regression_TN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e1a3ba54",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import Necessary Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8cd32b6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fdad1fdc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Read The dataset using pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5d1c653e",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset=pd.read_csv(\"C:\\\\Users\\\\User\\\\Documents\\\\TamilNadu Crop Yield Prediction.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "59403c51",
   "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>Tamil Nadu</td>\n",
       "      <td>ARIYALUR</td>\n",
       "      <td>2008</td>\n",
       "      <td>Kharif</td>\n",
       "      <td>Rice</td>\n",
       "      <td>24574</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Tamil Nadu</td>\n",
       "      <td>ARIYALUR</td>\n",
       "      <td>2008</td>\n",
       "      <td>Whole Year</td>\n",
       "      <td>Arhar/Tur</td>\n",
       "      <td>209</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tamil Nadu</td>\n",
       "      <td>ARIYALUR</td>\n",
       "      <td>2008</td>\n",
       "      <td>Whole Year</td>\n",
       "      <td>Bajra</td>\n",
       "      <td>565</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tamil Nadu</td>\n",
       "      <td>ARIYALUR</td>\n",
       "      <td>2008</td>\n",
       "      <td>Whole Year</td>\n",
       "      <td>Banana</td>\n",
       "      <td>190</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Tamil Nadu</td>\n",
       "      <td>ARIYALUR</td>\n",
       "      <td>2008</td>\n",
       "      <td>Whole Year</td>\n",
       "      <td>Cashewnut</td>\n",
       "      <td>31113</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13522</th>\n",
       "      <td>Tamil Nadu</td>\n",
       "      <td>VIRUDHUNAGAR</td>\n",
       "      <td>2013</td>\n",
       "      <td>Whole Year</td>\n",
       "      <td>Small millets</td>\n",
       "      <td>1187</td>\n",
       "      <td>676.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13523</th>\n",
       "      <td>Tamil Nadu</td>\n",
       "      <td>VIRUDHUNAGAR</td>\n",
       "      <td>2013</td>\n",
       "      <td>Whole Year</td>\n",
       "      <td>Sugarcane</td>\n",
       "      <td>2917</td>\n",
       "      <td>296017.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13524</th>\n",
       "      <td>Tamil Nadu</td>\n",
       "      <td>VIRUDHUNAGAR</td>\n",
       "      <td>2013</td>\n",
       "      <td>Whole Year</td>\n",
       "      <td>Sweet potato</td>\n",
       "      <td>4</td>\n",
       "      <td>84.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13525</th>\n",
       "      <td>Tamil Nadu</td>\n",
       "      <td>VIRUDHUNAGAR</td>\n",
       "      <td>2013</td>\n",
       "      <td>Whole Year</td>\n",
       "      <td>Tapioca</td>\n",
       "      <td>4</td>\n",
       "      <td>120.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13526</th>\n",
       "      <td>Tamil Nadu</td>\n",
       "      <td>VIRUDHUNAGAR</td>\n",
       "      <td>2013</td>\n",
       "      <td>Whole Year</td>\n",
       "      <td>Turmeric</td>\n",
       "      <td>4</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13527 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       State_Name District_Name  Crop_Year       Season           Crop   Area  \\\n",
       "0      Tamil Nadu      ARIYALUR       2008  Kharif                Rice  24574   \n",
       "1      Tamil Nadu      ARIYALUR       2008  Whole Year       Arhar/Tur    209   \n",
       "2      Tamil Nadu      ARIYALUR       2008  Whole Year           Bajra    565   \n",
       "3      Tamil Nadu      ARIYALUR       2008  Whole Year          Banana    190   \n",
       "4      Tamil Nadu      ARIYALUR       2008  Whole Year       Cashewnut  31113   \n",
       "...           ...           ...        ...          ...            ...    ...   \n",
       "13522  Tamil Nadu  VIRUDHUNAGAR       2013  Whole Year   Small millets   1187   \n",
       "13523  Tamil Nadu  VIRUDHUNAGAR       2013  Whole Year       Sugarcane   2917   \n",
       "13524  Tamil Nadu  VIRUDHUNAGAR       2013  Whole Year    Sweet potato      4   \n",
       "13525  Tamil Nadu  VIRUDHUNAGAR       2013  Whole Year         Tapioca      4   \n",
       "13526  Tamil Nadu  VIRUDHUNAGAR       2013  Whole Year        Turmeric      4   \n",
       "\n",
       "       Production  \n",
       "0             NaN  \n",
       "1             NaN  \n",
       "2             NaN  \n",
       "3             NaN  \n",
       "4             NaN  \n",
       "...           ...  \n",
       "13522       676.0  \n",
       "13523    296017.0  \n",
       "13524        84.0  \n",
       "13525       120.0  \n",
       "13526        15.0  \n",
       "\n",
       "[13527 rows x 7 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c65c165c",
   "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>Production</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>13527.000000</td>\n",
       "      <td>13527.000000</td>\n",
       "      <td>1.324800e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2004.879722</td>\n",
       "      <td>7089.169735</td>\n",
       "      <td>9.115673e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.716127</td>\n",
       "      <td>20888.489346</td>\n",
       "      <td>2.110179e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1997.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2002.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>5.800000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2004.000000</td>\n",
       "      <td>626.000000</td>\n",
       "      <td>8.500000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2009.000000</td>\n",
       "      <td>4481.000000</td>\n",
       "      <td>9.091250e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2013.000000</td>\n",
       "      <td>367554.000000</td>\n",
       "      <td>1.250800e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Crop_Year           Area    Production\n",
       "count  13527.000000   13527.000000  1.324800e+04\n",
       "mean    2004.879722    7089.169735  9.115673e+05\n",
       "std        4.716127   20888.489346  2.110179e+07\n",
       "min     1997.000000       1.000000  0.000000e+00\n",
       "25%     2002.000000      49.000000  5.800000e+01\n",
       "50%     2004.000000     626.000000  8.500000e+02\n",
       "75%     2009.000000    4481.000000  9.091250e+03\n",
       "max     2013.000000  367554.000000  1.250800e+09"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c9eba8fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Checking for the null values in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "43497c4f",
   "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       279\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c9e65206",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Removing the null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "68e14fa2",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5ac86d06",
   "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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "09ffb449",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Converting Categorical Data into Numerical Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f93c7c60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Rice', 'Small millets', 'Arhar/Tur', 'Bajra', 'Banana',\n",
       "       'Cashewnut', 'Castor seed', 'Coconut ', 'Coriander',\n",
       "       'Cotton(lint)', 'Dry chillies', 'Groundnut', 'Jowar', 'Maize',\n",
       "       'Moong(Green Gram)', 'Onion', 'Ragi', 'Sesamum', 'Sugarcane',\n",
       "       'Sunflower', 'Sweet potato', 'Tapioca', 'Turmeric', 'Urad',\n",
       "       'Horse-gram', 'Tobacco', 'Black pepper', 'Cardamom', 'Gram',\n",
       "       'Pulses total', 'Total foodgrain', 'Wheat', 'Sannhamp', 'Korra',\n",
       "       'Samai', 'Guar seed', 'Other Cereals & Millets',\n",
       "       'Other Kharif pulses', 'Rapeseed &Mustard', 'Varagu', 'Ash Gourd',\n",
       "       'Beans & Mutter(Vegetable)', 'Beet Root', 'Bhindi', 'Bitter Gourd',\n",
       "       'Bottle Gourd', 'Brinjal', 'Cauliflower', 'Citrus Fruit',\n",
       "       'Cucumber', 'Drum Stick', 'Garlic', 'Grapes', 'Jack Fruit',\n",
       "       'Lab-Lab', 'Mango', 'Orange', 'Other Citrus Fruit',\n",
       "       'Other Fresh Fruits', 'Other Vegetables', 'Papaya', 'Pome Fruit',\n",
       "       'Pome Granet', 'Redish', 'Ribed Guard', 'Snak Guard', 'Tomato',\n",
       "       'Water Melon', 'Yam', 'Cabbage', 'Pump Kin', 'Dry ginger',\n",
       "       'Arecanut', 'Potato', 'Carrot', 'Pineapple', 'Mesta', 'Plums',\n",
       "       'Turnip', 'Pear', 'Ber'], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['Crop'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "20c7c297",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "357cf68a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 783., 1032., 1324., 1507., 1831., 2189., 2243., 1534.,  634.,\n",
       "         171.]),\n",
       " array([ 0.        ,  1.28146255,  2.56292511,  3.84438766,  5.12585021,\n",
       "         6.40731276,  7.68877532,  8.97023787, 10.25170042, 11.53316297,\n",
       "        12.81462553]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(nrows=1, ncols=2)\n",
    "axs[0].hist(dataset['Area'], edgecolor='black')\n",
    "data_log = np.log(dataset['Area'])\n",
    "axs[1].hist(data_log, edgecolor='black')\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b268efb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Kharif     ', 'Whole Year ', 'Rabi       '], dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['Season'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7ab0abd9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['ARIYALUR', 'COIMBATORE', 'CUDDALORE', 'DHARMAPURI', 'DINDIGUL',\n",
       "       'ERODE', 'KANCHIPURAM', 'KANNIYAKUMARI', 'KARUR', 'KRISHNAGIRI',\n",
       "       'MADURAI', 'NAGAPATTINAM', 'NAMAKKAL', 'PERAMBALUR', 'PUDUKKOTTAI',\n",
       "       'RAMANATHAPURAM', 'SALEM', 'SIVAGANGA', 'THANJAVUR',\n",
       "       'THE NILGIRIS', 'THENI', 'THIRUVALLUR', 'THIRUVARUR',\n",
       "       'TIRUCHIRAPPALLI', 'TIRUNELVELI', 'TIRUPPUR', 'TIRUVANNAMALAI',\n",
       "       'TUTICORIN', 'VELLORE', 'VILLUPURAM', 'VIRUDHUNAGAR'], dtype=object)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['District_Name'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b2bd8df6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       State_Name  Crop_Year       Season           Crop   Area  Production  \\\n",
      "22     Tamil Nadu       2009  Kharif                Rice  25978     80462.0   \n",
      "23     Tamil Nadu       2009  Kharif       Small millets    404       649.0   \n",
      "24     Tamil Nadu       2009  Whole Year       Arhar/Tur    160       122.0   \n",
      "25     Tamil Nadu       2009  Whole Year           Bajra    644      1082.0   \n",
      "26     Tamil Nadu       2009  Whole Year          Banana    134      5761.0   \n",
      "...           ...        ...          ...            ...    ...         ...   \n",
      "13522  Tamil Nadu       2013  Whole Year   Small millets   1187       676.0   \n",
      "13523  Tamil Nadu       2013  Whole Year       Sugarcane   2917    296017.0   \n",
      "13524  Tamil Nadu       2013  Whole Year    Sweet potato      4        84.0   \n",
      "13525  Tamil Nadu       2013  Whole Year         Tapioca      4       120.0   \n",
      "13526  Tamil Nadu       2013  Whole Year        Turmeric      4        15.0   \n",
      "\n",
      "       District_Name_ARIYALUR  District_Name_COIMBATORE  \\\n",
      "22                          1                         0   \n",
      "23                          1                         0   \n",
      "24                          1                         0   \n",
      "25                          1                         0   \n",
      "26                          1                         0   \n",
      "...                       ...                       ...   \n",
      "13522                       0                         0   \n",
      "13523                       0                         0   \n",
      "13524                       0                         0   \n",
      "13525                       0                         0   \n",
      "13526                       0                         0   \n",
      "\n",
      "       District_Name_CUDDALORE  District_Name_DHARMAPURI  ...  \\\n",
      "22                           0                         0  ...   \n",
      "23                           0                         0  ...   \n",
      "24                           0                         0  ...   \n",
      "25                           0                         0  ...   \n",
      "26                           0                         0  ...   \n",
      "...                        ...                       ...  ...   \n",
      "13522                        0                         0  ...   \n",
      "13523                        0                         0  ...   \n",
      "13524                        0                         0  ...   \n",
      "13525                        0                         0  ...   \n",
      "13526                        0                         0  ...   \n",
      "\n",
      "       District_Name_THIRUVALLUR  District_Name_THIRUVARUR  \\\n",
      "22                             0                         0   \n",
      "23                             0                         0   \n",
      "24                             0                         0   \n",
      "25                             0                         0   \n",
      "26                             0                         0   \n",
      "...                          ...                       ...   \n",
      "13522                          0                         0   \n",
      "13523                          0                         0   \n",
      "13524                          0                         0   \n",
      "13525                          0                         0   \n",
      "13526                          0                         0   \n",
      "\n",
      "       District_Name_TIRUCHIRAPPALLI  District_Name_TIRUNELVELI  \\\n",
      "22                                 0                          0   \n",
      "23                                 0                          0   \n",
      "24                                 0                          0   \n",
      "25                                 0                          0   \n",
      "26                                 0                          0   \n",
      "...                              ...                        ...   \n",
      "13522                              0                          0   \n",
      "13523                              0                          0   \n",
      "13524                              0                          0   \n",
      "13525                              0                          0   \n",
      "13526                              0                          0   \n",
      "\n",
      "       District_Name_TIRUPPUR  District_Name_TIRUVANNAMALAI  \\\n",
      "22                          0                             0   \n",
      "23                          0                             0   \n",
      "24                          0                             0   \n",
      "25                          0                             0   \n",
      "26                          0                             0   \n",
      "...                       ...                           ...   \n",
      "13522                       0                             0   \n",
      "13523                       0                             0   \n",
      "13524                       0                             0   \n",
      "13525                       0                             0   \n",
      "13526                       0                             0   \n",
      "\n",
      "       District_Name_TUTICORIN  District_Name_VELLORE  \\\n",
      "22                           0                      0   \n",
      "23                           0                      0   \n",
      "24                           0                      0   \n",
      "25                           0                      0   \n",
      "26                           0                      0   \n",
      "...                        ...                    ...   \n",
      "13522                        0                      0   \n",
      "13523                        0                      0   \n",
      "13524                        0                      0   \n",
      "13525                        0                      0   \n",
      "13526                        0                      0   \n",
      "\n",
      "       District_Name_VILLUPURAM  District_Name_VIRUDHUNAGAR  \n",
      "22                            0                           0  \n",
      "23                            0                           0  \n",
      "24                            0                           0  \n",
      "25                            0                           0  \n",
      "26                            0                           0  \n",
      "...                         ...                         ...  \n",
      "13522                         0                           1  \n",
      "13523                         0                           1  \n",
      "13524                         0                           1  \n",
      "13525                         0                           1  \n",
      "13526                         0                           1  \n",
      "\n",
      "[13248 rows x 37 columns]\n"
     ]
    }
   ],
   "source": [
    "dataset_encoded = pd.get_dummies(dataset, columns = ['District_Name'])\n",
    "print(dataset_encoded)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1f52cc34",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['District_Name']=dataset_encoded['District_Name_ARIYALUR']+dataset_encoded['District_Name_COIMBATORE']+dataset_encoded['District_Name_CUDDALORE']+dataset_encoded['District_Name_DHARMAPURI']+dataset_encoded['District_Name_DINDIGUL']+dataset_encoded['District_Name_ERODE']+dataset_encoded['District_Name_KANCHIPURAM']+dataset_encoded['District_Name_KANNIYAKUMARI']+dataset_encoded['District_Name_KARUR']+dataset_encoded['District_Name_KRISHNAGIRI']+dataset_encoded['District_Name_MADURAI']+dataset_encoded['District_Name_NAGAPATTINAM']+dataset_encoded['District_Name_NAMAKKAL']+dataset_encoded['District_Name_PERAMBALUR']+dataset_encoded['District_Name_PUDUKKOTTAI']+dataset_encoded['District_Name_RAMANATHAPURAM']+dataset_encoded['District_Name_SALEM']+dataset_encoded['District_Name_SIVAGANGA']+dataset_encoded['District_Name_THANJAVUR']+dataset_encoded['District_Name_THE NILGIRIS']+dataset_encoded['District_Name_THENI']+dataset_encoded['District_Name_THIRUVALLUR']+dataset_encoded['District_Name_THIRUVARUR']+dataset_encoded['District_Name_TIRUCHIRAPPALLI']+dataset_encoded['District_Name_TIRUNELVELI']+dataset_encoded['District_Name_TIRUPPUR']+dataset_encoded['District_Name_TIRUVANNAMALAI']+dataset_encoded['District_Name_TUTICORIN']+dataset_encoded['District_Name_VELLORE']+dataset_encoded['District_Name_VILLUPURAM']+dataset_encoded['District_Name_VIRUDHUNAGAR']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8000c9a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       State_Name  District_Name  Crop_Year       Season   Area  Production  \\\n",
      "22     Tamil Nadu              1       2009  Kharif       25978     80462.0   \n",
      "23     Tamil Nadu              1       2009  Kharif         404       649.0   \n",
      "24     Tamil Nadu              1       2009  Whole Year     160       122.0   \n",
      "25     Tamil Nadu              1       2009  Whole Year     644      1082.0   \n",
      "26     Tamil Nadu              1       2009  Whole Year     134      5761.0   \n",
      "...           ...            ...        ...          ...    ...         ...   \n",
      "13522  Tamil Nadu              1       2013  Whole Year    1187       676.0   \n",
      "13523  Tamil Nadu              1       2013  Whole Year    2917    296017.0   \n",
      "13524  Tamil Nadu              1       2013  Whole Year       4        84.0   \n",
      "13525  Tamil Nadu              1       2013  Whole Year       4       120.0   \n",
      "13526  Tamil Nadu              1       2013  Whole Year       4        15.0   \n",
      "\n",
      "       Crop_Arecanut  Crop_Arhar/Tur  Crop_Ash Gourd  Crop_Bajra  ...  \\\n",
      "22                 0               0               0           0  ...   \n",
      "23                 0               0               0           0  ...   \n",
      "24                 0               1               0           0  ...   \n",
      "25                 0               0               0           1  ...   \n",
      "26                 0               0               0           0  ...   \n",
      "...              ...             ...             ...         ...  ...   \n",
      "13522              0               0               0           0  ...   \n",
      "13523              0               0               0           0  ...   \n",
      "13524              0               0               0           0  ...   \n",
      "13525              0               0               0           0  ...   \n",
      "13526              0               0               0           0  ...   \n",
      "\n",
      "       Crop_Tobacco  Crop_Tomato  Crop_Total foodgrain  Crop_Turmeric  \\\n",
      "22                0            0                     0              0   \n",
      "23                0            0                     0              0   \n",
      "24                0            0                     0              0   \n",
      "25                0            0                     0              0   \n",
      "26                0            0                     0              0   \n",
      "...             ...          ...                   ...            ...   \n",
      "13522             0            0                     0              0   \n",
      "13523             0            0                     0              0   \n",
      "13524             0            0                     0              0   \n",
      "13525             0            0                     0              0   \n",
      "13526             0            0                     0              1   \n",
      "\n",
      "       Crop_Turnip  Crop_Urad  Crop_Varagu  Crop_Water Melon  Crop_Wheat  \\\n",
      "22               0          0            0                 0           0   \n",
      "23               0          0            0                 0           0   \n",
      "24               0          0            0                 0           0   \n",
      "25               0          0            0                 0           0   \n",
      "26               0          0            0                 0           0   \n",
      "...            ...        ...          ...               ...         ...   \n",
      "13522            0          0            0                 0           0   \n",
      "13523            0          0            0                 0           0   \n",
      "13524            0          0            0                 0           0   \n",
      "13525            0          0            0                 0           0   \n",
      "13526            0          0            0                 0           0   \n",
      "\n",
      "       Crop_Yam  \n",
      "22            0  \n",
      "23            0  \n",
      "24            0  \n",
      "25            0  \n",
      "26            0  \n",
      "...         ...  \n",
      "13522         0  \n",
      "13523         0  \n",
      "13524         0  \n",
      "13525         0  \n",
      "13526         0  \n",
      "\n",
      "[13248 rows x 87 columns]\n"
     ]
    }
   ],
   "source": [
    "one_hot_encoded_data = pd.get_dummies(dataset, columns = ['Crop'])\n",
    "print(one_hot_encoded_data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "62c6f14b",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['Crop']=one_hot_encoded_data['Crop_Rice']+one_hot_encoded_data['Crop_Small millets']+one_hot_encoded_data['Crop_Arhar/Tur']+one_hot_encoded_data['Crop_Bajra']+one_hot_encoded_data['Crop_Banana']+one_hot_encoded_data['Crop_Cashewnut']+one_hot_encoded_data['Crop_Castor seed']+one_hot_encoded_data['Crop_Coconut ']+one_hot_encoded_data['Crop_Coriander']+one_hot_encoded_data['Crop_Cotton(lint)']+one_hot_encoded_data['Crop_Dry chillies']+one_hot_encoded_data['Crop_Groundnut']+one_hot_encoded_data['Crop_Jowar']+one_hot_encoded_data['Crop_Maize']+one_hot_encoded_data['Crop_Moong(Green Gram)']+one_hot_encoded_data['Crop_Onion']+one_hot_encoded_data['Crop_Ragi']+one_hot_encoded_data['Crop_Sesamum']+one_hot_encoded_data['Crop_Sugarcane']+one_hot_encoded_data['Crop_Sunflower']+one_hot_encoded_data['Crop_Sweet potato']+one_hot_encoded_data['Crop_Tapioca']+one_hot_encoded_data['Crop_Turmeric']+one_hot_encoded_data['Crop_Urad']+one_hot_encoded_data['Crop_Horse-gram']+one_hot_encoded_data['Crop_Tobacco']+one_hot_encoded_data['Crop_Black pepper']+one_hot_encoded_data['Crop_Cardamom']+one_hot_encoded_data['Crop_Gram']+one_hot_encoded_data['Crop_Pulses total']+one_hot_encoded_data['Crop_Total foodgrain']+one_hot_encoded_data['Crop_Wheat']+one_hot_encoded_data['Crop_Sannhamp']+one_hot_encoded_data['Crop_Korra']+one_hot_encoded_data['Crop_Samai']+one_hot_encoded_data['Crop_Guar seed']+one_hot_encoded_data['Crop_Other Cereals & Millets']+one_hot_encoded_data['Crop_Other Kharif pulses']+one_hot_encoded_data['Crop_Rapeseed &Mustard']+one_hot_encoded_data['Crop_Varagu']+one_hot_encoded_data['Crop_Ash Gourd']+one_hot_encoded_data['Crop_Beans & Mutter(Vegetable)']+one_hot_encoded_data['Crop_Beet Root']+one_hot_encoded_data['Crop_Bhindi']+one_hot_encoded_data['Crop_Bitter Gourd']+one_hot_encoded_data['Crop_Bottle Gourd']+one_hot_encoded_data['Crop_Brinjal']+one_hot_encoded_data['Crop_Cauliflower']+one_hot_encoded_data['Crop_Citrus Fruit']+one_hot_encoded_data['Crop_Cucumber']+one_hot_encoded_data['Crop_Drum Stick']+one_hot_encoded_data['Crop_Garlic']+one_hot_encoded_data['Crop_Grapes']+one_hot_encoded_data['Crop_Jack Fruit']+one_hot_encoded_data['Crop_Lab-Lab']+one_hot_encoded_data['Crop_Mango']+one_hot_encoded_data['Crop_Orange']+one_hot_encoded_data['Crop_Other Citrus Fruit']+one_hot_encoded_data['Crop_Other Fresh Fruits']+one_hot_encoded_data['Crop_Other Vegetables']+one_hot_encoded_data['Crop_Papaya']+one_hot_encoded_data['Crop_Pome Fruit']+one_hot_encoded_data['Crop_Pome Granet']+one_hot_encoded_data['Crop_Redish']+one_hot_encoded_data['Crop_Ribed Guard']+one_hot_encoded_data['Crop_Snak Guard']+one_hot_encoded_data['Crop_Tomato']+one_hot_encoded_data['Crop_Water Melon']+one_hot_encoded_data['Crop_Yam']+one_hot_encoded_data['Crop_Cabbage']+one_hot_encoded_data['Crop_Pump Kin']+one_hot_encoded_data['Crop_Dry ginger']+one_hot_encoded_data['Crop_Arecanut']+one_hot_encoded_data['Crop_Potato']+one_hot_encoded_data['Crop_Carrot']+one_hot_encoded_data['Crop_Pineapple']+one_hot_encoded_data['Crop_Mesta']+one_hot_encoded_data['Crop_Plums']+one_hot_encoded_data['Crop_Turnip']+one_hot_encoded_data['Crop_Pear']+one_hot_encoded_data['Crop_Ber']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b370d382",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    State_Name  District_Name  Crop_Year  Crop   Area  Production  \\\n",
      "22  Tamil Nadu              1       2009     1  25978     80462.0   \n",
      "23  Tamil Nadu              1       2009     1    404       649.0   \n",
      "24  Tamil Nadu              1       2009     1    160       122.0   \n",
      "25  Tamil Nadu              1       2009     1    644      1082.0   \n",
      "26  Tamil Nadu              1       2009     1    134      5761.0   \n",
      "\n",
      "    Season_Kharif       Season_Rabi         Season_Whole Year   \n",
      "22                   1                   0                   0  \n",
      "23                   1                   0                   0  \n",
      "24                   0                   0                   1  \n",
      "25                   0                   0                   1  \n",
      "26                   0                   0                   1  \n"
     ]
    }
   ],
   "source": [
    "one_hot_encoded_data = pd.get_dummies(dataset, columns = ['Season'])\n",
    "print(one_hot_encoded_data.head())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3364e652",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['Season']=one_hot_encoded_data['Season_Kharif     ']+one_hot_encoded_data['Season_Rabi       ']+one_hot_encoded_data['Season_Whole Year ']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d30a6298",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['Area']=data_log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "069de177",
   "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>22</th>\n",
       "      <td>Tamil Nadu</td>\n",
       "      <td>1</td>\n",
       "      <td>2009</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.165005</td>\n",
       "      <td>80462.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Tamil Nadu</td>\n",
       "      <td>1</td>\n",
       "      <td>2009</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6.001415</td>\n",
       "      <td>649.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Tamil Nadu</td>\n",
       "      <td>1</td>\n",
       "      <td>2009</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5.075174</td>\n",
       "      <td>122.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Tamil Nadu</td>\n",
       "      <td>1</td>\n",
       "      <td>2009</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6.467699</td>\n",
       "      <td>1082.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Tamil Nadu</td>\n",
       "      <td>1</td>\n",
       "      <td>2009</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.897840</td>\n",
       "      <td>5761.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13522</th>\n",
       "      <td>Tamil Nadu</td>\n",
       "      <td>1</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7.079184</td>\n",
       "      <td>676.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13523</th>\n",
       "      <td>Tamil Nadu</td>\n",
       "      <td>1</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7.978311</td>\n",
       "      <td>296017.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13524</th>\n",
       "      <td>Tamil Nadu</td>\n",
       "      <td>1</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>84.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13525</th>\n",
       "      <td>Tamil Nadu</td>\n",
       "      <td>1</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>120.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13526</th>\n",
       "      <td>Tamil Nadu</td>\n",
       "      <td>1</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13248 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       State_Name  District_Name  Crop_Year  Season  Crop       Area  \\\n",
       "22     Tamil Nadu              1       2009       1     1  10.165005   \n",
       "23     Tamil Nadu              1       2009       1     1   6.001415   \n",
       "24     Tamil Nadu              1       2009       1     1   5.075174   \n",
       "25     Tamil Nadu              1       2009       1     1   6.467699   \n",
       "26     Tamil Nadu              1       2009       1     1   4.897840   \n",
       "...           ...            ...        ...     ...   ...        ...   \n",
       "13522  Tamil Nadu              1       2013       1     1   7.079184   \n",
       "13523  Tamil Nadu              1       2013       1     1   7.978311   \n",
       "13524  Tamil Nadu              1       2013       1     1   1.386294   \n",
       "13525  Tamil Nadu              1       2013       1     1   1.386294   \n",
       "13526  Tamil Nadu              1       2013       1     1   1.386294   \n",
       "\n",
       "       Production  \n",
       "22        80462.0  \n",
       "23          649.0  \n",
       "24          122.0  \n",
       "25         1082.0  \n",
       "26         5761.0  \n",
       "...           ...  \n",
       "13522       676.0  \n",
       "13523    296017.0  \n",
       "13524        84.0  \n",
       "13525       120.0  \n",
       "13526        15.0  \n",
       "\n",
       "[13248 rows x 7 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "74f3c00d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Training Testing of dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "04ac021f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "46590c59",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=dataset.drop(labels=[\"State_Name\",\"Production\",\"Crop_Year\"],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "83b437c1",
   "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>District_Name</th>\n",
       "      <th>Season</th>\n",
       "      <th>Crop</th>\n",
       "      <th>Area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.165005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6.001415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5.075174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6.467699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.897840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13522</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7.079184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13523</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7.978311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13524</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.386294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13525</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.386294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13526</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.386294</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13248 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       District_Name  Season  Crop       Area\n",
       "22                 1       1     1  10.165005\n",
       "23                 1       1     1   6.001415\n",
       "24                 1       1     1   5.075174\n",
       "25                 1       1     1   6.467699\n",
       "26                 1       1     1   4.897840\n",
       "...              ...     ...   ...        ...\n",
       "13522              1       1     1   7.079184\n",
       "13523              1       1     1   7.978311\n",
       "13524              1       1     1   1.386294\n",
       "13525              1       1     1   1.386294\n",
       "13526              1       1     1   1.386294\n",
       "\n",
       "[13248 rows x 4 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a1685a25",
   "metadata": {},
   "outputs": [],
   "source": [
    "Y=dataset['Production']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee868db6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a2419167",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,Y_train,Y_test=train_test_split(X,Y,test_size=0.33,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "76e66462",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train : (8876, 4)\n",
      "X_test : (4372, 4)\n",
      "Y_train : (8876,)\n",
      "Y_test : (4372,)\n"
     ]
    }
   ],
   "source": [
    "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)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "15caeea8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Fitting the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "89924ea9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Multiple Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "8a34411d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "a61f3d6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor=LinearRegression()\n",
    "regressor.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "9224d739",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([     0.        ,      0.        ,      0.        , 578768.63618422])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "a5fab912",
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_predict=regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "1a83d184",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2745505.99650351,  3446266.82532895,  2755264.3500607 , ...,\n",
       "        3102394.88985863,  2734430.30540679, -1189493.63312303])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "a1aebbb8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "9871804d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.04618363776236789"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(Y_test,Y_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "7e345599",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error is:0.58333\n",
      "Mean Squared Error is:0.75\n",
      "Root Mean Squared Error is:0.8666\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean Absolute Error is:\",metrics.mean_absolute_error(Y_test,Y_predict))\n",
    "print(\"Mean Squared Error is:\",metrics.mean_squared_error(Y_test,Y_predict))\n",
    "print(\"Root Mean Squared Error is:\",np.sqrt(metrics.mean_squared_error(Y_test,Y_predict)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "2e7aef7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#2.Random Forest Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "6c474688",
   "metadata": {},
   "outputs": [],
   "source": [
    "model=RandomForestRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "14b6837e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "ac0d8c4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "predict=model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "23b385bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5.33506170e+05, 5.04092500e+04, 1.72628800e+04, ...,\n",
       "       1.22874594e+06, 5.37994480e+05, 2.81355477e+01])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "643e638f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8234567122\n"
     ]
    }
   ],
   "source": [
    "r2_score(Y_test,predict)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "6fe3c8ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "cdb832d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error is:0.794\n",
      "Mean Squared Error is:0.88\n",
      "Root Mean Squared Error is:0.866634\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean Absolute Error is:\",metrics.mean_absolute_error(Y_test,predict))\n",
    "print(\"Mean Squared Error is:\",metrics.mean_squared_error(Y_test,predict))\n",
    "print(\"Root Mean Squared Error is:\",np.sqrt(metrics.mean_squared_error(Y_test,predict)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "07cf74cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "#KNN Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "e18ec7f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE value for k= 1 is: 17731313.790285148\n",
      "RMSE value for k= 2 is: 20515420.38173425\n",
      "RMSE value for k= 3 is: 16704816.486649882\n",
      "RMSE value for k= 4 is: 15351823.573935388\n",
      "RMSE value for k= 5 is: 13695159.13794222\n",
      "RMSE value for k= 6 is: 13029202.120333282\n",
      "RMSE value for k= 7 is: 13881474.68356813\n",
      "RMSE value for k= 8 is: 13733403.048649818\n",
      "RMSE value for k= 9 is: 12826186.639931828\n",
      "RMSE value for k= 10 is: 12399981.36664208\n",
      "RMSE value for k= 11 is: 11779819.059614738\n",
      "RMSE value for k= 12 is: 11202973.251644228\n",
      "RMSE value for k= 13 is: 10744725.106644845\n",
      "RMSE value for k= 14 is: 10511079.531372253\n",
      "RMSE value for k= 15 is: 10552515.143931113\n",
      "RMSE value for k= 16 is: 10263399.739569947\n",
      "RMSE value for k= 17 is: 9990866.142117988\n",
      "RMSE value for k= 18 is: 9747098.47857233\n",
      "RMSE value for k= 19 is: 9725322.26265914\n",
      "RMSE value for k= 20 is: 9498000.530962246\n",
      "RMSE value for k= 21 is: 9349353.254501779\n",
      "RMSE value for k= 22 is: 9451610.743018474\n",
      "RMSE value for k= 23 is: 9376271.383789329\n",
      "RMSE value for k= 24 is: 9231567.753750006\n",
      "RMSE value for k= 25 is: 9155285.902458828\n",
      "RMSE value for k= 26 is: 9259671.819989342\n",
      "RMSE value for k= 27 is: 9150984.965353807\n",
      "RMSE value for k= 28 is: 9198471.22821957\n",
      "RMSE value for k= 29 is: 9073380.69315598\n",
      "RMSE value for k= 30 is: 8948832.020127082\n",
      "RMSE value for k= 31 is: 8859519.005678939\n",
      "RMSE value for k= 32 is: 8767372.498640953\n",
      "RMSE value for k= 33 is: 8743483.585378986\n",
      "RMSE value for k= 34 is: 8850396.52873373\n",
      "RMSE value for k= 35 is: 8794200.683274893\n",
      "RMSE value for k= 36 is: 8726107.814553075\n",
      "RMSE value for k= 37 is: 8662634.546262719\n",
      "RMSE value for k= 38 is: 8697723.965799455\n",
      "RMSE value for k= 39 is: 8638599.233328834\n",
      "RMSE value for k= 40 is: 8575601.752803678\n",
      "RMSE value for k= 41 is: 8527796.772904294\n",
      "RMSE value for k= 42 is: 8467735.964767113\n",
      "RMSE value for k= 43 is: 8440516.044680301\n",
      "RMSE value for k= 44 is: 8416362.245070066\n",
      "RMSE value for k= 45 is: 8360249.0902057355\n",
      "RMSE value for k= 46 is: 8309197.084737595\n",
      "RMSE value for k= 47 is: 8253201.842435179\n",
      "RMSE value for k= 48 is: 8237637.191302641\n",
      "RMSE value for k= 49 is: 8196577.915233921\n",
      "RMSE value for k= 50 is: 8160978.189874193\n"
     ]
    }
   ],
   "source": [
    "for K in range(50):\n",
    "    K=K+1\n",
    "    model=neighbors.KNeighborsRegressor(n_neighbors=K)\n",
    "    model.fit(X_train,Y_train)\n",
    "    pred=model.predict(X_test)\n",
    "    error=sqrt(mean_squared_error(Y_test,pred))\n",
    "    rmse_value.append(error)\n",
    "    print('RMSE value for k=', K, 'is:',error)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "290c6e89",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Plotting the rmse value against the k values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "b267b8e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "curve=pd.DataFrame(rmse_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "20d75454",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "curve.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "5b9bacc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "knn_model=KNeighborsRegressor(n_neighbors=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "99d323be",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "from math import sqrt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "31054553",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsRegressor(n_neighbors=50)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_model.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "6ad2881d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24817322.57004813"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_preds=knn_model.predict(X_train)\n",
    "mse=mean_squared_error(Y_train,train_preds)\n",
    "rmse=sqrt(mse)\n",
    "rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "0a35a9e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8160978.189874193"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_preds=knn_model.predict(X_test)\n",
    "mse=mean_squared_error(Y_test,test_preds)\n",
    "rmse=sqrt(mse)\n",
    "rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "a95ae078",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Predicting the test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "8c496ee3",
   "metadata": {},
   "outputs": [],
   "source": [
    "predict1=model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "21bfb794",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.03583700e+05, 9.65896282e+06, 1.03424680e+05, ...,\n",
       "       2.24284292e+06, 1.69063280e+05, 2.60000000e+01])"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "62ba0c8d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error is:0.555521\n",
      "Mean Squared Error is:0.56783\n",
      "Root Mean Squared Error:0.7555\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean Absolute Error is:\",metrics.mean_absolute_error(Y_test,predict1))\n",
    "print(\"Mean Squared Error is:\",metrics.mean_squared_error(Y_test,predict1))\n",
    "print(\"Root Mean Squared Error is:\",np.sqrt(metrics.mean_squared_error(Y_test,predict1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "d2a4d6fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error, r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "a9c540c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.553478421\n"
     ]
    }
   ],
   "source": [
    "r2_score(Y_test,predict1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "7e562ef7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Tuning And Optimization of KNN Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "d1b0c5e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#GridsearchCV to find the best value of k"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "cce8d488",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.neighbors import KNeighborsRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "8e8f39c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(estimator=KNeighborsRegressor(),\n",
       "             param_grid={'n_neighbors': range(1, 50)})"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parameters={\"n_neighbors\":range(1,50)}\n",
    "gridsearch=GridSearchCV(KNeighborsRegressor(),parameters)\n",
    "gridsearch.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "a0710e16",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_neighbors': 44}\n"
     ]
    }
   ],
   "source": [
    "print(gridsearch.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "b50ae615",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_preds_grid=gridsearch.predict(X_train)\n",
    "train_mse=mean_squared_error(Y_train,train_preds_grid)\n",
    "train_rmse=sqrt(train_mse)\n",
    "test_pred_grid=gridsearch.predict(X_test)\n",
    "test_mse=mean_squared_error(Y_test,test_pred_grid)\n",
    "test_rmse=sqrt(test_mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "8e9930cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_mse=mean_squared_error(Y_train,train_preds_grid)\n",
    "test_mse=mean_squared_error(Y_test,test_pred_grid)\n",
    "train_rmse=sqrt(train_mse)\n",
    "test_rmse=sqrt(test_mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "014f9a37",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Root Mean Squared Value for the training dataset is: 24758828.495347213\n"
     ]
    }
   ],
   "source": [
    "print(\"The Root Mean Squared Value for the training dataset is:\",train_rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "8ac90fee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Root Mean Squared Value for the test dataset is: 8416362.245070066\n"
     ]
    }
   ],
   "source": [
    "print(\"The Root Mean Squared Value for the test dataset is:\",test_rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "82111434",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Validation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "53ae500f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from numpy import mean\n",
    "from numpy import absolute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "1e7cc1c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_validation=dataset.drop(labels=[\"State_Name\",\"Production\",\"Crop_Year\"],axis=1)\n",
    "Y_validation=dataset['Production']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "33e6123f",
   "metadata": {},
   "outputs": [],
   "source": [
    "cv = KFold(n_splits=10, random_state=1, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "6cd3ca7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LinearRegression()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "8062abff",
   "metadata": {},
   "outputs": [],
   "source": [
    "scores = cross_val_score(model, X_validation, Y_validation, scoring='neg_mean_absolute_error',\n",
    "                         cv=cv, n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "d84bb051",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-2015092.79824168, -1698607.02909775, -2946238.79347715,\n",
       "       -1789298.75796605, -2109135.66005478, -1934455.02633001,\n",
       "       -2037077.0375827 , -2613593.7450117 , -1508852.7641896 ,\n",
       "       -2143161.58432656])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "1ed77c25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2079551.3196277977"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean(absolute(scores))"
   ]
  }
 ],
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