analysisAndModel.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "from sklearn import tree\n",
    "from ipykernel import kernelapp as app\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import rcParams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "_uuid": "88c4351df7a08a898fef6cc5ae0f6985c4535eeb",
    "scrolled": true
   },
   "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>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</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</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</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</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>NICOBARS</td>\n",
       "      <td>2000</td>\n",
       "      <td>Whole Year</td>\n",
       "      <td>Coconut</td>\n",
       "      <td>18168.0</td>\n",
       "      <td>65100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>NICOBARS</td>\n",
       "      <td>2000</td>\n",
       "      <td>Whole Year</td>\n",
       "      <td>Dry ginger</td>\n",
       "      <td>36.0</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>NICOBARS</td>\n",
       "      <td>2000</td>\n",
       "      <td>Whole Year</td>\n",
       "      <td>Sugarcane</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>NICOBARS</td>\n",
       "      <td>2000</td>\n",
       "      <td>Whole Year</td>\n",
       "      <td>Sweet potato</td>\n",
       "      <td>5.0</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>NICOBARS</td>\n",
       "      <td>2000</td>\n",
       "      <td>Whole Year</td>\n",
       "      <td>Tapioca</td>\n",
       "      <td>40.0</td>\n",
       "      <td>169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>NICOBARS</td>\n",
       "      <td>2001</td>\n",
       "      <td>Kharif</td>\n",
       "      <td>Arecanut</td>\n",
       "      <td>1254.0</td>\n",
       "      <td>2061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>NICOBARS</td>\n",
       "      <td>2001</td>\n",
       "      <td>Kharif</td>\n",
       "      <td>Other Kharif pulses</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>NICOBARS</td>\n",
       "      <td>2001</td>\n",
       "      <td>Kharif</td>\n",
       "      <td>Rice</td>\n",
       "      <td>83.0</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>NICOBARS</td>\n",
       "      <td>2001</td>\n",
       "      <td>Whole Year</td>\n",
       "      <td>Cashewnut</td>\n",
       "      <td>719.0</td>\n",
       "      <td>192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>NICOBARS</td>\n",
       "      <td>2001</td>\n",
       "      <td>Whole Year</td>\n",
       "      <td>Coconut</td>\n",
       "      <td>18190.0</td>\n",
       "      <td>64430000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>NICOBARS</td>\n",
       "      <td>2001</td>\n",
       "      <td>Whole Year</td>\n",
       "      <td>Dry ginger</td>\n",
       "      <td>46.0</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>NICOBARS</td>\n",
       "      <td>2001</td>\n",
       "      <td>Whole Year</td>\n",
       "      <td>Sugarcane</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>NICOBARS</td>\n",
       "      <td>2001</td>\n",
       "      <td>Whole Year</td>\n",
       "      <td>Sweet potato</td>\n",
       "      <td>11.0</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>NICOBARS</td>\n",
       "      <td>2002</td>\n",
       "      <td>Kharif</td>\n",
       "      <td>Rice</td>\n",
       "      <td>189.2</td>\n",
       "      <td>510.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>NICOBARS</td>\n",
       "      <td>2002</td>\n",
       "      <td>Whole Year</td>\n",
       "      <td>Arecanut</td>\n",
       "      <td>1258.0</td>\n",
       "      <td>2083</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",
       "5   Andaman and Nicobar Islands      NICOBARS       2000  Whole Year    \n",
       "6   Andaman and Nicobar Islands      NICOBARS       2000  Whole Year    \n",
       "7   Andaman and Nicobar Islands      NICOBARS       2000  Whole Year    \n",
       "8   Andaman and Nicobar Islands      NICOBARS       2000  Whole Year    \n",
       "9   Andaman and Nicobar Islands      NICOBARS       2000  Whole Year    \n",
       "10  Andaman and Nicobar Islands      NICOBARS       2001  Kharif        \n",
       "11  Andaman and Nicobar Islands      NICOBARS       2001  Kharif        \n",
       "12  Andaman and Nicobar Islands      NICOBARS       2001  Kharif        \n",
       "13  Andaman and Nicobar Islands      NICOBARS       2001  Whole Year    \n",
       "14  Andaman and Nicobar Islands      NICOBARS       2001  Whole Year    \n",
       "15  Andaman and Nicobar Islands      NICOBARS       2001  Whole Year    \n",
       "16  Andaman and Nicobar Islands      NICOBARS       2001  Whole Year    \n",
       "17  Andaman and Nicobar Islands      NICOBARS       2001  Whole Year    \n",
       "18  Andaman and Nicobar Islands      NICOBARS       2002  Kharif        \n",
       "19  Andaman and Nicobar Islands      NICOBARS       2002  Whole Year    \n",
       "\n",
       "                   Crop     Area Production  \n",
       "0              Arecanut   1254.0       2000  \n",
       "1   Other Kharif pulses      2.0          1  \n",
       "2                  Rice    102.0        321  \n",
       "3                Banana    176.0        641  \n",
       "4             Cashewnut    720.0        165  \n",
       "5              Coconut   18168.0   65100000  \n",
       "6            Dry ginger     36.0        100  \n",
       "7             Sugarcane      1.0          2  \n",
       "8          Sweet potato      5.0         15  \n",
       "9               Tapioca     40.0        169  \n",
       "10             Arecanut   1254.0       2061  \n",
       "11  Other Kharif pulses      2.0          1  \n",
       "12                 Rice     83.0        300  \n",
       "13            Cashewnut    719.0        192  \n",
       "14             Coconut   18190.0   64430000  \n",
       "15           Dry ginger     46.0        100  \n",
       "16            Sugarcane      1.0          1  \n",
       "17         Sweet potato     11.0         33  \n",
       "18                 Rice    189.2     510.84  \n",
       "19             Arecanut   1258.0       2083  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"AgrcultureDataset.csv\")\n",
    "df.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Production']=pd.to_numeric(df['Production'],errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "_uuid": "ee5413b75ef49dd243ff0b778744a977df671fb3"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-83-ad7dd90c8505>:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  data=df.groupby(['Crop_Year'])['Area','Production'].mean()\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Crop_Year</th>\n",
       "      <th>Area</th>\n",
       "      <th>Production</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1997</td>\n",
       "      <td>26038.324081</td>\n",
       "      <td>9.565489e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1998</td>\n",
       "      <td>14479.153906</td>\n",
       "      <td>5.172545e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1999</td>\n",
       "      <td>12678.074790</td>\n",
       "      <td>5.172145e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2000</td>\n",
       "      <td>12102.612169</td>\n",
       "      <td>5.496723e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2001</td>\n",
       "      <td>12371.499489</td>\n",
       "      <td>5.616144e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2002</td>\n",
       "      <td>9463.680476</td>\n",
       "      <td>4.654666e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2003</td>\n",
       "      <td>9954.769395</td>\n",
       "      <td>4.619857e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2004</td>\n",
       "      <td>11891.933465</td>\n",
       "      <td>5.909555e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2005</td>\n",
       "      <td>11822.333236</td>\n",
       "      <td>5.949085e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2006</td>\n",
       "      <td>11913.672644</td>\n",
       "      <td>6.211571e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2007</td>\n",
       "      <td>10513.848637</td>\n",
       "      <td>4.821251e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2008</td>\n",
       "      <td>11768.527148</td>\n",
       "      <td>5.423063e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2009</td>\n",
       "      <td>11738.077997</td>\n",
       "      <td>5.564389e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2010</td>\n",
       "      <td>12557.355280</td>\n",
       "      <td>4.573050e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2011</td>\n",
       "      <td>10918.140920</td>\n",
       "      <td>1.037554e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2012</td>\n",
       "      <td>11369.858240</td>\n",
       "      <td>6.197705e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2013</td>\n",
       "      <td>10368.125223</td>\n",
       "      <td>9.575947e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2014</td>\n",
       "      <td>10549.306622</td>\n",
       "      <td>8.011596e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2015</td>\n",
       "      <td>8187.362989</td>\n",
       "      <td>1.236197e+04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Crop_Year          Area    Production\n",
       "0        1997  26038.324081  9.565489e+04\n",
       "1        1998  14479.153906  5.172545e+05\n",
       "2        1999  12678.074790  5.172145e+05\n",
       "3        2000  12102.612169  5.496723e+05\n",
       "4        2001  12371.499489  5.616144e+05\n",
       "5        2002   9463.680476  4.654666e+05\n",
       "6        2003   9954.769395  4.619857e+05\n",
       "7        2004  11891.933465  5.909555e+05\n",
       "8        2005  11822.333236  5.949085e+05\n",
       "9        2006  11913.672644  6.211571e+05\n",
       "10       2007  10513.848637  4.821251e+05\n",
       "11       2008  11768.527148  5.423063e+05\n",
       "12       2009  11738.077997  5.564389e+05\n",
       "13       2010  12557.355280  4.573050e+05\n",
       "14       2011  10918.140920  1.037554e+06\n",
       "15       2012  11369.858240  6.197705e+05\n",
       "16       2013  10368.125223  9.575947e+05\n",
       "17       2014  10549.306622  8.011596e+05\n",
       "18       2015   8187.362989  1.236197e+04"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=df.groupby(['Crop_Year'])['Area','Production'].mean()\n",
    "data=data.reset_index(level=0, inplace=False)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "_uuid": "7b2db82945dd22230e210abd188cada5f1f5a7b5"
   },
   "outputs": [
    {
     "data": {
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       "<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",
       "      <th>CPI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1997</td>\n",
       "      <td>26038.324081</td>\n",
       "      <td>95654.894483</td>\n",
       "      <td>3.673619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1998</td>\n",
       "      <td>14479.153906</td>\n",
       "      <td>517254.540970</td>\n",
       "      <td>35.724086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1999</td>\n",
       "      <td>12678.074790</td>\n",
       "      <td>517214.531396</td>\n",
       "      <td>40.795984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2000</td>\n",
       "      <td>12102.612169</td>\n",
       "      <td>549672.332849</td>\n",
       "      <td>45.417661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2001</td>\n",
       "      <td>12371.499489</td>\n",
       "      <td>561614.446722</td>\n",
       "      <td>45.395827</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Crop_Year          Area     Production        CPI\n",
       "0       1997  26038.324081   95654.894483   3.673619\n",
       "1       1998  14479.153906  517254.540970  35.724086\n",
       "2       1999  12678.074790  517214.531396  40.795984\n",
       "3       2000  12102.612169  549672.332849  45.417661\n",
       "4       2001  12371.499489  561614.446722  45.395827"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['CPI']=data['Production']/data['Area']\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "_uuid": "204f7d9c2b9d2552e2b8426e01c839212461c735"
   },
   "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",
       "      <th>CPI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>19.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>1.900000e+01</td>\n",
       "      <td>19.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2006.000000</td>\n",
       "      <td>12141.402985</td>\n",
       "      <td>5.496053e+05</td>\n",
       "      <td>48.098207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.627314</td>\n",
       "      <td>3633.397954</td>\n",
       "      <td>2.364676e+05</td>\n",
       "      <td>22.993961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1997.000000</td>\n",
       "      <td>8187.362989</td>\n",
       "      <td>1.236197e+04</td>\n",
       "      <td>1.509884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2001.500000</td>\n",
       "      <td>10531.577629</td>\n",
       "      <td>4.737958e+05</td>\n",
       "      <td>43.095905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2006.000000</td>\n",
       "      <td>11768.527148</td>\n",
       "      <td>5.496723e+05</td>\n",
       "      <td>46.408482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2010.500000</td>\n",
       "      <td>12237.055829</td>\n",
       "      <td>6.073395e+05</td>\n",
       "      <td>51.229452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2015.000000</td>\n",
       "      <td>26038.324081</td>\n",
       "      <td>1.037554e+06</td>\n",
       "      <td>95.030260</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Crop_Year          Area    Production        CPI\n",
       "count    19.000000     19.000000  1.900000e+01  19.000000\n",
       "mean   2006.000000  12141.402985  5.496053e+05  48.098207\n",
       "std       5.627314   3633.397954  2.364676e+05  22.993961\n",
       "min    1997.000000   8187.362989  1.236197e+04   1.509884\n",
       "25%    2001.500000  10531.577629  4.737958e+05  43.095905\n",
       "50%    2006.000000  11768.527148  5.496723e+05  46.408482\n",
       "75%    2010.500000  12237.055829  6.073395e+05  51.229452\n",
       "max    2015.000000  26038.324081  1.037554e+06  95.030260"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Box plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "_uuid": "3f4023c9cdd55472f498fb6555f3a70e6db8b2de"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='CPI'>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "seaborn.boxplot(x=data['CPI'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "_uuid": "07b930f9f69628840e7abdaa0fff8c84f888789c"
   },
   "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",
       "      <th>CPI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2000</td>\n",
       "      <td>12102.612169</td>\n",
       "      <td>549672.332849</td>\n",
       "      <td>45.417661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2001</td>\n",
       "      <td>12371.499489</td>\n",
       "      <td>561614.446722</td>\n",
       "      <td>45.395827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2002</td>\n",
       "      <td>9463.680476</td>\n",
       "      <td>465466.567649</td>\n",
       "      <td>49.184519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2003</td>\n",
       "      <td>9954.769395</td>\n",
       "      <td>461985.734566</td>\n",
       "      <td>46.408482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2004</td>\n",
       "      <td>11891.933465</td>\n",
       "      <td>590955.527122</td>\n",
       "      <td>49.693814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2005</td>\n",
       "      <td>11822.333236</td>\n",
       "      <td>594908.463112</td>\n",
       "      <td>50.320732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2007</td>\n",
       "      <td>10513.848637</td>\n",
       "      <td>482125.050009</td>\n",
       "      <td>45.856191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2008</td>\n",
       "      <td>11768.527148</td>\n",
       "      <td>542306.282654</td>\n",
       "      <td>46.081067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2009</td>\n",
       "      <td>11738.077997</td>\n",
       "      <td>556438.877374</td>\n",
       "      <td>47.404599</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Crop_Year          Area     Production        CPI\n",
       "3        2000  12102.612169  549672.332849  45.417661\n",
       "4        2001  12371.499489  561614.446722  45.395827\n",
       "5        2002   9463.680476  465466.567649  49.184519\n",
       "6        2003   9954.769395  461985.734566  46.408482\n",
       "7        2004  11891.933465  590955.527122  49.693814\n",
       "8        2005  11822.333236  594908.463112  50.320732\n",
       "10       2007  10513.848637  482125.050009  45.856191\n",
       "11       2008  11768.527148  542306.282654  46.081067\n",
       "12       2009  11738.077997  556438.877374  47.404599"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data[np.isfinite(data['CPI'])]\n",
    "data=data[data.CPI >42]\n",
    "data=data[data.CPI <52]\n",
    "data.set_index('Crop_Year')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corrmat = data.corr()\n",
    "f, ax = plt.subplots(figsize=(12, 9))\n",
    "seaborn.heatmap(corrmat, vmax=.8, square=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ashutoshverma/opt/anaconda3/lib/python3.8/site-packages/seaborn/axisgrid.py:1912: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "seaborn.set()\n",
    "cols = ['Crop_Year', 'Area', 'Production', 'CPI']\n",
    "seaborn.pairplot(data[cols], size = 2.5)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "_uuid": "d99d2ac4a35c1159e5eb6160726cb35e7ac2ac24"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 1 of 12\n",
      "building tree 2 of 12\n",
      "building tree 3 of 12\n",
      "building tree 4 of 12\n",
      "building tree 5 of 12\n",
      "building tree 6 of 12\n",
      "building tree 7 of 12\n",
      "building tree 8 of 12\n",
      "building tree 9 of 12\n",
      "building tree 10 of 12\n",
      "building tree 11 of 12\n",
      "building tree 12 of 12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.0s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(n_estimators=12, n_jobs=1, random_state=0, verbose=13)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import train_test_split\n",
    "x=data.iloc[:,0:1].values\n",
    "y=data.iloc[:,3].values\n",
    "regressor=RandomForestRegressor(n_estimators=12,random_state=0,n_jobs=1,verbose=13)\n",
    "regressor.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "_uuid": "c8b56fb48901de5ed92355836b39291cf16d5cb1"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.0s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([45.726107  , 45.71519001, 48.00600917, 47.33382739, 48.92472398,\n",
       "       49.51196079, 46.71271891, 46.54466636, 47.0961381 ])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=regressor.predict(x)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "_uuid": "b00eed7bea807405aa2b7d41c77f2ec225f7a310"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.0s finished\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_grid=np.arange(min(x),max(x),0.001)\n",
    "x_grid=x_grid.reshape(len(x_grid),1)\n",
    "plt.scatter(x,y,color='r')\n",
    "plt.plot(x_grid,regressor.predict(x_grid),color='b')\n",
    "a=plt.show()\n",
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#  DENSITY PLOTS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "_uuid": "bc03a5966aa8badd9f5d0911886752cfd6a4ab47"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ashutoshverma/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='CPI', ylabel='Density'>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "seaborn.distplot(data['CPI'], hist=True, kde=True, \n",
    "             bins=int(180/5), color = 'darkblue',\n",
    "             kde_kws={'linewidth': 4})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "_uuid": "63796b00eac958a619eea4a214475f14d06797e9"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ashutoshverma/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Area', ylabel='Density'>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "seaborn.distplot(data['Area'], hist=True, kde=True, \n",
    "             bins=int(180/5), color = 'darkblue', \n",
    "             hist_kws={'edgecolor':'black'},\n",
    "             kde_kws={'linewidth': 4})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ashutoshverma/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Production', ylabel='Density'>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "seaborn.distplot(data['Production'], hist=True, kde=True, \n",
    "             bins=int(180/5), color = 'darkblue', \n",
    "             hist_kws={'edgecolor':'black'},\n",
    "             kde_kws={'linewidth': 4})\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ashutoshverma/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Crop_Year', ylabel='Density'>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "seaborn.distplot(data['Crop_Year'], hist=True, kde=True, \n",
    "             bins=int(180/5), color = 'darkblue', \n",
    "             hist_kws={'edgecolor':'black'},\n",
    "             kde_kws={'linewidth': 4})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# regression model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#actual and predicted values\n",
    "dm = pd.DataFrame({'Actual': y, 'Predicted': y_pred}).reset_index()\n",
    "x_axis=dm.index\n",
    "y_axis=dm.Actual\n",
    "y1_axis=dm.Predicted\n",
    "plt.plot(x_axis,y_axis)\n",
    "plt.plot(x_axis,y1_axis)\n",
    "plt.title(\"Actual vs Predicted\")\n",
    "plt.legend([\"actual \",\"predicted\"])\n",
    "b=plt.show()\n",
    "b"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}