Season\"].str.rstrip()\n",
    "df[\"State_Name\"] = df[\"State_Name\"].str.rstrip()\n",
    "df[\"Crop\"] = df[\"Crop\"].str.rstrip() \n",
    "df[\"District_Name\"] = df[\"District_Name\"].str.rstrip() \n",
    "df.Season.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a2b19b37",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19\n",
      "[2000 2001 2002 2003 2004 2005 2006 2010 1997 1998 1999 2007 2008 2009\n",
      " 2011 2012 2013 2014 2015]\n"
     ]
    }
   ],
   "source": [
    "print(df.Crop_Year.nunique())\n",
    "print(df.Crop_Year.unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "368b8492",
   "metadata": {},
   "source": [
    "#The Data has been collected from the year 1997 to 2015 (19 yrs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6ef75566",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "646"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.District_Name.nunique() ## No. of districts "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "952696dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "State_Name          0\n",
       "District_Name       0\n",
       "Crop_Year           0\n",
       "Season              0\n",
       "Crop                0\n",
       "Area                0\n",
       "Production       3730\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()    ##Checking for missing values ,there is 3730 vaues in production are missing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6c093306",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Production'] = df['Production'].replace(0, np.nan)\n",
    "df['Production']=df['Production'].fillna(df.groupby('Crop')['Production'].transform('mean'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "99d0c4db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "State_Name         0\n",
       "District_Name      0\n",
       "Crop_Year          0\n",
       "Season             0\n",
       "Crop               0\n",
       "Area               0\n",
       "Production       663\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5b8b6dae",
   "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>Crop</th>\n",
       "      <th>Production</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>Turnip</td>\n",
       "      <td>484.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>other fibres</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>Yam</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>Water Melon</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Beet Root</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Ber</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ash Gourd</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Plums</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>Snak Guard</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Ribed Guard</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>Pump Kin</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>Peas  (vegetable)</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>Pear</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>Peach</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Other Dry Fruit</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>Other Citrus Fruit</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Cucumber</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Litchi</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Lab-Lab</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Crop  Production\n",
       "115              Turnip       484.0\n",
       "121        other fibres         0.0\n",
       "120                 Yam         0.0\n",
       "118         Water Melon         0.0\n",
       "11            Beet Root         0.0\n",
       "12                  Ber         0.0\n",
       "4             Ash Gourd         0.0\n",
       "84                Plums         0.0\n",
       "104          Snak Guard         0.0\n",
       "94          Ribed Guard         0.0\n",
       "89             Pump Kin         0.0\n",
       "80    Peas  (vegetable)         0.0\n",
       "79                 Pear         0.0\n",
       "78                Peach         0.0\n",
       "72      Other Dry Fruit         0.0\n",
       "71   Other Citrus Fruit         0.0\n",
       "35             Cucumber         0.0\n",
       "58               Litchi         0.0\n",
       "54              Lab-Lab         0.0\n",
       "0                 Apple         0.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Crop_Prod = df.groupby('Crop')['Production'].sum().reset_index().sort_values(by='Production',ascending=False)\n",
    "Crop_Prod.tail(20) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c19203e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "##the percentage of missing values is very less compare to whole data so we can drop the missing values rows\n",
    "df.dropna(subset=[\"Production\"],axis=0,inplace=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bc078c6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "State_Name       0\n",
       "District_Name    0\n",
       "Crop_Year        0\n",
       "Season           0\n",
       "Crop             0\n",
       "Area             0\n",
       "Production       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum() \n",
    "## now there is  no missing values after dropping the missing values rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1281d6c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 245428 entries, 0 to 246090\n",
      "Data columns (total 7 columns):\n",
      " #   Column         Non-Null Count   Dtype  \n",
      "---  ------         --------------   -----  \n",
      " 0   State_Name     245428 non-null  object \n",
      " 1   District_Name  245428 non-null  object \n",
      " 2   Crop_Year      245428 non-null  int64  \n",
      " 3   Season         245428 non-null  object \n",
      " 4   Crop           245428 non-null  object \n",
      " 5   Area           245428 non-null  float64\n",
      " 6   Production     245428 non-null  float64\n",
      "dtypes: float64(2), int64(1), object(4)\n",
      "memory usage: 15.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info() "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47d8ec96",
   "metadata": {},
   "source": [
    " #rows reduced to 238838 after dropping."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e3e42316",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BIJAPUR      945\n",
       "TUMKUR       936\n",
       "BELGAUM      925\n",
       "HASSAN       895\n",
       "BELLARY      887\n",
       "            ... \n",
       "HYDERABAD      8\n",
       "KHUNTI         6\n",
       "RAMGARH        6\n",
       "MUMBAI         1\n",
       "NAMSAI         1\n",
       "Name: District_Name, Length: 646, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.District_Name.value_counts() \n",
    "# shows the no. of data from each districts"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47f70cdf",
   "metadata": {},
   "source": [
    "# Add Rainfall Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7750ee55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATE</th>\n",
       "      <th>YEAR</th>\n",
       "      <th>ANNUAL RAINFALL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ARUNACHAL PRADESH</td>\n",
       "      <td>1997</td>\n",
       "      <td>2274.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ARUNACHAL PRADESH</td>\n",
       "      <td>1998</td>\n",
       "      <td>3616.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ARUNACHAL PRADESH</td>\n",
       "      <td>1999</td>\n",
       "      <td>2316.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ARUNACHAL PRADESH</td>\n",
       "      <td>2000</td>\n",
       "      <td>2596.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ARUNACHAL PRADESH</td>\n",
       "      <td>2001</td>\n",
       "      <td>2036.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               STATE  YEAR  ANNUAL RAINFALL\n",
       "0  ARUNACHAL PRADESH  1997           2274.9\n",
       "1  ARUNACHAL PRADESH  1998           3616.7\n",
       "2  ARUNACHAL PRADESH  1999           2316.6\n",
       "3  ARUNACHAL PRADESH  2000           2596.8\n",
       "4  ARUNACHAL PRADESH  2001           2036.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import rainfall data\n",
    "raindata=pd.read_csv(\"rainfall in india.csv\")\n",
    "raindata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "293ef740",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007,\n",
       "       2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015], dtype=int64)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raindata[\"YEAR\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "321c028d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "STATE              0\n",
       "YEAR               0\n",
       "ANNUAL RAINFALL    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raindata.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4bfed72c",
   "metadata": {},
   "outputs": [],
   "source": [
    "raindata.rename(columns={\"STATE\":\"State_Name\",\"YEAR\":\"Crop_Year\",\"ANNUAL RAINFALL\":\"Rainfall\"},inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "948e5433",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['ARUNACHAL PRADESH' 'ORISSA' 'JHARKHAND' 'BIHAR' 'UTTARAKHAND' 'PUNJAB'\n",
      " 'HIMACHAL PRADESH' 'JAMMU & KASHMIR' 'CHHATTISGARH' 'TELANGANA'\n",
      " 'TAMIL NADU' 'KERALA' 'LAKSHADWEEP' 'ANDAMAN & NICOBAR ISLANDS' 'ASSAM'\n",
      " 'MEGHALAYA' 'HARYANA ' 'CHANDIGARH' 'NAGALAND' 'MANIPUR' 'MIZORAM '\n",
      " 'TRIPURA' 'PUDUCHERRY' 'UTTAR PRADESH' 'RAJASTHAN' 'MADHYAPRADESH'\n",
      " 'SIKKIM' 'GUJARAT' 'GOA' 'ANDHRA PRADESH' 'KARNATAKA' 'WEST BENGAL'\n",
      " 'MAHARASHTRA' 'DADRA NAGAR HAVELI']\n",
      "['Andaman and Nicobar Islands' 'Andhra Pradesh' 'Arunachal Pradesh'\n",
      " 'Assam' 'Bihar' 'Chandigarh' 'Chhattisgarh' 'Dadra and Nagar Haveli'\n",
      " 'Goa' 'Gujarat' 'Haryana' 'Himachal Pradesh' 'Jammu and Kashmir'\n",
      " 'Jharkhand' 'Karnataka' 'Kerala' 'Madhya Pradesh' 'Maharashtra' 'Manipur'\n",
      " 'Meghalaya' 'Mizoram' 'Nagaland' 'Odisha' 'Puducherry' 'Punjab'\n",
      " 'Rajasthan' 'Sikkim' 'Tamil Nadu' 'Telangana' 'Tripura' 'Uttar Pradesh'\n",
      " 'Uttarakhand' 'West Bengal']\n"
     ]
    }
   ],
   "source": [
    "print(raindata.State_Name.unique())\n",
    "print(df.State_Name.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "bcce0a2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Arunachal Pradesh', 'Odisha', 'Jharkhand', 'Bihar', 'Uttarakhand',\n",
       "       'Punjab', 'Himachal Pradesh', 'Jammu and Kashmir', 'Chhattisgarh',\n",
       "       'Telangana', 'Tamil Nadu', 'Kerala', 'LAKSHADWEEP',\n",
       "       'Andaman and Nicobar Islands', 'Assam', 'Meghalaya', 'Haryana',\n",
       "       'Chandigarh', 'Nagaland', 'Manipur', 'Mizoram', 'Tripura',\n",
       "       'Puducherry', 'Uttar Pradesh', 'Rajasthan', 'Madhya Pradesh',\n",
       "       'Sikkim', 'Gujarat', 'Goa', 'Andhra Pradesh', 'Karnataka',\n",
       "       'West Bengal', 'Maharashtra', 'Dadra and Nagar Haveli'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raindata[\"State_Name\"].replace({'ARUNACHAL PRADESH':'Arunachal Pradesh',\n",
    "                    'ORISSA':'Odisha', 'JHARKHAND': 'Jharkhand','BIHAR':'Bihar',\n",
    "                    'UTTARAKHAND':'Uttarakhand',  'PUNJAB':'Punjab','HIMACHAL PRADESH':'Himachal Pradesh',\n",
    "                    'JAMMU & KASHMIR':'Jammu and Kashmir', 'CHHATTISGARH':'Chhattisgarh', 'TELANGANA':'Telangana',\n",
    "                    'TAMIL NADU':\"Tamil Nadu\", 'KERALA':'Kerala','ANDAMAN & NICOBAR ISLANDS': 'Andaman and Nicobar Islands',\n",
    "                    'ASSAM':'Assam','MEGHALAYA':'Meghalaya', 'HARYANA ':'Haryana', 'CHANDIGARH':'Chandigarh',\n",
    "                    'NAGALAND':'Nagaland', 'MANIPUR':'Manipur', 'MIZORAM ':'Mizoram', 'TRIPURA':'Tripura', \n",
    "                    'PUDUCHERRY':'Puducherry', 'UTTAR PRADESH': 'Uttar Pradesh', 'RAJASTHAN':'Rajasthan',\n",
    "                    'MADHYAPRADESH':'Madhya Pradesh','SIKKIM':'Sikkim', 'GUJARAT':'Gujarat', 'GOA': 'Goa',\n",
    "                    'ANDHRA PRADESH':'Andhra Pradesh', 'KARNATAKA':'Karnataka', 'WEST BENGAL':'West Bengal',\n",
    "                    'MAHARASHTRA':'Maharashtra','DADRA NAGAR HAVELI':'Dadra and Nagar Haveli'}, inplace=True)\n",
    "raindata.State_Name.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "39e09f1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(raindata.State_Name)==set(df.State_Name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "32e4ad64",
   "metadata": {},
   "outputs": [],
   "source": [
    "raindata=raindata[raindata.State_Name!=\"LAKSHADWEEP\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "04d39c0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(raindata.State_Name)==set(df.State_Name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "127cca01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State_Name</th>\n",
       "      <th>Crop_Year</th>\n",
       "      <th>Rainfall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>636</th>\n",
       "      <td>Dadra and Nagar Haveli</td>\n",
       "      <td>2006</td>\n",
       "      <td>1089.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>637</th>\n",
       "      <td>Dadra and Nagar Haveli</td>\n",
       "      <td>2007</td>\n",
       "      <td>1034.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>638</th>\n",
       "      <td>Dadra and Nagar Haveli</td>\n",
       "      <td>2008</td>\n",
       "      <td>759.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>639</th>\n",
       "      <td>Dadra and Nagar Haveli</td>\n",
       "      <td>2009</td>\n",
       "      <td>619.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>640</th>\n",
       "      <td>Dadra and Nagar Haveli</td>\n",
       "      <td>2010</td>\n",
       "      <td>1104.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>641</th>\n",
       "      <td>Dadra and Nagar Haveli</td>\n",
       "      <td>2011</td>\n",
       "      <td>816.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>642</th>\n",
       "      <td>Dadra and Nagar Haveli</td>\n",
       "      <td>2012</td>\n",
       "      <td>518.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>643</th>\n",
       "      <td>Dadra and Nagar Haveli</td>\n",
       "      <td>2013</td>\n",
       "      <td>900.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>644</th>\n",
       "      <td>Dadra and Nagar Haveli</td>\n",
       "      <td>2014</td>\n",
       "      <td>544.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>645</th>\n",
       "      <td>Dadra and Nagar Haveli</td>\n",
       "      <td>2015</td>\n",
       "      <td>532.30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 State_Name  Crop_Year  Rainfall\n",
       "636  Dadra and Nagar Haveli       2006   1089.90\n",
       "637  Dadra and Nagar Haveli       2007   1034.80\n",
       "638  Dadra and Nagar Haveli       2008    759.50\n",
       "639  Dadra and Nagar Haveli       2009    619.25\n",
       "640  Dadra and Nagar Haveli       2010   1104.30\n",
       "641  Dadra and Nagar Haveli       2011    816.50\n",
       "642  Dadra and Nagar Haveli       2012    518.90\n",
       "643  Dadra and Nagar Haveli       2013    900.20\n",
       "644  Dadra and Nagar Haveli       2014    544.80\n",
       "645  Dadra and Nagar Haveli       2015    532.30"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raindata.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "529a672b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.merge(df, raindata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "af464502",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State_Name</th>\n",
       "      <th>District_Name</th>\n",
       "      <th>Crop_Year</th>\n",
       "      <th>Season</th>\n",
       "      <th>Crop</th>\n",
       "      <th>Area</th>\n",
       "      <th>Production</th>\n",
       "      <th>Rainfall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>NICOBARS</td>\n",
       "      <td>2000</td>\n",
       "      <td>Kharif</td>\n",
       "      <td>Arecanut</td>\n",
       "      <td>1254.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>2763.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>NICOBARS</td>\n",
       "      <td>2000</td>\n",
       "      <td>Kharif</td>\n",
       "      <td>Other Kharif pulses</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2763.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>NICOBARS</td>\n",
       "      <td>2000</td>\n",
       "      <td>Kharif</td>\n",
       "      <td>Rice</td>\n",
       "      <td>102.0</td>\n",
       "      <td>321.0</td>\n",
       "      <td>2763.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>NICOBARS</td>\n",
       "      <td>2000</td>\n",
       "      <td>Whole Year</td>\n",
       "      <td>Banana</td>\n",
       "      <td>176.0</td>\n",
       "      <td>641.0</td>\n",
       "      <td>2763.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>NICOBARS</td>\n",
       "      <td>2000</td>\n",
       "      <td>Whole Year</td>\n",
       "      <td>Cashewnut</td>\n",
       "      <td>720.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>2763.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    State_Name District_Name  Crop_Year      Season  \\\n",
       "0  Andaman and Nicobar Islands      NICOBARS       2000      Kharif   \n",
       "1  Andaman and Nicobar Islands      NICOBARS       2000      Kharif   \n",
       "2  Andaman and Nicobar Islands      NICOBARS       2000      Kharif   \n",
       "3  Andaman and Nicobar Islands      NICOBARS       2000  Whole Year   \n",
       "4  Andaman and Nicobar Islands      NICOBARS       2000  Whole Year   \n",
       "\n",
       "                  Crop    Area  Production  Rainfall  \n",
       "0             Arecanut  1254.0      2000.0    2763.2  \n",
       "1  Other Kharif pulses     2.0         1.0    2763.2  \n",
       "2                 Rice   102.0       321.0    2763.2  \n",
       "3               Banana   176.0       641.0    2763.2  \n",
       "4            Cashewnut   720.0       165.0    2763.2  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "4af1be64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State_Name</th>\n",
       "      <th>District_Name</th>\n",
       "      <th>Crop_Year</th>\n",
       "      <th>Season</th>\n",
       "      <th>Area</th>\n",
       "      <th>Rainfall</th>\n",
       "      <th>Crop</th>\n",
       "      <th>Production</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>NICOBARS</td>\n",
       "      <td>2000</td>\n",
       "      <td>Kharif</td>\n",
       "      <td>1254.0</td>\n",
       "      <td>2763.2</td>\n",
       "      <td>Arecanut</td>\n",
       "      <td>2000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>NICOBARS</td>\n",
       "      <td>2000</td>\n",
       "      <td>Kharif</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2763.2</td>\n",
       "      <td>Other Kharif pulses</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>NICOBARS</td>\n",
       "      <td>2000</td>\n",
       "      <td>Kharif</td>\n",
       "      <td>102.0</td>\n",
       "      <td>2763.2</td>\n",
       "      <td>Rice</td>\n",
       "      <td>321.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>NICOBARS</td>\n",
       "      <td>2000</td>\n",
       "      <td>Whole Year</td>\n",
       "      <td>176.0</td>\n",
       "      <td>2763.2</td>\n",
       "      <td>Banana</td>\n",
       "      <td>641.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>NICOBARS</td>\n",
       "      <td>2000</td>\n",
       "      <td>Whole Year</td>\n",
       "      <td>720.0</td>\n",
       "      <td>2763.2</td>\n",
       "      <td>Cashewnut</td>\n",
       "      <td>165.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    State_Name District_Name  Crop_Year      Season    Area  \\\n",
       "0  Andaman and Nicobar Islands      NICOBARS       2000      Kharif  1254.0   \n",
       "1  Andaman and Nicobar Islands      NICOBARS       2000      Kharif     2.0   \n",
       "2  Andaman and Nicobar Islands      NICOBARS       2000      Kharif   102.0   \n",
       "3  Andaman and Nicobar Islands      NICOBARS       2000  Whole Year   176.0   \n",
       "4  Andaman and Nicobar Islands      NICOBARS       2000  Whole Year   720.0   \n",
       "\n",
       "   Rainfall                 Crop  Production  \n",
       "0    2763.2             Arecanut      2000.0  \n",
       "1    2763.2  Other Kharif pulses         1.0  \n",
       "2    2763.2                 Rice       321.0  \n",
       "3    2763.2               Banana       641.0  \n",
       "4    2763.2            Cashewnut       165.0  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=df[[\"State_Name\",\"District_Name\",\"Crop_Year\",\"Season\",\"Area\",\"Rainfall\",\"Crop\",\"Production\"]]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "abb2cf95",
   "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>Crop_Year</th>\n",
       "      <th>Area</th>\n",
       "      <th>Rainfall</th>\n",
       "      <th>Production</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>245428.000000</td>\n",
       "      <td>2.454280e+05</td>\n",
       "      <td>245428.000000</td>\n",
       "      <td>2.454280e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2005.649588</td>\n",
       "      <td>1.203498e+04</td>\n",
       "      <td>1270.235522</td>\n",
       "      <td>5.833894e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.955392</td>\n",
       "      <td>5.058780e+04</td>\n",
       "      <td>638.338616</td>\n",
       "      <td>1.697353e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1997.000000</td>\n",
       "      <td>4.000000e-02</td>\n",
       "      <td>188.850000</td>\n",
       "      <td>1.000000e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2002.000000</td>\n",
       "      <td>8.200000e+01</td>\n",
       "      <td>807.650000</td>\n",
       "      <td>1.000000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2006.000000</td>\n",
       "      <td>5.890000e+02</td>\n",
       "      <td>1144.000000</td>\n",
       "      <td>8.540000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2010.000000</td>\n",
       "      <td>4.410000e+03</td>\n",
       "      <td>1686.466667</td>\n",
       "      <td>7.492250e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2015.000000</td>\n",
       "      <td>8.580100e+06</td>\n",
       "      <td>3825.200000</td>\n",
       "      <td>1.250800e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Crop_Year          Area       Rainfall    Production\n",
       "count  245428.000000  2.454280e+05  245428.000000  2.454280e+05\n",
       "mean     2005.649588  1.203498e+04    1270.235522  5.833894e+05\n",
       "std         4.955392  5.058780e+04     638.338616  1.697353e+07\n",
       "min      1997.000000  4.000000e-02     188.850000  1.000000e-02\n",
       "25%      2002.000000  8.200000e+01     807.650000  1.000000e+02\n",
       "50%      2006.000000  5.890000e+02    1144.000000  8.540000e+02\n",
       "75%      2010.000000  4.410000e+03    1686.466667  7.492250e+03\n",
       "max      2015.000000  8.580100e+06    3825.200000  1.250800e+09"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "341fbc16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 35,
     "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)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdc74305",
   "metadata": {},
   "source": [
    "# State"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "5dd20cd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Uttar Pradesh     33306\n",
       "Madhya Pradesh    22904\n",
       "Karnataka         21122\n",
       "Bihar             18885\n",
       "Assam             14628\n",
       "Odisha            13575\n",
       "Tamil Nadu        13086\n",
       "Maharashtra       12628\n",
       "Rajasthan         12478\n",
       "Chhattisgarh      10709\n",
       "West Bengal        9613\n",
       "Andhra Pradesh     9599\n",
       "Gujarat            8436\n",
       "Haryana            5875\n",
       "Telangana          5621\n",
       "Name: State_Name, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.State_Name.value_counts().head(15)  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43ea254e",
   "metadata": {},
   "source": [
    "#This shows that we have more data from rich agriculture state like UP,MP etc.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "71b02cf5",
   "metadata": {},
   "outputs": [],
   "source": [
    "state_prod = df.groupby('State_Name')['Production'].sum().reset_index().sort_values(by='Production',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "156cbe67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Production', ylabel='State_Name'>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "plt.xticks(rotation=90)\n",
    "sns.barplot(data=state_prod, y= \"State_Name\",x=\"Production\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "1ec72095",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State_Name</th>\n",
       "      <th>Production</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Kerala</td>\n",
       "      <td>9.790763e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>1.732612e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Tamil Nadu</td>\n",
       "      <td>1.235086e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Uttar Pradesh</td>\n",
       "      <td>3.236034e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Assam</td>\n",
       "      <td>2.112198e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        State_Name    Production\n",
       "15          Kerala  9.790763e+10\n",
       "1   Andhra Pradesh  1.732612e+10\n",
       "27      Tamil Nadu  1.235086e+10\n",
       "30   Uttar Pradesh  3.236034e+09\n",
       "3            Assam  2.112198e+09"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state_prod.head(5) # Top 5 states with most production over the years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "9e83c309",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State_Name</th>\n",
       "      <th>Production</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Manipur</td>\n",
       "      <td>5.257139e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Sikkim</td>\n",
       "      <td>2.435735e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Mizoram</td>\n",
       "      <td>2.373936e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Dadra and Nagar Haveli</td>\n",
       "      <td>1.847871e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Chandigarh</td>\n",
       "      <td>6.923093e+04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                State_Name    Production\n",
       "18                 Manipur  5.257139e+06\n",
       "26                  Sikkim  2.435735e+06\n",
       "20                 Mizoram  2.373936e+06\n",
       "7   Dadra and Nagar Haveli  1.847871e+06\n",
       "5               Chandigarh  6.923093e+04"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state_prod.tail(5) # Top 5 states with least production over the years"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f6c3d1f",
   "metadata": {},
   "source": [
    "#We can see that Kerela has the highest production, \n",
    "Also we can see that the top 3 states with highest production over the years are from South India"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae57506c",
   "metadata": {},
   "source": [
    "# Crop Year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "3bbc2be3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2003    16946\n",
      "2002    16391\n",
      "2008    14550\n",
      "2007    14526\n",
      "2006    14328\n",
      "2004    14117\n",
      "2009    14116\n",
      "2011    14071\n",
      "2010    14065\n",
      "2005    13799\n",
      "2000    13658\n",
      "2013    13636\n",
      "2012    13396\n",
      "2001    13361\n",
      "1999    12515\n",
      "1998    11533\n",
      "2014    10959\n",
      "1997     8899\n",
      "2015      562\n",
      "Name: Crop_Year, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df.Crop_Year.value_counts())  ## count of data in each year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "37c46c65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cBwAAAJ0Q0gEAAKATprsD0D2fhwYANgohHYDu+Tw0ALBRmO4OAAAAnRDSAQAAoBNCOgAAAHRCSAcAAIBOCOkAAADQCSEdAAAAOiGkAwAAQCeEdAAAAOiEkA4AAACdENIBAACgE0I6AAAAdEJIBwAAgE4I6QAAANCJFYX0qvquqnpzVV1VVbdU1a2jt3EXCQAAABvBASvc7sIkxyf57SRXJ2njKggAAAA2qpWG9IckeWhr7RNjrAVYZ7Zs2ZK5ubkkyebNm7N169YpVwTAtNk3AOzdSkP6l5LUOAsBbms9/BMzNzeXbdu2TbsMADpi3wCwdysN6WcmeVVVPau19oXxlQPM808MAABsPCsN6W9NcnCSz1XVjiTfHF3ZWjtytYUBAADARrOaI+kAAADAGK0opLfW3jjuQgAA2Lv1cL4SAPZupUfSU1X7Jzk5yb0zuATbp5O8u7XmOukzbj38A7AexgAACzlfCcD6t6KQXlX3SPJXSe6S5N8yONP7vZJ8qaoe11r73PhKZK2th38A1sMYAACAjWe/FW73uiSfS3LX1tr9Wmv3TXJ8kiuG6wAAAIBlWul094cneWBr7dr5Ba21r1fVbyS5dCyVAQAAwAaz0pC+K8lhiyw/NMnNKy8HAACASdv2mk+MpZ9bt998m/vj6ve7XnDCWPqZRSud7v6XSf5nVf1YfccDk5yf5N3jKw8AAAA2jpWG9Odn8Jn0jyTZObxdmuSzSbaMpzQAAADYWFZ6nfRvJPnZqrpnku/P4Ozun26tfXaMtQEAAMCGstIj6UmS1tq/t9Yuaq29e6UBvarOqKorqmpnVV1WVQ/dR/unVtU/VdWOqrq6qt5QVUetbAQAAADQjyUfSa+qc5L8ZmvtxuH9PWqtvXCJfT4pyWuTnJHBdPlnJbm4qn6gtfbFRdo/JMmbkrwgyUUZXKf9/CQXJDllqWMBAACAHi1nuvt9k9xu5P44vDDJ61trFwwfn1lVj0nynCQvXqT9A5N8obU2fy32K6rqD5P82pjqAQAAgKlZckhvrT1ysfsrVVUHJrl/klctWHVJkgftYbMPJ3llVZ2U5OIkRyd5YpL37OV5NiXZNLJosUvHAQAAwNSt6DPpVfXHVbVb2K2qQ6rqj5fYzZ2S7J9k24Ll25Ics9gGrbUPJ3lqkrdmcD32ryb5RpLn7eV5XpzkupHbl5dYHwAAAKyplZ447heTHLTI8oOSPG2ZfbUFj2uRZYMVVT+Q5HVJfiuDo/CPTXK3DD6XvidnJzli5HbcMusDAACANbGsS7BV1eEZhOhKclhV7RxZvX+Sk5Jcs8Tuvpbk1ux+1Pzo7H50fd6Lk1zaWvud4eNPVtWNST5UVS9trV29cIPW2q4ku0bGsMTyAAAAYG0t9zrp38jgKHdLcvki61uSly+lo9bazVV1WZITk/zFyKoTk7xrD5sdnOSWBctuHX6VvgEAAJhpyw3pj8wgDP9tkickuXZk3c1JrmytXbWM/s5J8uaq+liSjyR5ZpLjM5y+XlVnJ7lLa21+Cv1FSf6oqp6T5L1Jjs3gEm7/sMznBQAAgO4sK6S31j6YJFV1tyRfbK0t+tnxZfT31qo6KsnLMgjcn0pyUmvtymGTYzMI7fPtLxyesO65SX43gyP7f5vk11dTBwAAAPRguUfS5z0qyQ1J3j66sKpOTXJwa+2NS+2otXZekvP2sO60RZadm+Tc5RQLAAAAs2ClZ3f/jQxO/LbQNUlesvJyAAAAYONaaUj/7iRXLLL8yoxMTwcAAACWbqUh/ZokP7zI8h9J8vWVlwMAAAAb10o/k/5nSV5XVdcn+f+Gyx6eZOtwHQAAALBMKw3pL81gyvv7853rlu+X5E3xmXQAAABYkRWF9NbazUmeVFW/mcEU95uS/PPIpdMAAACAZVrpkfQkSWvt8iSXj6kWAAAA2NBWFNKr6o/3tr619vSVlQMAAAAb10qPpN9xwePbJfnBJHdI8rerKQgAAAA2qpV+Jv2Uhcuqar8k5yX5/GqLAgAAgI1opddJ301r7VtJXpPkBePqEwAAADaSsYX0obtnlSejAwAAgI1qpSeOO2fhoiTHJnlckjeutigAAADYiFZ61Pu+Cx5/K8lckhcl2euZ3wEAAIDFrfTEcY8cdyEAAACw0Y37M+kAAADACi35SHpVfTxJW0rb1tr9VlwRAAAAbFDLme7+zpH7t09yRpJPJ/nIcNkDk9wng2ulA6wLW7ZsydzcXJJk8+bN2bp165QrAgBgPVtySG+tvWL+flVdkOR1rbXfHG1TVa9IctfxlQcwXXNzc9m2bdu0y1gVbzQAAMyOlZ7d/dQkD1hk+Z8k+ViSp6+4IgDGaj280QDj4A0rAGbBSkP6TUkekuTfFyx/SJKdq6oIAGACvGEFwCxYaUh/bZI/qKr7J/nocNkDMziC/ltjqAsAAAA2nJVeJ/1VVfX5JFuS/Nxw8WeSnNZae9u4igMAAICNZKVH0jMM4wI5AAAAjMmKQ3qSDKe73zuD66d/urX28bFUBQAAABvQikJ6VR2d5M+SPCLJN5JUkiOq6gNJntxamxtXgQAAALBR7LfC7c5NcniS+7TWjmyt3THJDw6XvW5cxQEAAMBGstLp7o9N8pOttc/ML2itfbqqfjnJJWOpDAAAADaYlYb0/ZJ8c5Hl38zKj84DAED3tmzZkrm5wac7N2/enK1bt065ImA9WWlI/9skW6vqKa21q5Kkqu6S5DVJ3j+u4gAAoDdzc3PZtm3btMsA1qmVHvV+bpLDknyhqj5XVZ9NcsVw2fPGVRwAAABsJCs6kt5a+1KS+1XViUm+P4Ozu3+6tfY34ywOAAAANpJlh/SqOiDJziQntNbel+R9Y68KAAAANqBlT3dvrd2S5Mok+4+/HAAAANi4VvqZ9P+a5OyqOnKcxQAAAMBGttKzuz8/yT2SXFVVVya5cXRla+1+qy0MAAAANpqVhvR3JmkZnDAOAAAAGINlhfSqOjjJ7yQ5OcntMrgm+vNaa18bf2kAAACwsSz3M+mvSHJakvck+dMkP5nkD8ZcEwAAAGxIy53u/vgkz2it/VmSVNX/SnJpVe3fWrt17NUBAADABrLcI+l3TfKh+QettX9IckuSO4+zKAAAANiIlhvS909y84Jlt2TlJ6ADAAAAhpYbrivJhVW1a2TZ7ZOcX1Xfvgxba+3x4ygOAAAANpLlhvQ3LrLsT8ZRCAAAAGx0ywrprbXTJ1UIAAAAbHTL/Uw6AAAAMCFCOgAAAHTCWdkBAGCDOfTQIxe9D0yfkA4AABvME0/+rWmXAOyB6e4AAADQCSEdAAAAOiGkAwAAQCeEdAAAAOiEkA4AAACdENIBAACgE0I6AAAAdEJIBwAAgE4I6QAAANCJA6ZdAAAAsLH8wxuuWXUfu2649Tb3x9Fnkvzo6UePpR9YKUfSAQAAoBNCOgAAAHRCSAcAAIBOCOkAAADQCSEdAAAAOiGkAwAAQCdcgg0AAGAK7nj7Ixe9z8YmpAMAAEzBSx/48mmXQIdMdwcAAIBOTD2kV9UZVXVFVe2sqsuq6qH7aL+pql5ZVVdW1a6q+lxVPX2t6gUAAIBJmep096p6UpLXJjkjyaVJnpXk4qr6gdbaF/ew2duSfFeSZyT5bJKjY9o+AOzRT73jj1fdx84dN3z7/jU7bhhLn0nyl0/wPjsAjJp2uH1hkte31i4YPj6zqh6T5DlJXrywcVU9NsnDk3xva+3a4eIvrEWhAAAAMGlTm+5eVQcmuX+SSxasuiTJg/aw2c8k+ViSX6uqr1TV5VX16qo6aC/Ps6mqDp+/JTlsHPUDAADAuE3zSPqdkuyfZNuC5duSHLOHbb43yUOS7ExyyrCP85IcmWRP8+VenMRpEwEAAOje1E8cl6QteFyLLJu333DdU1tr/9Ba+6sMpsyftpej6WcnOWLkdtzqSwYAAIDxm+aR9K8luTW7HzU/OrsfXZ93dZKvtNauG1n2mQyC/XFJ/n3hBq21XUl2zT+uqlWUDAAAAJMztSPprbWbk1yW5MQFq05M8uE9bHZpkjtX1aEjy+6V5FtJvjz2IgEAAGANTXu6+zlJfqmqnl5V966q1yQ5Psn5SVJVZ1fVm0bavyXJ15O8oap+oKoeluR3kvxxa+2mtS4eAAAAxmmql2Brrb21qo5K8rIkxyb5VJKTWmtXDpscm0Fon29/Q1WdmOTcDM7y/vUMrpv+0jUtHIAN43H/+3dX3cfOHdu/fX/bju1j6TNJ3vP4F42lHwCgH9O+Tnpaa+dlcIb2xdadtsiyf83uU+QBAABg5k17ujsAAAAwNPUj6QCsX//xnc8bSz8377j22/e37bh2LP1efPK5q+4DAGDcHEkHAACATgjpAAAA0AkhHQAAADohpAMAAEAnhHQAAADohJAOAAAAnRDSAQAAoBOukw7s5s/e8Jix9HPjDbeM3N82ln6ffPp7V90HwDSc/Od/s+o+btix89v3r9mxcyx9Jsk7n/iTY+kHgNVzJB0AAAA6IaQDAABAJ4R0AAAA6ISQDgAAAJ1w4jgYs/df8Lix9LPzhp0j97eNpd+f+KX3rLoPAABgcoR0AACW5NR3fHLVfXxjx83fvj+34+ax9Jkkb3/CD4+lH4BpM90dAAAAOiGkAwAAQCeEdAAAAOiEkA4AAACdENIBAACgE0I6AAAAdEJIBwAAgE4I6QAAANAJIR0AAAA6IaQDAABAJw6YdgEw6l/O+5mx9PPN63eM3L9mLP3e54x3r7oPAACAvXEkHQAAADohpAMAAEAnhHQAAADohM+kryNf/YNXjKWfW6//xm3uj6PfY57z8lX3AQAAsN45kg4AAACdENIBAACgE0I6AAAAdEJIBwAAgE4I6QAAANAJIR0AAAA6IaQDAABAJ4R0AAAA6ISQDgAAAJ0Q0gEAAKATQjoAAAB04oBpFwAAsC8/9fY/X3UfO2/c8e3719y4Yyx9JslfnvrEsfQDAImQDtCtX/nzx46ln/+345sj97eNpd9XP/GvV90HAAC7E9KBdenc//WYsfSz/cZbRu5vG0u/z3vqe1fdBwAA65PPpAMAAEAnhHQAAADohJAOAAAAnRDSAQAAoBNCOgAAAHRCSAcAAIBOCOkAAADQCddJBwBgw3jlX1y96j6u23Hrbe6Po88k+S+nHDuWfoDZ5kg6AAAAdEJIBwAAgE4I6QAAANAJIR0AAAA6IaQDAABAJ5zdHQAAgBU58vZHLHqflRPSAQAAWJGzHvy8aZew7pjuDgAAAJ0Q0gEAAKATQjoAAAB0QkgHAACATgjpAAAA0AkhHQAAADohpAMAAEAnph7Sq+qMqrqiqnZW1WVV9dAlbvfgqrqlqj4x4RIBAABgTUw1pFfVk5K8Nskrk9w3yYeSXFxVx+9juyOSvCnJ+yddIwAAAKyVaR9Jf2GS17fWLmitfaa1dmaSLyV5zj62+8Mkb0nykQnXBwAAAGtmaiG9qg5Mcv8klyxYdUmSB+1lu9OT3D3JKyZXHQAAAKy9A6b43HdKsn+SbQuWb0tyzGIbVNU9k7wqyUNba7dU1T6fpKo2Jdk0suiwFVULAAAAEzbt6e5J0hY8rkWWpar2z2CK+8tba5cvo/8XJ7lu5PblFdYJAAAAEzXNkP61JLdm96PmR2f3o+vJ4Aj4A5L83vCs7rckeVmSHxk+ftQenufsJEeM3I4bR/EAAAAwblOb7t5au7mqLktyYpK/GFl1YpJ3LbLJ9iQ/tGDZGUkeleSJSa7Yw/PsSrJr/vFSpsgDAADANEzzM+lJck6SN1fVxzI4U/szkxyf5Pwkqaqzk9yltfa01tq3knxqdOOquibJztbapwIAAAAzbqohvbX21qo6KoNp68dmEMJPaq1dOWxybAahHQAAANa9aR9JT2vtvCTn7WHdafvY9qwkZ429KAAAAJiCHs7uDgAAAERIBwAAgG4I6QAAANAJIR0AAAA6IaQDAABAJ4R0AAAA6ISQDgAAAJ0Q0gEAAKATQjoAAAB04oBpFwAAALBcRxx81KL3YdYJ6QAAwMx59mPOmnYJMBFCOgCwT3XIwYveBwDGS0gHAPZp0+MfPe0SAGBDcOI4AAAA6ISQDgAAAJ0Q0gEAAKATQjoAAAB0QkgHAACATgjpAAAA0AmXYAOge3XoAWkj9wEA1iv/6QDQvds98W7TLgEAYE0I6QAAM2K/Qw7Lt0buA7D+COkAADPi4FN+YdolADBhThwHAAAAnRDSAQAAoBOmuwPAhNWhtx85O/3tp1oLANA3IR0AJmzT43982iUAADPCdHcAAADohJAOAAAAnRDSAQAAoBNCOgAAAHRCSAcAAIBOOLs7dOqwgyoZXrRpcB8AAFjvhHTo1NNO3DTtElbtkIMXvw8AACxOSAcm5mce408MAAAsh/+gAQBYM/sdcodF7wMwIKQDALBmDn/886ddAkDXnN0dAAAAOiGkAwAAQCeEdAAAAOiEkA4AAACdENIBAACgE0I6AAAAdEJIBwAAgE64TjoAsCHUoYcseh8AeiKkAwAbwqaTT5l2CQCwT0I6uznyoE2L3gcAAGCyhHR28/JHnjDtEgAAADYkJ44DAACATjiSzrp0h4Nq0fuwXAcdvPh9AACYBCGddenMRxw07RJYJx72U/5MAgCwdvz3CbDO3e6QStJG7gMA0CshHWCdu9cp/tQDAMwKJ44DAACATgjpAAAA0AkhHQAAADrhg4oAALAMmw49ctH7AOMgpAMAwDLc/wkvm3YJwDpmujsAAAB0QkgHAACATgjpAAAA0AkhHQAAADohpAMAAEAnhHQAAADohJAOAAAAnRDSAQAAoBNCOgAAAHRCSAcAAIBOTD2kV9UZVXVFVe2sqsuq6qF7afv4qnpfVc1V1faq+khVPWYt6wUAAIBJmWpIr6onJXltklcmuW+SDyW5uKqO38MmD0vyviQnJbl/kg8kuaiq7jv5agEAAGCyDpjy878wyetbaxcMH585PDL+nCQvXti4tXbmgkUvqaqfTfLTST4+yUIBAABg0qZ2JL2qDszgaPglC1ZdkuRBS+xjvySHJbl2L202VdXh87dhewAAAOjONKe73ynJ/km2LVi+LckxS+zjRUkOSfK2vbR5cZLrRm5fXl6ZAAAAsDamfuK4JG3B41pk2W6q6ilJzkrypNbaNXtpenaSI0Zux62sTAAAAJisaX4m/WtJbs3uR82Pzu5H129jeMK51yc5tbX2N3tr21rblWTXyLYrKhYAAAAmbWpH0ltrNye5LMmJC1admOTDe9pueAT9wiQ/11p7z8QKBAAAgDU27bO7n5PkzVX1sSQfSfLMJMcnOT9JqursJHdprT1t+PgpSd6UZEuSj1bV/FH4m1pr16118QAAADBOUw3prbW3VtVRSV6W5Ngkn0pyUmvtymGTYzMI7fOelUHNvz+8zXtjktMmXjAAAABM0LSPpKe1dl6S8/aw7rQFjx+xBiUBAADAVPRwdncAAAAgQjoAAAB0Q0gHAACATgjpAAAA0AkhHQAAADohpAMAAEAnhHQAAADohJAOAAAAnRDSAQAAoBNCOgAAAHRCSAcAAIBOCOkAAADQCSEdAAAAOiGkAwAAQCeEdAAAAOiEkA4AAACdENIBAACgE0I6AAAAdEJIBwAAgE4I6QAAANAJIR0AAAA6IaQDAABAJ4R0AAAA6ISQDgAAAJ04YNoFrDdbtmzJ3NxckmTz5s3ZunXrlCsCAABgVgjpYzY3N5dt27ZNuwwAAABmkOnuAAAA0AkhHQAAADohpAMAAEAnhHQAAADohJAOAAAAnRDSAQAAoBNCOgAAAHRCSAcAAIBOHDDtAnox9wd/MpZ+br3+xtvcH0e/m5/z86vuAwAAgP45kg4AAACdENIBAACgE0I6AAAAdEJIBwAAgE4I6QAAANAJIR0AAAA6IaQDAABAJ1wnfcyOOujgRe8DAADAvgjpY/aKRz5u2iUAAAAwo0x3BwAAgE4I6QAAANAJIR0AAAA6IaQDAABAJ4R0AAAA6ISQDgAAAJ0Q0gEAAKATQjoAAAB0QkgHAACATgjpAAAA0AkhHQAAADohpAMAAEAnhHQAAADohJAOAAAAnRDSAQAAoBNCOgAAAHRCSAcAAIBOCOkAAADQCSEdAAAAOnHAtAsAAACAadmyZUvm5uaSJJs3b87WrVunWo+QDgAAwIY1NzeXbdu2TbuMbzPdHQAAADohpAMAAEAnTHcHAABg5lxz7vvG0s+t22+6zf1x9Xv0805c0XZTP5JeVWdU1RVVtbOqLquqh+6j/cOH7XZW1eer6tlrVSsAAABM0lRDelU9Kclrk7wyyX2TfCjJxVV1/B7a3y3JXw3b3TfJf0vyuqp6wpoUDAAAwLpy1EGH507D21EHHT7tcqY+3f2FSV7fWrtg+PjMqnpMkuckefEi7Z+d5IuttTOHjz9TVQ9I8itJ3jHpYgEAAFhfznrYz0+7hNuYWkivqgOT3D/JqxasuiTJg/aw2Y8P1496b5JnVNXtWmvfXOR5NiXZNLLosCTZvn37bdpdf9NN6dWmBbXuyfU37ZxwJSt38BLHcMNNu72E3Vj4M7MnN66DMey46ZYJV7JySx3DTTtmfwy71sEYbtlx84QrWbmljuGbO/r927r0MfS7j1v6GHZMuJKVW/oYbpxwJSu39DHcMOFKVm6pY9i54/oJV7Jy27cfsqR2O7oew4FLanfDTT2P4fb7bHP9zn7rT5Lt2w/eZ5vrd/b7+5wkBy3hd/r6m/r9u5oktx8Zw1L/RiVJtdYmUc++n7jqzkm+kuTBrbUPjyx/SZJfbK193yLbXJ7kwtbafxtZ9qAklya5c2vt6kW2OSvJy8c/AgAAAFiW41prX9lbg2lPd0+She8S1CLL9tV+seXzzk5yzoJlRya5dknVrcxhSb6c5Lgkfb/NtmfG0Adj6IMx9GHWxzDr9SfG0Atj6IMx9MEY+mAMS3+Oq/bVaJoh/WtJbk1yzILlRyfZtodtvrqH9rck+fpiG7TWdiXZtWDx0ucarEDV/PsGub61NtHnmhRj6IMx9MEY+jDrY5j1+hNj6IUx9MEY+mAMfTCGJVtSv1M7u3tr7eYklyVZePG4E5N8ePctkiQfWaT9o5N8bLHPowMAAMAsmfZ10s9J8ktV9fSqundVvSbJ8UnOT5KqOruq3jTS/vwk311V5wzbPz3JM5K8es0rBwAAgDGb6mfSW2tvraqjkrwsybFJPpXkpNbalcMmx2YQ2ufbX1FVJyV5TZJfzmA+//Nba71dfm1Xkldk92n2s8QY+mAMfTCGPsz6GGa9/sQYemEMfTCGPhhDH4xhjKZ2dncAAADgtqY93R0AAAAYEtIBAACgE0I6AAAAdEJIBwAAgE4I6XtQVQ+rqouq6qqqalV18oL131VVFw7X76iqv66qey5oc/eq+ouqmquq7VX1tqr6rpH1jxj2vdjtP8zCGIZt7lVV76qqrw3bXFpVj1xt/Ws8hvtV1fuq6htV9fWq+p9VdegY6n9xVf3fqrq+qq6pqndW1fctaFNVddZwDDdV1d9V1X0WtNlUVecOv8c3VtW7q+q4BW3uWFVvrqrrhrc3V9UdZmwM/6WqPjx8Lb+x2trXegxV9T1V9fqqumLYx+eq6hVVdeCsjGHY5t1V9cWq2llVVw9/lu48S2NY0PYTNfj7ccIsjaGqvlC77xteNUtjGLZ7XFX9n2E/X6uq/z0rY6gJ7qfX+Gdp7PvpNa6/9330M4fLtw9/Nu6wyHPdsfreRy9lDL3vo/c6hpqNffRSXofe99H7HMNI21730Ut5HSayj54npO/ZIUn+KclzF66oqkryziTfm+Rnk9w3yZVJ/qaqDhm2OSTJJUlakkcleXCSA5NcVFXz3/cPZ3CZudHbBUm+kORjMzKGJHlPBpfze1SS+yf5RJK/rKpjZmEMwz9sf5Pks0l+LMljk9wnyYVjqP/hSX4/yQOTnJjB9+mS+fqGfi3JC4dj/A9JvprkfVV12Eib1yY5JcmTkzwkyaEZfI/3H2nzliQnDOt/7PD+m2dsDAcmeXuSPxhD3dMYw/dn8Hf1WRn8DL0gybOT/LcZGkOSfCDJf0ryfUmekOTuSf58xsYw739kcLnOcVnrMcxfonT+9l9naQxV9YQM/g69IcmPZPA3+C0zNIZJ7qfX8mdpEvvpNal/RvbRByf56+z9b33v++iljKH3ffS+xjAL++ilvA6976OXMoZ5ve6jlzqGSeyjB1prbvu4ZRDwTh55fK/hsvuMLNs/ydeT/NLw8aOT3Jrk8JE2dxxu95N7eJ7bJdmW5DdnZQxJ7jR8/NCRNocNl/3EjIzhmcPv+34jbU4YtrnHmMewedjvw4aPK8nVSX59pM2mJN9I8qzh4yOS3JzkSSNt7jwc12OGj+897PfHRto8cLjs+2ZhDAue47Qk3xj378FajmGkza8m+fyMj+Fnknwrye1maQxJ/mOSzyT5geHznDBLr0MGQfDMSf0eTHoMGfxz9OUkz5jVMSzyPJPcT0/qdViT/fQE6+96H71g+0cMt7/DguVd76OXMoYFbU5LZ/vo5Y5hpG03++hVjKGbffRyxpBO99FLHUMmvI92JH1lNg2/7pxf0Fq7NYMdzUNG2rQku0a225nBL9FDsrifyWBneuEYa92TcY3h6xn8gj2tqg6pqgMyeIdyW5LLJlb9d+qbrynJisewKcnNrbVvjbS5afh1T6/VSh0x/Hrt8OvdkhyTwdH+JElrbVeSDyZ50HDR/TP4x3C0zVVJPjXS5seTXNda+z8jbT6a5LqRNr2PYS2t5RiOGHmecVqTMVTVkUmemuTDrbVvjrH+ZIJjqMFHWv4oyS8k2THmukdN+nX49RpM7/1EDaaarnpa5iImNYb7JblLkm9V1ceH0zIvXjitsPMxLDTJ/fSkxrBW++lJ1d/7Pnopet9H92Ytx9DTPnrZOtxHL0nn++jlmNg+WkhfmX/NYFr12TX4jNGBVfUbGbzoxw7bfDTJjUn+e1UdPJxm8TsZfM+PXazTJM9I8t7W2pcmW36SMY2hDd5KOjGDqebXZxCAX5Dksa21b8zCGJL8bZJjqupXh33cMd+Z3rKn12rZhtPzz0ny9621Tw0Xz0813Lag+baRdcdk8A/K/9tHm2sWedprRtqs2oTHsCbWcgxVdfckz0ty/mrrXtDvxMdQVf+9qm7M4B/84zP4SMnYTHIMw74vTHJ+a20cHx1a1Bq8DlszmAL8yCS/l+TMJOeNpfihCY/he4dfz8pgCuBPJfl/ST44/MdyLNb479JE9tOTHMNa7Kcn/Br0vo9eit730d1YyzF0uI9eznP0uo9eat8Xpt999FJNdB8tpK/A8J2qJ2Qw3fraDN4BekSSizOYopXW2lySU5P8dJIbMni39Igk/zjfZlQNTpLymCSvn/gAMr4xDH8BzstgR/PQJD+a5F0ZfJ5sbDvPSY6htfYvSX4xyYuGfXw1yecz+IXd7bVahd9L8sNJnrLYcBY8rkWWLbSwzWLtl9LPckx6DGthTcYw/BzlXyd5e2vtghXUuTdrMYbfyeCf+vmPjLxp+Ps+LpMcw/OSHJ7k7NUUuAQTfR1aa69prX2wtfbJ4c/Qs5M8o6qOWkXNC01yDPP/Y7yytfaO1tplSU4frj91ZeUuaq1+pye5n57YGNZoPz2x+md4H72vPlbaz95MegxrYU3GMGP76MXM2j561Kzuo2/bwYT30UL6CrXWLmutnZDkDkmOba09NslRSa4YaXNJa+3uSY5OcqfW2i9kMPXvit17zOkZvBv27gmX/m1jGsOjMjg68uTW2qWttX9srZ2RwVS0X5yRMaS19pbW2jHD5UdlcORncxZ/rZatqs7NYJrkI1trXx5Z9dXh14Xv3h2d77zL99Uk80cP9tbmu7K7zdn93cIVWYMxTNxajWG48/9Ako9k8HnKsVmrMbTWvtZau7y19r4M3ik+KYPPUK7aGozhUcNad1XVLRmccCpJPlZVbxzDEKb1+/DR4dd7LL/i3a3BGK4efv30/MrhtMLPZ3DkZ9XW+HWYyH56jX4fJrafXovXoPN99FL0vo/uwlqNoeN99JJ1vI9eit730Ss11n30RD7ovt5uyW1PWLaHNvfM4J2sR++lzaMy+Cz09y1YXhn80/LqWRtDBkeob01y6IJ2/5bkJbMwhj20eXoG0+TvsMqaK4N38r6S5J57WH91kl8bWXZgFj+xzn8aaXNsFj9x3I+OtPmxjOGkNGs1hgV9npYxnpRmLceQwT+Rlyf50yT7z+IYFun7rsOfpUfMwhgyCIA/OHJ79LD+JyQ5bhbGsIfn/qnhOI6fhTFkcKRkZ0ZOHJfvnHjtmbMwhgX9jXU/vYavw0T201P+XehmH72g/SOy9xPHdbmPXsoYFrQ5LZ3to5c6hnS8j17u6zDStpt99FLGkM730at4Hcayj/52f+P64VxvtwwuAXJCvnMG0RcM7x8/XH/q8IWbv/zXF5K8Y0Efp2fwTtHdk/x8Bu/A/+4iz/UTw+e496yNIYMT6HwtyTsyuLzOvTKYgnNzkh+ZhTEM2zw3g5Mc3SvJL2cwpe75Y6j/vOEv/sMzeNdu/nbQSJtfH7Y5JYM/Vm/J4HIUh420+YMkXxr+rNw3yfszuITO/iNtLs7gcnUPHN4+meSiGRvD8cPX92UZfHZy/rU/dBbGkMFZif99uPwuo881K69DBlNhnzv8vn93Bp+1+lAG73RvmoUxLPK835OM58yxa/g6/Hi+8/fubhlcbucrSd41K2MYtnltBmd4f3QGlwu6IIOQfsdZGcOw3dj302v4szSR/fQa/xz1vo8+JoPf1V8a/pw8dPj4yJE2ve+jlzKG3vfRex1DZmMfva8xzMI+ep8/Swue93vS3z56X6/DxPbR365hXB2tt1u+887JwtuFw/XPz2CncnMGJy/77SQHLujjVRlMq7g5g3ftXpikFnmutyS5dFbHkOQBSd6bQfjdnsH0of84Y2N407D+XRnsRH9hTPUvVntLctpIm8pg6t7VGRx1+mCSH1zQz+2TnDuscUeSi5LcdUGbI5P8yfA12D68f4cZG8OFe3iuR8zCGDI4urDoc83K65DkhzI4UdPXh31ckcE/0XeZlTEs8rzfk/H9A7BWr8P9Mpg6940MpiX/67DPg2dlDMM2t0vy6gyC+fYk78vIZTNnYQzDdmPfT6/x6zD2/fQa19/7PvqsJfTT+z56KWO4cA9tHjELY8hs7KP3NYZZ2Efv82dpQfvvSX/76H29DhPbR8/favhEAAAAwJQ5cRwAAAB0QkgHAACATgjpAAAA0AkhHQAAADohpAMAAEAnhHQAAADohJAOAAAAnRDSAQAAoBNCOgDMmKo6pqrOrarPV9WuqvpSVV1UVT8xpXruVVU7qurnFizfr6o+XFV/MY26AGAWCekAMEOq6nuSXJbkUUl+LckPJXlskg8k+f09bHO7SdbUWrs8yW8kObeqjh1Z9aIk90jyrHE/56THBADTIqQDwGw5L0lL8qOttT9vrV3eWvuX1to5SR6YJFXVqurZVfWuqroxyUuHy59TVZ+rqpur6t+q6hdGOx5u95yquriqbqqqK6rq1CXWdW6STyT5o2Ff35/kt5I8s7V2TVWdXlWfqaqdVfWvVXXGguf+71V1+fCI/Oer6rdHg3hVnVVVn6iqp1fV55PsqqpawfcPALompAPAjKiqIzM4av77rbUbF65vrX1j5OErkrwrgyPtf1xVpyTZmuR3k/xgkj9M8oaqeuSCbn47yTuS/EiSP0nyp1V1733V1lprSU5P8tCq+s9JLkzy1tbaO4ePX5nkvyS5d5KXJPntqvrFkS6uT3Jakh9IsiXJf07yggVPc48k/ynJE5KcsK+aAGAW1WCfCgD0rqp+NMn/SfL41toeP+ddVS3Ja1trLxhZdmmSf2mtPXNk2duSHNJae9zIdue31p4z0uajSf6xtXabI997ee7Tk1yQ5CtJfqi1dl1VfTHJr7fW/nSk3UuTnNRae9Ae+vnVJE9qrT1g+PisDML9XVprc0upBQBmkSPpADA75qd3L+Ud9o8teHzvJJcuWHbpcPmojyzyeJ9H0ue11t6Q5OokrxsG9M1J7prk9VV1w/wtgyn4d5/frqqeWFV/X1VfHa7/7STHL+j+SgEdgPXugGkXAAAs2b9nENDvneSd+2i723T47B7ua5Fli1nutLtbhrfkOwcE/nMGswBG3ZokVfXAJH+W5OVJ3pvkuiRPzuDEc6MWGxMArCuOpAPAjGitXZtBiP3lqjpk4fqqusNeNv9MkocsWPag4fJRD1zk8b8ur9LvaK1ty2Dq+/e21j674HbFsNmDMzhK/srW2sdaa/+e5LtX+pwAMMscSQeA2XJGkg8n+YeqelmST2awPz8xyXOy56npv5PkbVX1j0nen+Snkzw+yU8uaHdqVX0syd8neWqSH03yjFXWfFaS11XV9iQXJ9mU5AFJ7jg8K/1nkxxfVU9O8n+TPC7JKat8TgCYSY6kA8AMGR59vl8G10X/3SSfSvK+JD+RQUjf03bvzOCs6b+a5F8yuHb56a21v1vQ9OUZTDX/ZJJfTPLU1tqnV1nzBUl+KYOzt/9zkg8O718xXP+uJK9J8nsZXMbtQRl8Jh0ANhxndwcAknz77O6nDAM9ADAFjqQDAABAJ4R0AGCfquolo5dQW3C7eNr1AcB6Ybo7ALBPVXVkkiP3sPqm1tpX1rIeAFivhHQAAADohOnuAAAA0AkhHQAAADohpAMAAEAnhHQAAADohJAOAAAAnRDSAQAAoBNCOgAAAHRCSAcAAIBO/P9WRtYjeZcSVwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8),dpi=100)\n",
    "sns.barplot(data=df,x='Crop_Year',y='Production');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "306e0dd9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x700 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9,7),dpi=100)\n",
    "sns.lineplot(data=df,x='Crop_Year',y='Production');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c10a010",
   "metadata": {},
   "source": [
    "#the period between 2010-2012 and then 2012-2013 happens to be the year which saw highest yield for the crops."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "9c83c2ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State_Name</th>\n",
       "      <th>Crop_Year</th>\n",
       "      <th>Production</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>Kerala</td>\n",
       "      <td>2005</td>\n",
       "      <td>6.330183e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>431</th>\n",
       "      <td>Tamil Nadu</td>\n",
       "      <td>2011</td>\n",
       "      <td>6.262207e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>Kerala</td>\n",
       "      <td>2006</td>\n",
       "      <td>6.058022e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>Kerala</td>\n",
       "      <td>2004</td>\n",
       "      <td>6.005739e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>Kerala</td>\n",
       "      <td>2014</td>\n",
       "      <td>5.955679e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>Kerala</td>\n",
       "      <td>2011</td>\n",
       "      <td>5.949664e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>Kerala</td>\n",
       "      <td>2013</td>\n",
       "      <td>5.928176e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>Kerala</td>\n",
       "      <td>2003</td>\n",
       "      <td>5.881859e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>Kerala</td>\n",
       "      <td>2008</td>\n",
       "      <td>5.809112e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>Kerala</td>\n",
       "      <td>2012</td>\n",
       "      <td>5.805751e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     State_Name  Crop_Year    Production\n",
       "226      Kerala       2005  6.330183e+09\n",
       "431  Tamil Nadu       2011  6.262207e+09\n",
       "227      Kerala       2006  6.058022e+09\n",
       "225      Kerala       2004  6.005739e+09\n",
       "235      Kerala       2014  5.955679e+09\n",
       "232      Kerala       2011  5.949664e+09\n",
       "234      Kerala       2013  5.928176e+09\n",
       "224      Kerala       2003  5.881859e+09\n",
       "229      Kerala       2008  5.809112e+09\n",
       "233      Kerala       2012  5.805751e+09"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  production from each state in a year \n",
    "prod_year= df.groupby(['State_Name','Crop_Year'])['Production'].sum().reset_index().sort_values(by='Production',ascending=False)\n",
    "prod_year.head(10)  # Top states with most production in a year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "f2ecfcab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State_Name</th>\n",
       "      <th>Crop_Year</th>\n",
       "      <th>Production</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Chandigarh</td>\n",
       "      <td>2002</td>\n",
       "      <td>5930.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>Chandigarh</td>\n",
       "      <td>2003</td>\n",
       "      <td>4605.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>Chandigarh</td>\n",
       "      <td>2005</td>\n",
       "      <td>3485.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>Chandigarh</td>\n",
       "      <td>2004</td>\n",
       "      <td>3455.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>Chandigarh</td>\n",
       "      <td>2006</td>\n",
       "      <td>3318.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>Chandigarh</td>\n",
       "      <td>2007</td>\n",
       "      <td>3295.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>Chandigarh</td>\n",
       "      <td>2008</td>\n",
       "      <td>3195.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>Chandigarh</td>\n",
       "      <td>2010</td>\n",
       "      <td>3109.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>Chandigarh</td>\n",
       "      <td>2009</td>\n",
       "      <td>3095.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>Manipur</td>\n",
       "      <td>1997</td>\n",
       "      <td>412.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     State_Name  Crop_Year  Production\n",
       "84   Chandigarh       2002      5930.0\n",
       "85   Chandigarh       2003      4605.0\n",
       "87   Chandigarh       2005      3485.0\n",
       "86   Chandigarh       2004      3455.0\n",
       "88   Chandigarh       2006      3318.0\n",
       "89   Chandigarh       2007      3295.0\n",
       "90   Chandigarh       2008      3195.0\n",
       "92   Chandigarh       2010      3109.0\n",
       "91   Chandigarh       2009      3095.0\n",
       "271     Manipur       1997       412.0"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prod_year.tail(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b0c956e",
   "metadata": {},
   "source": [
    "# Season"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "dbce2fe1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,5),dpi=100)\n",
    "sns.countplot(data=df,x='Season');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "a96b690a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,5),dpi=100)\n",
    "sns.barplot(data=df,x='Season',y='Production');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f94fdad0",
   "metadata": {},
   "source": [
    "#Whole Year season seems to have yeilded more crops compared to other seasons"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30940114",
   "metadata": {},
   "source": [
    "## Crops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "d37cf5a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rice                 15104\n",
      "Maize                13947\n",
      "Moong(Green Gram)    10318\n",
      "Urad                  9850\n",
      "Sesamum               9046\n",
      "Groundnut             8834\n",
      "Sugarcane             7921\n",
      "Wheat                 7899\n",
      "Rapeseed &Mustard     7592\n",
      "Arhar/Tur             7578\n",
      "Name: Crop, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df.Crop.value_counts().head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "162e8e0f",
   "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>Crop</th>\n",
       "      <th>Production</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Coconut</td>\n",
       "      <td>1.318414e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>Sugarcane</td>\n",
       "      <td>5.611484e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>Rice</td>\n",
       "      <td>1.608239e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>Wheat</td>\n",
       "      <td>1.336888e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>Potato</td>\n",
       "      <td>4.261174e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Cotton(lint)</td>\n",
       "      <td>3.084704e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Maize</td>\n",
       "      <td>2.778036e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Jute</td>\n",
       "      <td>1.873608e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Banana</td>\n",
       "      <td>1.510760e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>Soyabean</td>\n",
       "      <td>1.434901e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Crop    Production\n",
       "24        Coconut  1.318414e+11\n",
       "90      Sugarcane  5.611484e+09\n",
       "80           Rice  1.608239e+09\n",
       "102         Wheat  1.336888e+09\n",
       "74         Potato  4.261174e+08\n",
       "29   Cotton(lint)  3.084704e+08\n",
       "52          Maize  2.778036e+08\n",
       "44           Jute  1.873608e+08\n",
       "5          Banana  1.510760e+08\n",
       "89       Soyabean  1.434901e+08"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Crop_Prod = df.groupby('Crop')['Production'].sum().reset_index().sort_values(by='Production',ascending=False)\n",
    "Crop_Prod.head(10) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "1e967ec3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Production', ylabel='Crop'>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,5),dpi=100)\n",
    "sns.barplot(data=Crop_Prod.head(15),x='Production',y='Crop')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7531d30d",
   "metadata": {},
   "source": [
    "#From 1997 to 2015,Rice is produced more, but more yield is obtained from coconut"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2366eae9",
   "metadata": {},
   "source": [
    "#Coconut, Sugarcane, Rice, Wheat and Potato happen to be the top 5 crops yeilding more productions in India over the years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "5aa7b1de",
   "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>Crop</th>\n",
       "      <th>Production</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Kapas</td>\n",
       "      <td>8542.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Bean</td>\n",
       "      <td>6240.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Carrot</td>\n",
       "      <td>5992.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>Ricebean (nagadal)</td>\n",
       "      <td>5230.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Bottle Gourd</td>\n",
       "      <td>4566.545455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Bitter Gourd</td>\n",
       "      <td>3247.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Cond-spcs other</td>\n",
       "      <td>2260.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>Perilla</td>\n",
       "      <td>1410.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Jobster</td>\n",
       "      <td>1180.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>Turnip</td>\n",
       "      <td>484.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Crop   Production\n",
       "46               Kapas  8542.000000\n",
       "7                 Bean  6240.000000\n",
       "17              Carrot  5992.000000\n",
       "81  Ricebean (nagadal)  5230.000000\n",
       "13        Bottle Gourd  4566.545455\n",
       "10        Bitter Gourd  3247.600000\n",
       "27     Cond-spcs other  2260.400000\n",
       "70             Perilla  1410.000000\n",
       "42             Jobster  1180.000000\n",
       "99              Turnip   484.000000"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Crop_Prod.tail(10)  # worst performing crops"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0c35ada",
   "metadata": {},
   "source": [
    "# Analyze the highest produced crops in the country"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eadad409",
   "metadata": {},
   "source": [
    "# 1. Coconut"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "79814cb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "coconut_df = df[df['Crop'] == 'Coconut']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "d1b0c0ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Average Coconut production in states\n",
    "plt.figure(figsize=(8,5),dpi=100)\n",
    "sns.barplot(data=coconut_df,x='Production',y='State_Name');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "3041ee40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State_Name</th>\n",
       "      <th>Production</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Kerala</td>\n",
       "      <td>9.780304e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>1.680600e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tamil Nadu</td>\n",
       "      <td>1.120342e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Assam</td>\n",
       "      <td>1.954408e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Chhattisgarh</td>\n",
       "      <td>1.594052e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       State_Name    Production\n",
       "0          Kerala  9.780304e+10\n",
       "1  Andhra Pradesh  1.680600e+10\n",
       "2      Tamil Nadu  1.120342e+10\n",
       "3           Assam  1.954408e+09\n",
       "4    Chhattisgarh  1.594052e+09"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coconut_df.groupby('State_Name').sum()['Production'].nlargest().reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75136387",
   "metadata": {},
   "source": [
    "#Kerala followed by Andra Pradesh and Andaman & Nicobar islands has highest production of coconut in India"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "9b1a731a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Coconut Production vs Year\n",
    "plt.figure(figsize=(8,5),dpi=100)\n",
    "sns.lineplot(data=coconut_df,x='Crop_Year',y='Production');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53acf4b7",
   "metadata": {},
   "source": [
    "#We can see that, coconut production is increasing over years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "a52e82ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Season\n",
       "Whole Year    1.315756e+11\n",
       "Kharif        2.658019e+08\n",
       "Name: Production, dtype: float64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cocnut production vs Season\n",
    "coconut_df.groupby('Season')['Production'].sum().nlargest()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "4bded9ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 57,
     "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(coconut_df.corr(),annot=True,cmap = \"coolwarm\" )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0f9fc0c",
   "metadata": {},
   "source": [
    "#Whole Year season has the highest yield of Cococnut crops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "96d5e538",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=coconut_df,x='Area',y='Production');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "65220521",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='District_Name', ylabel='Production'>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# coconut production in districts \n",
    "plt.figure(figsize=(10,5),dpi=100)\n",
    "sns.barplot(data=coconut_df.groupby('District_Name').sum()['Production'].nlargest().reset_index(),x='District_Name',y='Production')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a275170",
   "metadata": {},
   "source": [
    "# 2. Sugar cane"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "69043b3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "sugar_df = df[df['Crop'] == 'Sugarcane']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "455e5f11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Production', ylabel='State_Name'>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x700 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Average Sugar cane production in states\n",
    "plt.figure(figsize=(8,7),dpi=100)\n",
    "sns.barplot(data=sugar_df,x='Production',y='State_Name')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "e6844e1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State_Name</th>\n",
       "      <th>Production</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Uttar Pradesh</td>\n",
       "      <td>2.202875e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>9.384860e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tamil Nadu</td>\n",
       "      <td>8.489137e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Karnataka</td>\n",
       "      <td>5.158455e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>2.267303e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       State_Name    Production\n",
       "0   Uttar Pradesh  2.202875e+09\n",
       "1     Maharashtra  9.384860e+08\n",
       "2      Tamil Nadu  8.489137e+08\n",
       "3       Karnataka  5.158455e+08\n",
       "4  Andhra Pradesh  2.267303e+08"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sugar_df.groupby('State_Name').sum()['Production'].nlargest().reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8eebafb",
   "metadata": {},
   "source": [
    "#UP, Maharashtra, Tamil Nadu, Karnataka and Andra Pradesh have the highest production of Sugarcane"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "9005cc65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='District_Name', ylabel='Production'>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Sugarcane production in districts \n",
    "plt.figure(figsize=(10,5),dpi=100)\n",
    "sns.barplot(data=sugar_df.groupby('District_Name').sum()['Production'].nlargest().reset_index(),x='District_Name',y='Production')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "21263bae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6),dpi=100)\n",
    "sns.lineplot(data=sugar_df,x='Crop_Year',y='Production');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d918abd6",
   "metadata": {},
   "source": [
    "#Production of sugarcane decreased drastically during 1998, and then it was stable till 2014"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "cb1d38ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Season', ylabel='Production'>"
      ]
     },
     "execution_count": 66,
     "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.barplot(data=sugar_df,x='Season',y='Production')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "555d2717",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 67,
     "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(sugar_df.corr(),annot=True,cmap = \"coolwarm\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "f05aaf42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=sugar_df,x='Area',y='Production');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d702c905",
   "metadata": {},
   "source": [
    "# 3. Rice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "dbd618a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "rice_df = df[df['Crop'] == 'Rice']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "edce8ce3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State_Name</th>\n",
       "      <th>Production</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>West Bengal</td>\n",
       "      <td>2.580928e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Uttar Pradesh</td>\n",
       "      <td>2.187934e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Punjab</td>\n",
       "      <td>1.797020e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>1.437425e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Odisha</td>\n",
       "      <td>1.199551e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       State_Name    Production\n",
       "0     West Bengal  2.580928e+08\n",
       "1   Uttar Pradesh  2.187934e+08\n",
       "2          Punjab  1.797020e+08\n",
       "3  Andhra Pradesh  1.437425e+08\n",
       "4          Odisha  1.199551e+08"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rice_df.groupby('State_Name').sum()['Production'].nlargest().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "d04c8709",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Production', ylabel='State_Name'>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x700 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Average Rice production in states\n",
    "plt.figure(figsize=(8,7),dpi=100)\n",
    "sns.barplot(data=rice_df,x='Production',y='State_Name')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "524d2d6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x540 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6),dpi=90)\n",
    "sns.barplot(data=rice_df,x='Crop_Year',y='Production');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "a041a088",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Season', ylabel='Production'>"
      ]
     },
     "execution_count": 73,
     "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.barplot(data=rice_df,x='Season',y='Production')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "c3f48004",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 74,
     "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(rice_df.corr(),annot=True,cmap = \"coolwarm\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "e4bb7d4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=rice_df,x='Area',y='Production');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5fd4463",
   "metadata": {},
   "source": [
    "# Zone-Wise Production - 1997-2014"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "85e05003",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "north_india = ['Jammu and Kashmir', 'Punjab', 'Himachal Pradesh', 'Haryana', 'Uttarakhand', 'Uttar Pradesh', 'Chandigarh']\n",
    "east_india = ['Bihar', 'Odisha', 'Jharkhand', 'West Bengal']\n",
    "south_india = ['Andhra Pradesh', 'Karnataka', 'Kerala' ,'Tamil Nadu', 'Telangana']\n",
    "west_india = ['Rajasthan' , 'Gujarat', 'Goa','Maharashtra']\n",
    "central_india = ['Madhya Pradesh', 'Chhattisgarh']\n",
    "north_east_india = ['Assam', 'Sikkim', 'Nagaland', 'Meghalaya', 'Manipur', 'Mizoram', 'Tripura', 'Arunachal Pradesh']\n",
    "ut_india = ['Andaman and Nicobar Islands', 'Dadra and Nagar Haveli', 'Puducherry']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "063e199c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Union Terr', 'South Zone', 'NE Zone', 'East Zone', 'North Zone',\n",
       "       'Central Zone', 'West Zone'], dtype=object)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_zonal_names(row):\n",
    "    if row['State_Name'].strip() in north_india:\n",
    "        val = 'North Zone'\n",
    "    elif row['State_Name'].strip()  in south_india:\n",
    "        val = 'South Zone'\n",
    "    elif row['State_Name'].strip()  in east_india:\n",
    "        val = 'East Zone'\n",
    "    elif row['State_Name'].strip()  in west_india:\n",
    "        val = 'West Zone'\n",
    "    elif row['State_Name'].strip()  in central_india:\n",
    "        val = 'Central Zone'\n",
    "    elif row['State_Name'].strip()  in north_east_india:\n",
    "        val = 'NE Zone'\n",
    "    elif row['State_Name'].strip()  in ut_india:\n",
    "        val = 'Union Terr'\n",
    "    else:\n",
    "        val = 'No Value'\n",
    "    return val\n",
    "\n",
    "df['Zones'] = df.apply(get_zonal_names, axis=1)\n",
    "df['Zones'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "9866b615",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "South Zone      53619\n",
       "North Zone      51468\n",
       "East Zone       43339\n",
       "West Zone       33750\n",
       "Central Zone    33613\n",
       "NE Zone         28297\n",
       "Union Terr       1342\n",
       "Name: Zones, dtype: int64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Zones.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "f2342786",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Zone-Wise Production: Total')"
      ]
     },
     "execution_count": 79,
     "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": [
    "fig, ax = plt.subplots(figsize=(15,10))\n",
    "sns.barplot(x=df.Zones.sort_values(ascending=True), y=df.Production)\n",
    "plt.yscale('log')\n",
    "plt.title('Zone-Wise Production: Total')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "730f95bb",
   "metadata": {},
   "source": [
    "# Different categories of Crops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "aeb4148e",
   "metadata": {},
   "outputs": [],
   "source": [
    "crop=df['Crop']\n",
    "def cat_crop(crop):\n",
    "    for i in ['Rice','Maize','Wheat','Barley','Varagu','Other Cereals & Millets','Ragi','Small millets','Bajra','Jowar', 'Paddy','Total foodgrain','Jobster']:\n",
    "        if crop==i:\n",
    "            return 'Cereal'\n",
    "    for i in ['Moong','Urad','Arhar/Tur','Peas & beans','Masoor',\n",
    "              'Other Kharif pulses','other misc. pulses','Ricebean (nagadal)',\n",
    "              'Rajmash Kholar','Lentil','Samai','Blackgram','Korra','Cowpea(Lobia)',\n",
    "              'Other  Rabi pulses','Other Kharif pulses','Peas & beans (Pulses)','Pulses total','Gram']:\n",
    "        if crop==i:\n",
    "            return 'Pulses'\n",
    "    for i in ['Peach','Apple','Litchi','Pear','Plums','Ber','Sapota','Lemon','Pome Granet',\n",
    "               'Other Citrus Fruit','Water Melon','Jack Fruit','Grapes','Pineapple','Orange',\n",
    "               'Pome Fruit','Citrus Fruit','Other Fresh Fruits','Mango','Papaya','Coconut','Banana']:\n",
    "        if crop==i:\n",
    "            return 'Fruits'\n",
    "    for i in ['Bean','Lab-Lab','Moth','Guar seed','Soyabean','Horse-gram']:\n",
    "        if crop==i:\n",
    "            return 'Beans'\n",
    "    for i in ['Turnip','Peas','Beet Root','Carrot','Yam','Ribed Guard','Ash Gourd ','Pump Kin','Redish','Snak Guard','Bottle Gourd',\n",
    "              'Bitter Gourd','Cucumber','Drum Stick','Cauliflower','Beans & Mutter(Vegetable)','Cabbage',\n",
    "              'Bhindi','Tomato','Brinjal','Khesari','Sweet potato','Potato','Onion','Tapioca','Colocosia']:\n",
    "              if crop==i:\n",
    "                return 'Vegetables'\n",
    "    for i in ['Perilla','Ginger','Cardamom','Black pepper','Dry ginger','Garlic','Coriander','Turmeric','Dry chillies','Cond-spcs other']:\n",
    "        if crop==i:\n",
    "            return 'spices'\n",
    "    for i in ['other fibres','Kapas','Jute & mesta','Jute','Mesta','Cotton(lint)','Sannhamp']:\n",
    "        if crop==i:\n",
    "            return 'fibres'\n",
    "    for i in ['Arcanut (Processed)','Atcanut (Raw)','Cashewnut Processed','Cashewnut Raw','Cashewnut','Arecanut','Groundnut']:\n",
    "        if crop==i:\n",
    "            return 'Nuts'\n",
    "    for i in ['other oilseeds','Safflower','Niger seed','Castor seed','Linseed','Sunflower','Rapeseed &Mustard','Sesamum','Oilseeds total']:\n",
    "        if crop==i:\n",
    "            return 'oilseeds'\n",
    "    for i in ['Tobacco','Coffee','Tea','Sugarcane','Rubber']:\n",
    "        if crop==i:\n",
    "            return 'Commercial'\n",
    "\n",
    "df['cat_crop']=df['Crop'].apply(cat_crop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "71c16298",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cereal        63800\n",
       "Pulses        41689\n",
       "oilseeds      34454\n",
       "Vegetables    22967\n",
       "spices        21986\n",
       "Nuts          11588\n",
       "Commercial    10716\n",
       "fibres        10185\n",
       "Beans          9307\n",
       "Fruits         8037\n",
       "Name: cat_crop, dtype: int64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"cat_crop\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "010cd0cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "plt.tick_params(labelsize=10)\n",
    "df.groupby(\"cat_crop\")[\"Production\"].agg(\"count\").plot.bar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "50d3ba3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BcAAAAO6E1fym4ZYM9zHOujnJXjs4/Um5/T2FX01yTpJHVdUZSdLdx4/DXpbkXkk+Nb4/K8nLZ6bd3nAAAADuhOpe2S19VfW+JG/r7tfO9PvFJE/u7qPWuHzrrqp6pesA2PWc88id7vC1bo4695x5FwEA2EBVle6+w08rLlhNTePzk7yvqn4uyWeTPDDJQ5M8enVFBAAAYLNa8T2N3f3xJIcleW+GZqr/K8mDu/uja1w2AAAA5mw1NY3p7kuS/MYalwUAAIBNZsWhsapeNDWsu19654oDAADAZrKamsb/sOj9N4/9/vLOFwcAAIDNZMWhsbsfv7hfVT0hycPXpEQAAABsGit+EM6EtyV5yhrNCwAAgE1irULjMUmuX6N5AQAAsEms5kE4VyfpmV53S3JTkl9Yq0IBAACwOazmQTiPW/T++iSf6e7r7nxxAAAA2ExW8yCcc9ajIAAAAGw+OxQaq+pNOzJedx9754oDAADAZrKjD8K5dqa7JcmTkhyY5MYk90nyxCQ3r0cBAQAAmJ8dqmns7v+y8HdV/WmSn+3ud830+7EkahkBAAB2Mav5yY3HJPmLRf3+MskP3fniAAAAsJmsJjT+W+5Yq/j0JBfe2cIAAACwuazmJzdOSPJnVfUrGYLi/ZPcL8lPrWG5AAAA2ARW85Mb51bVA5L8RJJvTnJpknd399VrXTgAAADmazU1jenua5L8YVXdu7uvXNsiAQAAsFms+J7Gqrp7Vb22qm5IcnlV3VBVZ1TVN6xD+QAAAJij1TwI51VJvi3JDya57/j6rUl+aw3LBQAAwCawmuapP5nkO7v7qvH95VV1dJL/k+SZa1YyAAAA5m41NY2V5JZF/W4Z+wMAALALWU1ofFeSP62qh1TVAVX1vUn+OMmfr23RAAAAmLfVhMbnJrkoyfuTfCHJuUkuTvK8NSwXAAAAm8CK7mmsqrskeVqSZyc5LskBSa7o7l6HsgEAADBnK6pp7O6bk5zS3V/twRcFRgAAgF3Xapqn/lVVPXrNSwIAAMCms5qf3LguydlV9d4M9zbe+iTV7n7uWhUMAACA+VtNaPymJH8y/r3/GpYFAACATWaHQ2NVPSTJ2Unum+Tfkvxkd39yfYoFAADAZrCSexp/M8nbknxnkncm+Y11KREAAACbxkqap35Xkh/u7q9V1QuT/Ms6lQkAAIBNYiU1jXt299eSpLtvSLL3+hQJAACAzWIlNY13rapfnnm/96L36e5T16ZYAAAAbAYrCY0fSfL4mff/e9H7TiI0AgAA7EJ2ODR296PWsRwAAABsQiu5pxEAAIDdjNAIAADAJKERAACASUIjAAAAk4RGAAAAJgmNAAAATJpLaKyqPavqtKq6auxeXVVL/vxHVV2/qLupqj4xM/zMqvraonEetnFLAwAAsOuaV03jSUmOSHLY2B2Z5IVLjdjd+8x2ST6V5K2LRjt90XgfXs/CAwAA7C7mFRqPTXJyd1/W3ZclOSXJcdubqKoemuTBSc5c3+IBAACQzCE0VtU9kxyU5PyZ3ucnOaSq9t/O5McleU93X7qo/1PHZq4XVNXzqsq9mgAAAGtgHuFqn/H1mpl+C3/vOzVRVd09yROSvGHRoFOTPCjJARlC5XPGbmo+W6uqF7oVlRwAAGA3M4/QeP34OluruPD3dctMd3SSG5K8e7Znd5/X3Vd0983d/ZEkr0hyzNRMuntrd9dCt/LiAwAA7D42PDR299VJLk5y+Ezvw5Ns6+5rl5n055P8fnd/fTsfccudKiAAAAC3mte9f29OcmJVHVhVB2Z4curiZqe3qqoHJXl4kjctMezoqtqvBg9J8oIkb1+ncgMAAOxWlvxtxA3wsiT3yvDzGUlyVpKXJ0lVnZEk3X38zPjHJXl/d39miXmdkOR1GZblkiSnJ3nV+hQbAABg91Ldu/ezYKqqd/d1ACTnPPKoeRdh0zjq3HPmXQQAYANVVZZ73oufpgAAAGCS0AgAAMAkoREAAIBJQiMAAACThEYAAAAmCY0AAABMEhoBAACYJDQCAAAwSWgEAABgktAIAADAJKERAACASUIjAAAAk4RGAAAAJgmNAAAATBIaAQAAmCQ0AgAAMEloBAAAYJLQCAAAwCShEQAAgElCIwAAAJOERgAAACYJjQAAAEwSGgEAAJgkNAIAADBJaAQAAGCS0AgAAMAkoREAAIBJQiMAAACThEYAAAAmCY0AAABMEhoBAACYJDQCAAAwSWgEAABgktAIAADAJKERAACASUIjAAAAk4RGAAAAJgmNAAAATBIaAQAAmCQ0AgAAMEloBAAAYJLQCAAAwCShEQAAgElzCY1VtWdVnVZVV43dq6tqy8S4Z1bV16rq+pnuYauZFwAAACszr5rGk5IckeSwsTsyyQuXGf/07t5npvvwnZgXAAAAO2heofHYJCd392XdfVmSU5IctwnmBQAAwIwND41Vdc8kByU5f6b3+UkOqar9JyZ76tj09IKqel5V7XEn5gUAAMAOmkdN4z7j6zUz/Rb+3neJ8U9N8qAkB2SoQXzO2K1mXqmqrVXVC90Kyg0AALDbmUdovH58na0JXPj7usUjd/d53X1Fd9/c3R9J8ookx6xmXuP8tnZ3LXSrWgIAAIDdxIaHxu6+OsnFSQ6f6X14km3dfe0OzOKWNZwXAAAAy5jXg3DenOTEqjqwqg7M8LTTNyw1YlUdXVX71eAhSV6Q5O2rmRcAAAArM6/fM3xZknsl+dT4/qwkL0+SqjojSbr7+HHYCUlel6GslyQ5PcmrdmReAAAA3DnVvXs/C6aqendfB0ByziOPmncRNo2jzj1n3kUAADZQVWW5573Mq3kqAAAAOwGhEQAAgElCIwAAAJOERgAAACYJjQAAAEwSGgEAAJgkNAIAADBJaAQAAGCS0AgAAMAkoREAAIBJQiMAAACThEYAAAAmCY0AAABMEhoBAACYJDQCAAAwSWgEAABgktAIAADAJKERAACASUIjAAAAk4RGAAAAJgmNAAAATBIaAQAAmCQ0AgAAMEloBAAAYJLQCAAAwCShEQAAgElCIwAAAJOERgAAACYJjQAAAEwSGgEAAJgkNAIAADBJaAQAAGCS0AgAAMAkoREAAIBJQiMAAACThEYAAAAmCY0AAABMEhoBAACYJDQCAAAwSWgEAABgktAIAADAJKERAACASUIjAAAAk+YSGqtqz6o6raquGrtXV9WWJcbbq6peX1X/WlXXVdWnq+rYReOcWVVfq6rrZ7qHbdzSAAAA7LrmVdN4UpIjkhw2dkcmeeES421JclmSRyfZL8nTk7yqqh6zaLzTu3ufme7D61ZyAACA3ci8QuOxSU7u7su6+7IkpyQ5bvFI3f2V7n5Rd3+uBx9J8rcZAicAAADrbMNDY1XdM8lBSc6f6X1+kkOqav/tTLt3kocm+cSiQU8dm7leUFXPqyr3agIAAKyBeYSrfcbXa2b6Lfy979REVVVJ3pDks0neMTPo1CQPSnJAhtrK54zd1Hy2VlUvdCstPAAAwO5kHqHx+vF1tlZx4e/rlppgDIyvyRAOH9fdtywM6+7zuvuK7r55bL76iiTHTH14d2/t7lro7syCAAAA7Oo2PDR299VJLk5y+Ezvw5Ns6+5rF48/Bsbfy9As9TFLjbPILdsZDgAAwA6a171/b05yYlUdWFUHZnhy6hsmxj0tySOS/NAYOG+nqo6uqv1q8JAkL0jy9vUqOAAAwO7kDr+NuEFeluReST41vj8rycuTpKrOSJLuPr6q7p/kWUluTHLhUOmYJHlLdx8//n1CktdlWJZLkpye5FUbsAwAAAC7vOrevZ8FU1W9u68DIDnnkUfNuwibxlHnnjPvIgAAG6iqstzzXvw0BQAAAJOERgAAACYJjQAAAEwSGgEAAJgkNAIAADBJaAQAAGCS0AgAAMAkoREAAIBJQiMAAACThEYAAAAmCY0AAABMEhoBAACYJDQCAAAwSWgEAABgktAIAADAJKERAACASUIjAAAAk4RGAAAAJgmNAAAATBIaAQAAmCQ0AgAAMEloBAAAYJLQCAAAwCShEQAAgElCIwAAAJOERgAAACYJjQAAAEwSGgEAAJgkNAIAADBJaAQAAGCS0AgAAMAkoREAAIBJW+ZdAABg87rxxhuzbdu2W98ffPDB2WuvveZYIgA2mtAIAEzatm1bnvGMZ9z6/rWvfW0e+MAHzrFEAGw0zVMBAACYpKYRADa5U57ys3P77Otu+vrt3r/hxP+affec3+nDiW/507l9NsDuSk0jAAAAk4RGAAAAJmmeCgBMuvuWu+R7773v7d4DsHsRGgGASXepmus9jADMn+apAAAATBIaAQAAmDSX0FhVe1bVaVV11di9uqqWbPuyvXFXMi8AAABWZl41jSclOSLJYWN3ZJIXrnLclcwLAACAFZhXaDw2ycndfVl3X5bklCTHrXLclcwLAACAFdjwZpxVdc8kByU5f6b3+UkOqar9u/vaHR03Q+jdoXnBrugRr37EvIuwKXzwlz447yIAwJ32tj956LyLsGkc/XN/P+8iMKO6e2M/sOrgJBclOaC7rxz7HZDki0kO7u6Ld3TcJLWj85qZ59YkL163BQQAANjJdHdNDZvHA2OuH1/3T3LlzN9Jct0Kx91jO8PvoLu3Jtm6wjLPXVX1cv9Idpx1ubasz7Vlfa4d63JtWZ9ry/pcW9bn2rEu19ausj43/J7G7r46ycVJDp/pfXiSbYubk25v3JXMCwAAgJWb14Nw3pzkxKo6sKoOzPC00zesctyVzAsAAIAVmNfvGb4syb2SfGp8f1aSlydJVZ2RJN19/PbG3cHhu4qXzLsAuxDrcm1Zn2vL+lw71uXasj7XlvW5tqzPtWNdrq1dYn1u+INwAAAA2HnMq3kqAAAAOwGhEQAAgElCIwAAAJOERnZ5VfV3VfUr8y4Hy6uqrqrD512OzaKqzqiqV867HDuTqnphVf3RvMvByq1ke6+qp1fV+etcpLmrqkdV1TXLDN8l1kNVHVlVF8+8n8t3dlWdXVVbN/pz10NVHVBVf1NVX66qm6rqQzPD/q2qHjfH4rGTEhrnYDwg3lxV3zXT7x7jSfOhOzD9mVX1O+tZxs1oXG83VtX1VXV1VZ1TVd8773LtzKrqiKp6z7g+r6mqf6yq51fVXeddtp3Vou10oXvWSufT3cd396+N8zx0PD7cY80LvAvp7pd39xPnXY61tLvso7Pb+3oa1+VpS/Tfr6puqKrvX8fPdrK+hO5+f3cfNO9y7GJ+McnNSe7R3Xt298PnXaCd0aLv8+uq6oKq+rl5l2tehMb5uTrJr8+7EDuhX+vufZJ8c5Lzkpw93+LsvKrqx5O8J8lfJfnW7r5HkmOSPDjD+l3JvOb18z2b1a919z4z3emzA60vdsRa7qPztom2+TckeVJV7bWo/xOTXJbk7za8RLD2HpDkgu6+5c7MpKruUlW1RmXaWS2cd+6X5PlJzqqq+8+5THMhNM7P6UkeXlWPXDxgcU3ibC1kVf1ykicnedZ45eOCcZwnV9Vnxyshl1TVf9+oBZmH7v6/Sd6Y5L5V9X9mm7JU1eFVteRvyVTVN1bV/6yqq8ar9h9f2Pmras+qemlVfa6qvlRV76yq+47DqqpeWVVfGJt7fGY8odspjV8CpyZ5ZXf/TndfmSTd/enufnp3X1hV31JVf15VV1TVhVV1UlXtMU7/9Ko6v6peUlVfSPLHY/8nVNUnxnX70ap6+MxnPrmqPjluoxdV1ct2ly+jcZ9+Y1W9raq+nOSZy+3nM9MsDP/78fXicb9/8nLb8q6gqp47bifXjTU0Pz+z3b183Ecvqpla3KraWlVnz7w/sKreUlWXjuvo3Kq62zjsm6rqrHHYpVX1OwtBYjOs2x3cRx9SVR8cy/hPVfXEmem3jvvvGVV1bVX9a1V9f1U9vqr+pYaay1Nmxp/dp68cj3XHVNUjxv322nEb3mNmmu+uqr8d19O/VNUvLPr8d1XVa6rqqiSvrKo9quqXq+rT4//1s1X1I+P4i/eHhf/bl8f1v1Y1gO9M8vUkj1vU/z8neVOSH6yqvx/X6QVV9ZMzZdprXJ9XjevzuEX7bM0s3zU11FJ8+zjsT5IckuSPxn34jLH/b9RwfL1u/B/eoRajqn6pqi4b/ycvGbeNO6iqfarqtHG/+GJV/UFV7T9T9jeN/9trx//phrbUqar71HAMvGIs4ylVtaWWaYa73L5Yy3xnj8Mn9/Fx+M+M2+21VfX6zPx2+XKfu9mN29rTctt54nF1x2bMh1XVeeP+9VeL1ltX1QlV9ckkNyTZp5Y/H3hAVf31uB6vquGYdPeNWt6N0oN3J7kmyYOS7R4D/0NVfWAcdkVV/VFV3Wtm+N9V1a+P6//68f/xnTPD7/AduIGLuyShcX6uSvIbSV6xkom6+9QkZyU5fazBOKyqviHJmUmO6+59kxyW5C/XuLybynhA+vkkFyb50gom/a8ZvhgOSnKvJMcluW4cdkqSRyQ5IsNV/M8kees47IeSPCnJd3f3fkkePQ7fWX1rhiuRS97/VcOJ9fuS/E2S+yU5MskTMpxYLfiODCdfhyT5T1X12CS/leTpSb4xQ036n88cJK9K8tMZrtb9ZIbmM09ay4Xa5J6Y4ULHPcbXlXjo+HrQuN+fleW35Z1aVX1bkpOTPGY8pn1fbgvO35GkM+yjxyR5RS198W2P3BYQDkty7yQvTHLLeNL9ziRfSPLAJN+Z5N8nOWmcfDOs2+3to/fIcJx/a5IDkjwzyeur6hEzo/1wkr/OsD+eleQtGcLSv89wnPuvVfXdM+MfluGE6MAk/z3J65I8N8lRGWo3f3ycPlV1YJL/leQ14+c/LslLquoHZ+b3I0n+d5JvGud3QpJfyXDhc78kP5jhGL6U9yX59gzr/61J/rSq9p0Yd4d1901J/jDJsQv9qurBSR6S5ANJ/iTJCzKss2ck+cOqetA46knjeIclOTzJ4xfN/pkZtpWfyLC9vSPDMfCu3f1zSS5K8sRxHz5+nOYfk3xvhuPCS8fPe8DMPPdN8t1JviXJo8ZyP3Vi8d40lvu7Mmw7eyZZaIr7tAz/9weOn/XTGbb/jfQ/ktw0lu3IDNvM87czzaq+s7e3j1fVt47l+S/jfD+eYXvdkc/d1MZt7dbzxAzNVBf7+QzfvwdmWEdnLRr+pCSPybCf3pLlzwdOSfIvGbb5+yT51QzH3V1KDRe9firJ3kn+YQeOgbdkOJbcJ8P31v1yx3P+p47j3CPJx5K8evys5b4D50ZonK/fSXL/Wpt7HG5K8u1VtV93X9PdH12DeW5Gvz5ekfx8kv8vQ/hYiZsyfAF8a3ff3N3nd/dV4xfMs5I8t7sv6+6vZfhyeURVHTxOt3eGq3N7dvdF3b0zh8YDxtdLJob/eJKru/u3u/tr3X1Rkt/N7UPetUlOGYffkOTZSX6zu8/r7lu6+x1JPp3ksUnS3e/p7s+MV+vOz3Ay/Kg1X7LN4dfHq9PXjNvrNyR5b3f/1bhubliDz1hyW16D+W4GNyepDPvb3br78u7+xDjsK0m2jtvdhzOc7Cx1Ev29GYLOM7v76u7+end/oLtvzHDi/61JfrW7b+juLyV5eW7bvjfDut3ePvpjSa7o7ld3903dfU6Gk+CnzYxzXnf/aXffPA67b5Jf7+6vdPcFGQLLbGi8ctznv55hve6X5PXd/aXuviTJOTPj/6ck53b328Z19Mkkb87tjxGf7O4zx3V/Q4ZQtbW7Pz4eBy7q7k8ttXDd/ebuvnZctt/McL7yXUuNuwpvTPLo8dieDEHsr5IcneTM7v6bcT/9QJJ3jf0zLtsrxu+Ia5O8ZNF8n53kRd392XGZT01ytwwnfEvq7rO6+4vjOnxrhmPm7P1ne2RoHndDd386Qwj8T4vnU1UHJPmZJCeM5wBfSfKiJMdU1V0ybNP7ZgjiNR6Lt+3Y6rrzqup+SX4gyfO6+/ruvjBD2Hj6diZd7Xf29vbxJyR5X3f/+fi/OiPJZ7f3uWuwKjaL1/TQauGGDMH9UVU1e1/pb3T3pePx8rFZ/nzgpgyh/dBxf/3Q+P/YVSycd34lw4Wgk7v7imznGNjd/zh+59zU3Zcn+f9zx3OeP+zufxiPub+f5HvG/st9B86N0DhH3f3VDF86L09ylzsxn69kuLL5U0m2jdXh63Yz/5z9t+6+R3cf2N0/soqd6DeTvD/J22po6vO7Y63avTOc2J87c6L/hSRfS3Jwd/9tkhcneVmSK6vq7YuuBu9srhxf7zcx/NAk37Eo+Lwqw1XJBZf07e+XODTJyxdNc/jCZ1TVD1fVh2psHpXk+AzrfVe0sJ3eo4f70L6SoZZhLU1tyzu97v5chvBzQpLLq+q9dduTdS/tobZowYVZeju+f4Zt9KtLDDs0w5Xdq2a21T/NcEU42Rzrdnv76EFJ/m1Rv8+P/RfM1iTdMNFvn5n3ly/8MXNhY2r8Q5M8dtH+/su5/b2Wi7f5++f2J+ZLGq/on1JD89Uvj/PeP2t0vOjuf8pw1f5pNdxr+ZQMQfLQJMcvWqafyhC2M77OBq3Fy3dokrcsmv6euf3/5Haq6r/U0Az22nH878jtl/P/dvcXZ95Pbe+HZjin+/zMZ380Q23HgRlqV89MckaG77Azq2ojj78HZViW2e1p8fa6lFV9Z2f7+/h9c8da7tn3m+EYsJ5uXdYx0NyY229Xs9v2oVn+fOBXM1zc+uuxGeXWmmnGvgtY+D6/W4Zmqf+5qp6R7RwDq+qBVfVnNTazz9DSY/E+N7s/fCXj8XU734Fzsyv9U3dWb8zwf5i9Onx9ktn24IsfeHCHG5u7+33d/dgMG+SfJPmfu9hOu5ztra9bjVc4f627H5TkYRmaRz0rQxPXG5J83+zJfnffrbs/NE57enf/xwzNMW/McL/RzuozGU44nzAxfFuSjy9aF/t192Ez4yzeDrdluIo8O803dPcranjS4zuSvDbJ/bp7/wwnL7vFPY2jxetrh7fbJaZdblveJYxXb78/w0neP2Y46U2G+5j3nBn1kCxdG3dhkvtNnOhtS/LFRdvq/j005dos63Z7++jFGU5aZj1g7L8RtiX5n4vW4b7j99CCxdvthRmaCm7Pk8bux5LsP154uTZre7x4Y4Zarh/P8B385xmW6XcXLdM+3f3McZpLMwSSBYcsmue2JD+3aPq7d/dCE+PbrY+qOiLJ1gw15fccl/OTuf1y7l1V37ToM5fa3reN87/vos/fu7svGWvTXt7d/z5DbeMhGS6EbpSLMyzLfWb6bXd7vRPf2cvu4xn+l4vvUbz1/7lJjgHr6dZlH7evvXL77Wp2W132fKCHmvJndff9M+xPx+eOTbd3Cd39L0nenWE5t3cMPCPDOn1wD7c1PSUrOIYt8x04N7tLqNi0emg2dGKGe20WnJfkh6vqm2u4h2Pxgf3yJP9u4U0NN5c/fhz360m+nKXbsO+qzkvy01W1/3jwm7xHoqp+vKq+bQzUX87QrOLrY43ZGUletdBkqaruVVXHjH9/b1U9fAw/X81wRWinbbPf3Z3kl5K8oIaHLNwrGdrRV9Ubk3w4yX2q6llVtXcNT1B7UFU9apnZnpbkV6vqe2pw96p6dA1NXvbK0Lz3S919Y1V9X3av+xmXsr39fNYVGb7Ev2Whx9S2vJ4F3ijjtvZDY+D7WoaAvbBs35Dkv1fVXcft6Mm54/04yVDL8s9Jfq+GhwxtqeHnK/Yah11UVSdX1b7j9nr/qvrR8fPnvm53YB/9QJJvGvfRLVV1ZIZ96g82qIh/mOQHaniYyJ5jd3gt/3CV1yZ58TheVdUhNT4oZpH9Mvzfr0xy16p60dhvLb01Q03Jbyf5g7H2+rUZahG+fzzm7VVVD5sp4x8leX4ND1jaP8N9mrN+L8lLa7wHsoaf8fipuu1ezMszsw+Py/T1DPv3HlV1bIaaxlm3ZGged7dxvs/OEtv7WIN3dpLTaqxBHMv5+PHvHxjX+5YM31//Nxu4TffQvPlvk/xWVX1DVR2S4bzn95ebbrXf2dnOPp7kbRkeevRj4/7zC0m+bXufu0arYzN4xnicvVuSV2ZoZjkV4N+VZc4HqurocV+uDBd3bs6uta5uVcPDkB6b5P9k+8fA/TLcB/vlcRv91RV8znLfgXMjNG4C3f32DDcRL3hLhntHPp3k/AxXNWa9IcMV9Kur6hMZ/o/PyXDV49oMXyo/23fyUcs7kd/O8Kj0bRlu1P7jZcZ9YIaHR1yX5J8yhKPXjMP+2/j+b6rqugw3xj9mHLZfhifefilDc4L7ZljnO63ufleSH81wNf9zdVvznU9nWJ+PznB19d8yLPf/yO2bpy41vxckeX2Gn5T51wzraI/uvi7Ddvm6GpppnJjl/0+7g+3t57fq25qyv6eGZjBPyvLb8s7urhmagl+eYdv7gdx279MnMzyg4rIM2+uJPTQfv53x+PcTGWpz/zlDADk5w/Z48zjsfkk+leG4+e7cVgu2KdbtDuyjP5rh6vWXMjy05pk93Ie3EWW7JMODdp4xluXyDKFpuXB3aob1+LYM6/avc8faumQIEhdkqJn8fIYLdWt6/113Xz+W49CMD6bq7n/I8MCqkzMEuUsybIcLT9w8OcMV/3/KsM/+xdj/xvH1tAxNQN8xHuc+ldtfHHt5khPG7+7TM2xjb89wAnpphgfsfHBRUa8bP+vzSc7NcFFgKmg9PcODjD46fv77c9s9UvfJEHqvyXBsXuqezPX2pAz3eF6YYTnfneGBgMtZ1Xf29vbx7v7nDPeknZph//m+3P4BgpviGLCO3pRhe7g8wzp68tSI476y3PnA9yT5UIZg8+EM+9M716nc8/DKGn9zOcN2+9dJXroDx8DnZqiR/HKSP8uwr++o5b4D56aGi5kAsLlV1dOT/Ep3Hz7nokBq+Emhv0uy9250kRbYTalpBADYjhp+92+h6ep9M9Qcvl1gBHYHQiMAwPbdJcPtENdmaKZ6WYb7TgF2eZqnAgAAMElNIwAAAJOERgAAACYJjQAAAEwSGgEAAJgkNAIAADBJaASANVRVh1ZVV9U95l0WAFgLQiMA7ATGH5WveZcDgN2P0AgAE6pqv6o6raouqqovV9VHq+rgqnpuVX22qq6rqs9V1Qkzk/39+HpxVV1fVU/ezmccWFVvqapLq+qaqjq3qu42DuuqOqGqPpnkhiT7VNVDquqD47j/VFVPnJnX1qp6V1W9cSzvZ6vq8Wu9XgDYvQiNADDtzCQPTPIfk9wjyS8m+WqSC5P8QJL9kvx8kt+sqkeM0zx0fD2ou/fp7rOmZl5VeyR5Z5KvJzksyb2TvDDJLTOjPSnJY8bP2jPJXyZ5a5IDkjwzyetnPjtJfiRDcP3GJM9N8kdV9S0rX3QAGFR3z7sMALDpVNV9knwhyf27+6LtjHt2ko929ylVdWiSf01yz+6+ZjvTfV+S9yU5oLu/usTwTvL47j57fP/kJCd197fPjPO6JOnuX6yqrUmO7u4Hzwx/T5IPdvfJ21tmAFiKmkYAWNr9k9y4VGCsqidX1XlVdXVVXZPksRlqCVfzGZcsFRhnzH7+QUn+bdHwz4/9F1y4aPiFSe63irIBQBKhEQCmXJhkr6o6eLZnVR2S5PeTPD9DDeE9kvxFkoWH1Mw2Ld2Rz7jfwj2ME2bnd3GSQxcNf8DYf8H9Fw0/JMklKygTANyO0AgAS+juy5P8WZIzquqbq2qPqvoPSQ7OEBC/mOSWqnpshnsOF1yRIejtyH2EH03yz0l+r6ruUVVbquqIqtprYvy/SPJNVfWscdwjM9zz+Acz43xbVf3COPzHMtx7+cc7vuQAcHtCIwBMe1qSbUk+luSaJGdkqB08JcnfJPlSkmMyPMwmSTI2NX1JkveMTzh90tTMu/uWJD+R5O4ZwuOVSU7OxPdzd1+d5EeTPGX87NcleWZ3f2BmtL/M8OCeq5L8bpKndPdnV7jcAHArD8IBgF3E+CCcw7v7cXMuCgC7EDWNAAAATBIaAWAdVdULq+r6pbp5lw0AdoTmqQAAAExS0wgAAMAkoREAAIBJQiMAAACThEYAAAAmCY0AAABMEhoBAACYJDQCAAAwSWgEAABg0v8DI4wuo7FS/CwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x540 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6),dpi=90)\n",
    "sns.barplot(data=df,x='cat_crop',y='Production');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "15b96fb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State_Name</th>\n",
       "      <th>District_Name</th>\n",
       "      <th>Crop_Year</th>\n",
       "      <th>Season</th>\n",
       "      <th>Rainfall</th>\n",
       "      <th>Crop</th>\n",
       "      <th>Area</th>\n",
       "      <th>Production</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>NICOBARS</td>\n",
       "      <td>2000</td>\n",
       "      <td>Kharif</td>\n",
       "      <td>2763.2</td>\n",
       "      <td>Arecanut</td>\n",
       "      <td>1254.0</td>\n",
       "      <td>2000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>NICOBARS</td>\n",
       "      <td>2000</td>\n",
       "      <td>Kharif</td>\n",
       "      <td>2763.2</td>\n",
       "      <td>Other Kharif pulses</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>NICOBARS</td>\n",
       "      <td>2000</td>\n",
       "      <td>Kharif</td>\n",
       "      <td>2763.2</td>\n",
       "      <td>Rice</td>\n",
       "      <td>102.0</td>\n",
       "      <td>321.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>NICOBARS</td>\n",
       "      <td>2000</td>\n",
       "      <td>Whole Year</td>\n",
       "      <td>2763.2</td>\n",
       "      <td>Banana</td>\n",
       "      <td>176.0</td>\n",
       "      <td>641.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>NICOBARS</td>\n",
       "      <td>2000</td>\n",
       "      <td>Whole Year</td>\n",
       "      <td>2763.2</td>\n",
       "      <td>Cashewnut</td>\n",
       "      <td>720.0</td>\n",
       "      <td>165.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    State_Name District_Name  Crop_Year      Season  Rainfall  \\\n",
       "0  Andaman and Nicobar Islands      NICOBARS       2000      Kharif    2763.2   \n",
       "1  Andaman and Nicobar Islands      NICOBARS       2000      Kharif    2763.2   \n",
       "2  Andaman and Nicobar Islands      NICOBARS       2000      Kharif    2763.2   \n",
       "3  Andaman and Nicobar Islands      NICOBARS       2000  Whole Year    2763.2   \n",
       "4  Andaman and Nicobar Islands      NICOBARS       2000  Whole Year    2763.2   \n",
       "\n",
       "                  Crop    Area  Production  \n",
       "0             Arecanut  1254.0      2000.0  \n",
       "1  Other Kharif pulses     2.0         1.0  \n",
       "2                 Rice   102.0       321.0  \n",
       "3               Banana   176.0       641.0  \n",
       "4            Cashewnut   720.0       165.0  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cultivation_data = df[[\"State_Name\",\"District_Name\",\"Crop_Year\",\"Season\",\"Rainfall\",\"Crop\",\"Area\",\"Production\"]]\n",
    "cultivation_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8782c1f0",
   "metadata": {},
   "source": [
    "# Dummy encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "23e87b16",
   "metadata": {},
   "outputs": [],
   "source": [
    "cultivation_data = pd.get_dummies(cultivation_data)\n",
    "x = cultivation_data.drop(\"Production\",axis=1)\n",
    "y = cultivation_data[[\"Production\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "463a14d2",
   "metadata": {},
   "source": [
    "# Train/Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "43de1542",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train : (164436, 793)\n",
      "x_test : (80992, 793)\n",
      "y_train : (164436, 1)\n",
      "y_test : (80992, 1)\n"
     ]
    }
   ],
   "source": [
    "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.33, random_state=42)\n",
    "print(\"x_train :\",x_train.shape)\n",
    "print(\"x_test :\",x_test.shape)\n",
    "print(\"y_train :\",y_train.shape)\n",
    "print(\"y_test :\",y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cb637cb",
   "metadata": {},
   "source": [
    "# Regression Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "c2341265",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTreeRegressor(random_state=42) === \n",
      "DecisionTreeRegressor(random_state=42) [0] Accuracy :  0.5860185022006028\n",
      "DecisionTreeRegressor(random_state=42) [1] Accuracy :  0.5794436734910096\n",
      "DecisionTreeRegressor(random_state=42) [2] Accuracy :  0.9131971928120086\n",
      "DecisionTreeRegressor(random_state=42) [3] Accuracy :  0.8568274942694323\n",
      "DecisionTreeRegressor(random_state=42) [4] Accuracy :  0.9100813447226233\n",
      "DecisionTreeRegressor(random_state=42) [5] Accuracy :  0.9410618099660132\n",
      "DecisionTreeRegressor(random_state=42) [6] Accuracy :  0.8832158819525073\n",
      "DecisionTreeRegressor(random_state=42) [7] Accuracy :  0.8706327557425898\n",
      "DecisionTreeRegressor(random_state=42) [8] Accuracy :  0.5698179193116277\n",
      "DecisionTreeRegressor(random_state=42) [9] Accuracy :  0.8451576978838091\n",
      "K-fold CV average score: 0.80\n",
      "MSE: 18319686061727.92\n",
      "RMSE: 4280150.24\n",
      "DecisionTreeRegressor(random_state=42): 0.795545 (0.144648)\n",
      "============================================\n",
      "============================================\n",
      "============================================\n",
      "KNeighborsRegressor() === \n",
      "KNeighborsRegressor() [0] Accuracy :  0.09777412686986131\n",
      "KNeighborsRegressor() [1] Accuracy :  -0.03781235366930624\n",
      "KNeighborsRegressor() [2] Accuracy :  0.06062434227764257\n",
      "KNeighborsRegressor() [3] Accuracy :  0.37318697629452724\n",
      "KNeighborsRegressor() [4] Accuracy :  0.07496231889939864\n",
      "KNeighborsRegressor() [5] Accuracy :  0.15225133120098433\n",
      "KNeighborsRegressor() [6] Accuracy :  -0.037146269568582246\n",
      "KNeighborsRegressor() [7] Accuracy :  0.23058646783535008\n",
      "KNeighborsRegressor() [8] Accuracy :  0.27083877226603703\n",
      "KNeighborsRegressor() [9] Accuracy :  0.22306201333958853\n",
      "K-fold CV average score: 0.14\n",
      "MSE: 230586538153652.09\n",
      "RMSE: 15185076.17\n",
      "KNeighborsRegressor(): 0.140833 (0.127301)\n",
      "============================================\n",
      "============================================\n",
      "============================================\n",
      "BaggingRegressor(random_state=42) === \n",
      "BaggingRegressor(random_state=42) [0] Accuracy :  0.9408918015142856\n",
      "BaggingRegressor(random_state=42) [1] Accuracy :  0.7789641856828017\n",
      "BaggingRegressor(random_state=42) [2] Accuracy :  0.9748244907347511\n",
      "BaggingRegressor(random_state=42) [3] Accuracy :  0.9489319962033602\n",
      "BaggingRegressor(random_state=42) [4] Accuracy :  0.9499920942406461\n",
      "BaggingRegressor(random_state=42) [5] Accuracy :  0.8981712708236906\n",
      "BaggingRegressor(random_state=42) [6] Accuracy :  0.831357330944358\n",
      "BaggingRegressor(random_state=42) [7] Accuracy :  0.8000719310318474\n",
      "BaggingRegressor(random_state=42) [8] Accuracy :  0.9296542469389915\n",
      "BaggingRegressor(random_state=42) [9] Accuracy :  0.38669146619016614\n",
      "K-fold CV average score: 0.84\n",
      "MSE: 17103178481423.58\n",
      "RMSE: 4135598.93\n",
      "BaggingRegressor(random_state=42): 0.843955 (0.165671)\n",
      "============================================\n",
      "============================================\n",
      "============================================\n",
      "RandomForestRegressor(n_estimators=10, random_state=42) === \n",
      "RandomForestRegressor(n_estimators=10, random_state=42) [0] Accuracy :  0.9283020815122789\n",
      "RandomForestRegressor(n_estimators=10, random_state=42) [1] Accuracy :  0.7927290174093833\n",
      "RandomForestRegressor(n_estimators=10, random_state=42) [2] Accuracy :  0.9726495903478434\n",
      "RandomForestRegressor(n_estimators=10, random_state=42) [3] Accuracy :  0.9488905564905972\n",
      "RandomForestRegressor(n_estimators=10, random_state=42) [4] Accuracy :  0.9472045523292916\n",
      "RandomForestRegressor(n_estimators=10, random_state=42) [5] Accuracy :  0.8910863516800513\n",
      "RandomForestRegressor(n_estimators=10, random_state=42) [6] Accuracy :  0.8025762565621118\n",
      "RandomForestRegressor(n_estimators=10, random_state=42) [7] Accuracy :  0.8258435465563851\n",
      "RandomForestRegressor(n_estimators=10, random_state=42) [8] Accuracy :  0.9237256487104344\n",
      "RandomForestRegressor(n_estimators=10, random_state=42) [9] Accuracy :  0.3296100693016365\n",
      "K-fold CV average score: 0.84\n",
      "MSE: 18317311067465.16\n",
      "RMSE: 4279872.79\n",
      "RandomForestRegressor(n_estimators=10, random_state=42): 0.836262 (0.179599)\n",
      "============================================\n",
      "============================================\n",
      "============================================\n",
      "GradientBoostingRegressor() === \n",
      "GradientBoostingRegressor() [0] Accuracy :  0.8907689618100144\n",
      "GradientBoostingRegressor() [1] Accuracy :  0.9102025229884233\n",
      "GradientBoostingRegressor() [2] Accuracy :  0.9673430639471947\n",
      "GradientBoostingRegressor() [3] Accuracy :  0.9388903662032724\n",
      "GradientBoostingRegressor() [4] Accuracy :  0.8616973884830679\n",
      "GradientBoostingRegressor() [5] Accuracy :  0.8262696196256971\n",
      "GradientBoostingRegressor() [6] Accuracy :  0.8878754219110565\n",
      "GradientBoostingRegressor() [7] Accuracy :  0.9088381132808071\n",
      "GradientBoostingRegressor() [8] Accuracy :  0.9400901586702063\n",
      "GradientBoostingRegressor() [9] Accuracy :  0.9056845025572495\n",
      "K-fold CV average score: 0.90\n",
      "MSE: 18875527341186.08\n",
      "RMSE: 4344597.49\n",
      "GradientBoostingRegressor(): 0.903766 (0.038552)\n",
      "============================================\n",
      "============================================\n",
      "============================================\n"
     ]
    }
   ],
   "source": [
    "Predictions=y_test.copy()\n",
    "models = []\n",
    "models.append(('dtr_reg', DecisionTreeRegressor(random_state=42)))\n",
    "models.append(('knr_reg', KNeighborsRegressor()))\n",
    "models.append(('bagging_reg', BaggingRegressor(n_estimators=10, random_state=42)))\n",
    "models.append(('rf_reg', RandomForestRegressor(n_estimators=10,random_state=42)))\n",
    "models.append(('grad_reg', GradientBoostingRegressor()))\n",
    "\n",
    "# evaluate each model in turn\n",
    "results = []\n",
    "names=[]\n",
    "RMSE=[]\n",
    "for name,model in models:\n",
    "    bla=model.fit(x_train, y_train)\n",
    "    print(f\"%s === \"%model)\n",
    "    kfold = KFold(n_splits=10, random_state=1, shuffle=True)\n",
    "    kf_cv_scores = cross_val_score(model, x_train, y_train, cv=kfold)\n",
    "    for i in range (len(kf_cv_scores)):\n",
    "        print(f\"%s {[i]} Accuracy :  {kf_cv_scores[i]}\"% model)\n",
    "    \n",
    "    print(\"K-fold CV average score: %.2f\" % kf_cv_scores.mean())\n",
    "    results.append(kf_cv_scores)\n",
    "    names.append(name)\n",
    "    ypred = model.predict(x_test)\n",
    "    Predictions[name]=ypred\n",
    "    mse = mean_squared_error(y_test,ypred)\n",
    "    print(\"MSE: %.2f\" % mse)\n",
    "    print(\"RMSE: %.2f\" % np.sqrt(mse))\n",
    "    RMSE.append(np.sqrt(mse))\n",
    "    \n",
    "    msg = \"%s: %f (%f)\" % (model, kf_cv_scores.mean(), kf_cv_scores.std())\n",
    "    print(msg)\n",
    "    print(\"============================================\")\n",
    "    print(\"============================================\")\n",
    "    print(\"============================================\")\n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d9fdce5",
   "metadata": {},
   "source": [
    "# Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "6925f47a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Production</th>\n",
       "      <th>dtr_reg</th>\n",
       "      <th>knr_reg</th>\n",
       "      <th>bagging_reg</th>\n",
       "      <th>rf_reg</th>\n",
       "      <th>grad_reg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>160095</th>\n",
       "      <td>61.4</td>\n",
       "      <td>43.1</td>\n",
       "      <td>29.02</td>\n",
       "      <td>36.16</td>\n",
       "      <td>36.88</td>\n",
       "      <td>1519.128187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10748</th>\n",
       "      <td>3284.0</td>\n",
       "      <td>5504.0</td>\n",
       "      <td>65468.20</td>\n",
       "      <td>3623.20</td>\n",
       "      <td>3627.00</td>\n",
       "      <td>-10358.579555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213093</th>\n",
       "      <td>216.0</td>\n",
       "      <td>215.0</td>\n",
       "      <td>1971.20</td>\n",
       "      <td>210.80</td>\n",
       "      <td>204.40</td>\n",
       "      <td>-10362.181755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204140</th>\n",
       "      <td>112.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>94.00</td>\n",
       "      <td>95.50</td>\n",
       "      <td>93.70</td>\n",
       "      <td>-10362.181755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232624</th>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>91.40</td>\n",
       "      <td>2.60</td>\n",
       "      <td>2.40</td>\n",
       "      <td>-10358.579555</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Production  dtr_reg   knr_reg  bagging_reg   rf_reg      grad_reg\n",
       "160095        61.4     43.1     29.02        36.16    36.88   1519.128187\n",
       "10748       3284.0   5504.0  65468.20      3623.20  3627.00 -10358.579555\n",
       "213093       216.0    215.0   1971.20       210.80   204.40 -10362.181755\n",
       "204140       112.0    107.0     94.00        95.50    93.70 -10362.181755\n",
       "232624         5.0      2.0     91.40         2.60     2.40 -10358.579555"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Predictions.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "0f054fdd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>Production</th>\n",
       "      <th>dtr_reg</th>\n",
       "      <th>knr_reg</th>\n",
       "      <th>bagging_reg</th>\n",
       "      <th>rf_reg</th>\n",
       "      <th>grad_reg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>98366</th>\n",
       "      <td>7.170000e+08</td>\n",
       "      <td>9.990000e+08</td>\n",
       "      <td>1.576200e+08</td>\n",
       "      <td>800500900.0</td>\n",
       "      <td>827659200.0</td>\n",
       "      <td>7.003941e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7260</th>\n",
       "      <td>7.199610e+08</td>\n",
       "      <td>7.208950e+08</td>\n",
       "      <td>6.432120e+04</td>\n",
       "      <td>677667025.6</td>\n",
       "      <td>678753734.0</td>\n",
       "      <td>5.968495e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7831</th>\n",
       "      <td>7.299650e+08</td>\n",
       "      <td>7.208950e+08</td>\n",
       "      <td>1.482780e+05</td>\n",
       "      <td>698551752.5</td>\n",
       "      <td>691884835.6</td>\n",
       "      <td>5.711188e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100202</th>\n",
       "      <td>8.240000e+08</td>\n",
       "      <td>8.630000e+08</td>\n",
       "      <td>7.189621e+08</td>\n",
       "      <td>848537000.0</td>\n",
       "      <td>836374000.0</td>\n",
       "      <td>8.723052e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100418</th>\n",
       "      <td>8.510000e+08</td>\n",
       "      <td>8.630000e+08</td>\n",
       "      <td>3.436764e+08</td>\n",
       "      <td>862437000.0</td>\n",
       "      <td>850274000.0</td>\n",
       "      <td>8.723052e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100217</th>\n",
       "      <td>8.590000e+08</td>\n",
       "      <td>8.630000e+08</td>\n",
       "      <td>3.092634e+05</td>\n",
       "      <td>868500000.0</td>\n",
       "      <td>858568500.0</td>\n",
       "      <td>8.276768e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101479</th>\n",
       "      <td>8.820000e+08</td>\n",
       "      <td>1.125000e+09</td>\n",
       "      <td>6.366716e+05</td>\n",
       "      <td>947370300.0</td>\n",
       "      <td>912401800.0</td>\n",
       "      <td>8.879562e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98562</th>\n",
       "      <td>9.030000e+08</td>\n",
       "      <td>9.990000e+08</td>\n",
       "      <td>1.801947e+08</td>\n",
       "      <td>894500000.0</td>\n",
       "      <td>880900000.0</td>\n",
       "      <td>8.638032e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101235</th>\n",
       "      <td>9.467810e+08</td>\n",
       "      <td>1.063000e+09</td>\n",
       "      <td>3.843641e+08</td>\n",
       "      <td>956580000.0</td>\n",
       "      <td>948716800.0</td>\n",
       "      <td>9.608601e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102036</th>\n",
       "      <td>1.001000e+09</td>\n",
       "      <td>8.520090e+08</td>\n",
       "      <td>1.897873e+08</td>\n",
       "      <td>910970300.0</td>\n",
       "      <td>909669400.0</td>\n",
       "      <td>8.879562e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Production       dtr_reg       knr_reg  bagging_reg       rf_reg  \\\n",
       "98366   7.170000e+08  9.990000e+08  1.576200e+08  800500900.0  827659200.0   \n",
       "7260    7.199610e+08  7.208950e+08  6.432120e+04  677667025.6  678753734.0   \n",
       "7831    7.299650e+08  7.208950e+08  1.482780e+05  698551752.5  691884835.6   \n",
       "100202  8.240000e+08  8.630000e+08  7.189621e+08  848537000.0  836374000.0   \n",
       "100418  8.510000e+08  8.630000e+08  3.436764e+08  862437000.0  850274000.0   \n",
       "100217  8.590000e+08  8.630000e+08  3.092634e+05  868500000.0  858568500.0   \n",
       "101479  8.820000e+08  1.125000e+09  6.366716e+05  947370300.0  912401800.0   \n",
       "98562   9.030000e+08  9.990000e+08  1.801947e+08  894500000.0  880900000.0   \n",
       "101235  9.467810e+08  1.063000e+09  3.843641e+08  956580000.0  948716800.0   \n",
       "102036  1.001000e+09  8.520090e+08  1.897873e+08  910970300.0  909669400.0   \n",
       "\n",
       "            grad_reg  \n",
       "98366   7.003941e+08  \n",
       "7260    5.968495e+08  \n",
       "7831    5.711188e+08  \n",
       "100202  8.723052e+08  \n",
       "100418  8.723052e+08  \n",
       "100217  8.276768e+08  \n",
       "101479  8.879562e+08  \n",
       "98562   8.638032e+08  \n",
       "101235  9.608601e+08  \n",
       "102036  8.879562e+08  "
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Predictions.sort_values('Production').tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "7af9c5ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "Predictions.plot(kind='scatter', x='Production', y='dtr_reg', color='r')\n",
    "plt.title(\"Decision Tree\")\n",
    "Predictions.plot(kind='scatter', x='Production', y='knr_reg', color='g')\n",
    "plt.title(\"kneighbors regression\")\n",
    "Predictions.plot(kind='scatter', x='Production', y='bagging_reg', color='b') \n",
    "plt.title(\"Bagging\")\n",
    "Predictions.plot(kind='scatter', x='Production', y='rf_reg', color='r')\n",
    "plt.title(\"Random Forest\")\n",
    "Predictions.plot(kind='scatter', x='Production', y='grad_reg', color='g')\n",
    "plt.title(\"Gradient Boosting\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90f7d7ab",
   "metadata": {},
   "source": [
    "# Cross Validation Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "99c993ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dtr_reg  : 0.7955454272352223\n",
      "knr_reg  : 0.14083277257455012\n",
      "bagging_reg  : 0.8439550814304899\n",
      "rf_reg  : 0.8362617670900013\n",
      "grad_reg  : 0.9037660119476989\n"
     ]
    }
   ],
   "source": [
    "for i in range(5):\n",
    "    print(f\"{models[i][0]}  : {results[i].mean()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "263d4e42",
   "metadata": {},
   "source": [
    "Gradient Boosting method has highest cross validation score, followed by Bagging regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "7defe10e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "fig.suptitle('Machine Learning Model Comparison')\n",
    "ax = fig.add_subplot()\n",
    "plt.boxplot(results)\n",
    "ax.set_xticklabels(names)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "693ed0da",
   "metadata": {},
   "source": [
    "# Root mean square error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "c0260f52",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dtr_reg    : 4280150.238219261\n",
      "knr_reg    : 15185076.165553087\n",
      "bagging_reg    : 4135598.926567064\n",
      "rf_reg    : 4279872.786364577\n",
      "grad_reg    : 4344597.488972486\n"
     ]
    }
   ],
   "source": [
    "for name, model in models:\n",
    "    MSE = np.square(np.subtract(Predictions.Production,Predictions[name])).mean()\n",
    "    rmse =np.sqrt(MSE)\n",
    "    print(f\"{name}    : {rmse}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a76bdb0",
   "metadata": {},
   "source": [
    "Bagging regression have the least root mean square error, Followed by Random forest and Decision tree respectively"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "ace21cdf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dtr_reg    : 0.9344826373293126\n",
      "knr_reg    : 0.17534493788338446\n",
      "bagging_reg    : 0.9388332778403936\n",
      "rf_reg    : 0.9344911311080776\n",
      "grad_reg    : 0.9324947618509396\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "\n",
    "for name, model in models:\n",
    "    r2score=r2_score(Predictions.Production,Predictions[name])\n",
    "    print(f\"{name}    : {r2score}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2bf88be3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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