Crop Recommendation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing necessary libraries\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'skl2onnx'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_12232/1817013082.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mskl2onnx\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mconvert_sklearn\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mskl2onnx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcommon\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata_types\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mFloatTensorType\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0minitial_type\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'float_input'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mFloatTensorType\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m4\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0monx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mconvert_sklearn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mclf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minitial_types\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0minitial_type\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"crop_recommendation.onnx\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"wb\"\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'skl2onnx'"
     ]
    }
   ],
   "source": [
    "from skl2onnx import convert_sklearn\n",
    "from skl2onnx.common.data_types import FloatTensorType\n",
    "initial_type = [('float_input', FloatTensorType([None, 4]))]\n",
    "onx = convert_sklearn(clf, initial_types = initial_type)\n",
    "with open(\"crop_recommendation.onnx\",\"wb\") as f:\n",
    "    f.write(onx.SerializeToString())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing the dataset\n",
    "crop = pd.read_csv(\"Crop_recommendation.csv\")\n",
    "crop.drop(crop[crop.label == 'muskmelon'].index, inplace = True )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2100 entries, 0 to 2199\n",
      "Data columns (total 8 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   N            2100 non-null   int64  \n",
      " 1   P            2100 non-null   int64  \n",
      " 2   K            2100 non-null   int64  \n",
      " 3   temperature  2100 non-null   float64\n",
      " 4   humidity     2100 non-null   float64\n",
      " 5   ph           2100 non-null   float64\n",
      " 6   rainfall     2100 non-null   float64\n",
      " 7   label        2100 non-null   object \n",
      "dtypes: float64(4), int64(3), object(1)\n",
      "memory usage: 147.7+ KB\n"
     ]
    }
   ],
   "source": [
    "#copying original data\n",
    "data = crop.copy()\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking for null values\n",
    "data.isnull().sum().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "column Name :  N\n",
      "No. of unique values : 137 \n",
      "\n",
      "column Name :  P\n",
      "No. of unique values : 117 \n",
      "\n",
      "column Name :  K\n",
      "No. of unique values : 73 \n",
      "\n",
      "column Name :  TEMPERATURE\n",
      "No. of unique values : 2100 \n",
      "\n",
      "column Name :  HUMIDITY\n",
      "No. of unique values : 2100 \n",
      "\n",
      "column Name :  PH\n",
      "No. of unique values : 2100 \n",
      "\n",
      "column Name :  RAINFALL\n",
      "No. of unique values : 2100 \n",
      "\n",
      "column Name :  LABEL\n",
      "No. of unique values : 21 \n",
      "\n",
      "Unique values :  ['rice' 'maize' 'chickpea' 'kidneybeans' 'pigeonpeas' 'mothbeans'\n",
      " 'mungbean' 'blackgram' 'lentil' 'pomegranate' 'banana' 'mango' 'grapes'\n",
      " 'watermelon' 'apple' 'orange' 'papaya' 'coconut' 'cotton' 'jute' 'coffee']\n"
     ]
    }
   ],
   "source": [
    "#checking for unique values\n",
    "for i in data.columns:\n",
    "    print(\"column Name : \",i.upper())\n",
    "    print(\"No. of unique values : {} \\n\".format(data[i].nunique()))\n",
    "    if(data[i].dtype == 'object'):\n",
    "        print('Unique values : ',pd.unique(data[i]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 22 classes in output variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#label encoding for output variable\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "encod = LabelEncoder()\n",
    "data['Encoded_label'] = encod.fit_transform(data.label) #label will be encoded in alphabetical order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>N</th>\n",
       "      <th>P</th>\n",
       "      <th>K</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "      <th>ph</th>\n",
       "      <th>rainfall</th>\n",
       "      <th>label</th>\n",
       "      <th>Encoded_label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>90</td>\n",
       "      <td>42</td>\n",
       "      <td>43</td>\n",
       "      <td>20.879744</td>\n",
       "      <td>82.002744</td>\n",
       "      <td>6.502985</td>\n",
       "      <td>202.935536</td>\n",
       "      <td>rice</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>85</td>\n",
       "      <td>58</td>\n",
       "      <td>41</td>\n",
       "      <td>21.770462</td>\n",
       "      <td>80.319644</td>\n",
       "      <td>7.038096</td>\n",
       "      <td>226.655537</td>\n",
       "      <td>rice</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60</td>\n",
       "      <td>55</td>\n",
       "      <td>44</td>\n",
       "      <td>23.004459</td>\n",
       "      <td>82.320763</td>\n",
       "      <td>7.840207</td>\n",
       "      <td>263.964248</td>\n",
       "      <td>rice</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>74</td>\n",
       "      <td>35</td>\n",
       "      <td>40</td>\n",
       "      <td>26.491096</td>\n",
       "      <td>80.158363</td>\n",
       "      <td>6.980401</td>\n",
       "      <td>242.864034</td>\n",
       "      <td>rice</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>78</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>20.130175</td>\n",
       "      <td>81.604873</td>\n",
       "      <td>7.628473</td>\n",
       "      <td>262.717340</td>\n",
       "      <td>rice</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    N   P   K  temperature   humidity        ph    rainfall label  \\\n",
       "0  90  42  43    20.879744  82.002744  6.502985  202.935536  rice   \n",
       "1  85  58  41    21.770462  80.319644  7.038096  226.655537  rice   \n",
       "2  60  55  44    23.004459  82.320763  7.840207  263.964248  rice   \n",
       "3  74  35  40    26.491096  80.158363  6.980401  242.864034  rice   \n",
       "4  78  42  42    20.130175  81.604873  7.628473  262.717340  rice   \n",
       "\n",
       "   Encoded_label  \n",
       "0             19  \n",
       "1             19  \n",
       "2             19  \n",
       "3             19  \n",
       "4             19  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>encoded</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>label</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>apple</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>banana</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>blackgram</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chickpea</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>coconut</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>coffee</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cotton</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grapes</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jute</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kidneybeans</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lentil</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>maize</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mango</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mothbeans</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mungbean</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>orange</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>papaya</th>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pigeonpeas</th>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pomegranate</th>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rice</th>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>watermelon</th>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             encoded\n",
       "label               \n",
       "apple              0\n",
       "banana             1\n",
       "blackgram          2\n",
       "chickpea           3\n",
       "coconut            4\n",
       "coffee             5\n",
       "cotton             6\n",
       "grapes             7\n",
       "jute               8\n",
       "kidneybeans        9\n",
       "lentil            10\n",
       "maize             11\n",
       "mango             12\n",
       "mothbeans         13\n",
       "mungbean          14\n",
       "orange            15\n",
       "papaya            16\n",
       "pigeonpeas        17\n",
       "pomegranate       18\n",
       "rice              19\n",
       "watermelon        20"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#encoded labels for classes \n",
    "a = pd.DataFrame(pd.unique(data.label));\n",
    "a.rename(columns={0:'label'},inplace=True)\n",
    "b = pd.DataFrame(pd.unique(data.Encoded_label));\n",
    "b.rename(columns={0:'encoded'},inplace=True)\n",
    "classes = pd.concat([a,b],axis=1).sort_values('encoded').set_index('label')\n",
    "classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MANGO\n"
     ]
    }
   ],
   "source": [
    "#fetching the label for given encoded value\n",
    "a=12\n",
    "for i in range(0,len(classes)):\n",
    "    if(classes.encoded[i]==a):\n",
    "        print(classes.index[i].upper())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dropping duplicate values\n",
    "data = data.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#checking the corelation\n",
    "plt.figure(figsize=(10,6))\n",
    "sns.heatmap(data.corr(),annot=True,cmap='RdBu')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above diagram, the variable 'K' and 'P' have high positive corelation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>N</th>\n",
       "      <th>P</th>\n",
       "      <th>K</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "      <th>ph</th>\n",
       "      <th>rainfall</th>\n",
       "      <th>Encoded_label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2100.000000</td>\n",
       "      <td>2100.000000</td>\n",
       "      <td>2100.000000</td>\n",
       "      <td>2100.000000</td>\n",
       "      <td>2100.000000</td>\n",
       "      <td>2100.000000</td>\n",
       "      <td>2100.000000</td>\n",
       "      <td>2100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>48.181905</td>\n",
       "      <td>55.060000</td>\n",
       "      <td>48.057143</td>\n",
       "      <td>25.471157</td>\n",
       "      <td>70.488397</td>\n",
       "      <td>6.474750</td>\n",
       "      <td>107.214784</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>36.016740</td>\n",
       "      <td>32.772902</td>\n",
       "      <td>51.833864</td>\n",
       "      <td>5.134671</td>\n",
       "      <td>22.303929</td>\n",
       "      <td>0.790175</td>\n",
       "      <td>53.425191</td>\n",
       "      <td>6.056743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>8.825675</td>\n",
       "      <td>14.258040</td>\n",
       "      <td>3.504752</td>\n",
       "      <td>30.920140</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>20.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>22.602437</td>\n",
       "      <td>59.237124</td>\n",
       "      <td>5.948298</td>\n",
       "      <td>67.133894</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>36.000000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>25.373357</td>\n",
       "      <td>79.995776</td>\n",
       "      <td>6.431478</td>\n",
       "      <td>97.659622</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>80.250000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>28.282950</td>\n",
       "      <td>88.018773</td>\n",
       "      <td>6.945725</td>\n",
       "      <td>130.140535</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>140.000000</td>\n",
       "      <td>145.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>43.675493</td>\n",
       "      <td>99.981876</td>\n",
       "      <td>9.935091</td>\n",
       "      <td>298.560117</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 N            P            K  temperature     humidity  \\\n",
       "count  2100.000000  2100.000000  2100.000000  2100.000000  2100.000000   \n",
       "mean     48.181905    55.060000    48.057143    25.471157    70.488397   \n",
       "std      36.016740    32.772902    51.833864     5.134671    22.303929   \n",
       "min       0.000000     5.000000     5.000000     8.825675    14.258040   \n",
       "25%      20.000000    31.000000    20.000000    22.602437    59.237124   \n",
       "50%      36.000000    53.000000    30.000000    25.373357    79.995776   \n",
       "75%      80.250000    69.000000    47.000000    28.282950    88.018773   \n",
       "max     140.000000   145.000000   205.000000    43.675493    99.981876   \n",
       "\n",
       "                ph     rainfall  Encoded_label  \n",
       "count  2100.000000  2100.000000    2100.000000  \n",
       "mean      6.474750   107.214784      10.000000  \n",
       "std       0.790175    53.425191       6.056743  \n",
       "min       3.504752    30.920140       0.000000  \n",
       "25%       5.948298    67.133894       5.000000  \n",
       "50%       6.431478    97.659622      10.000000  \n",
       "75%       6.945725   130.140535      15.000000  \n",
       "max       9.935091   298.560117      20.000000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#EDA\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable Name : N\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zainr\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Variable Name : P\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zainr\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Variable Name : K\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zainr\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Variable Name : TEMPERATURE\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zainr\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Variable Name : HUMIDITY\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zainr\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Variable Name : PH\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zainr\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Variable Name : RAINFALL\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zainr\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#checking for outliers in the data\n",
    "for i in data.columns[:-2]:\n",
    "    print('Variable Name :',i.upper())\n",
    "    fig, axes = plt.subplots(1,2,figsize=(8,4))\n",
    "    axes[0].set_title('Distribution')\n",
    "    axes[1].set_title('Outliers Detection')\n",
    "    data[i].hist(ax=axes[0])\n",
    "    sns.boxplot(data[i],ax=axes[1])\n",
    "    plt.show()\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable : N\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable : P\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable : K\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable : TEMPERATURE\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable : HUMIDITY\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable : PH\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable : RAINFALL\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting effect of input variable with output variable\n",
    "for i in data.columns[:-2]:\n",
    "    plt.figure(figsize=(15,5))\n",
    "    print('Variable :',i.upper())\n",
    "    sns.boxplot(x=data.label,y=data[i])\n",
    "    plt.grid()\n",
    "    plt.xticks(rotation=90)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#which crops can grow at higher temperature .i.e., temperature > 30\n",
    "x = pd.DataFrame(pd.crosstab(data.label[data.temperature > 30],'count',normalize=True)*100)\n",
    "x.plot.pie(y = 'count',autopct='%1.1f%%',figsize=(8,8),legend=None,shadow=True, startangle=90)\n",
    "plt.title('Probability of crops grow when temperature > 30')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#which crops can grow at higher rainfall .i.e., rainfall > 150mm\n",
    "x1 = pd.DataFrame(pd.crosstab(data.label[data.rainfall > 150],'count',normalize=True)*100)\n",
    "x1.plot.pie(y = 'count',autopct='%1.1f%%',figsize=(8,8),legend=None,shadow=True, startangle=90)\n",
    "plt.title('Probability of crops grow when rainfall > 150mm')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#which crops can grow at higher ph value .i.e., (alkaline nature) ph > 7.5\n",
    "x = pd.DataFrame(pd.crosstab(data.label[data.ph > 7.5],'count',normalize=True)*100)\n",
    "x.plot.pie(y = 'count',autopct='%1.1f%%',figsize=(8,8),legend=None,shadow=True, startangle=90)\n",
    "plt.title('Probability of crops grow when ph > 7.5 i.e.,alkaline nature')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input variables \n",
      "     N   P   K  temperature   humidity        ph    rainfall\n",
      "0  90  42  43    20.879744  82.002744  6.502985  202.935536\n",
      "1  85  58  41    21.770462  80.319644  7.038096  226.655537\n",
      "2  60  55  44    23.004459  82.320763  7.840207  263.964248\n",
      "3  74  35  40    26.491096  80.158363  6.980401  242.864034\n",
      "4  78  42  42    20.130175  81.604873  7.628473  262.717340\n",
      "\n",
      "Output Variable\n",
      " 0    19\n",
      "1    19\n",
      "2    19\n",
      "3    19\n",
      "4    19\n",
      "Name: Encoded_label, dtype: int32\n"
     ]
    }
   ],
   "source": [
    "#Splitting the data into input and output\n",
    "x = data.iloc[:,:-2]\n",
    "y = data.Encoded_label\n",
    "print('Input variables \\n',x.head())\n",
    "print('\\nOutput Variable\\n',y.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of Splitting :\n",
      "x_train = (1680, 7), x_test = (420, 7), y_train = (1680,), y_test = (420,)\n"
     ]
    }
   ],
   "source": [
    "#Splitting the data into train and test\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(x,y,test_size=0.2,random_state=2)\n",
    "print('Shape of Splitting :')\n",
    "print('x_train = {}, x_test = {}, y_train = {}, y_test = {}'.format(x_train.shape,x_test.shape,y_train.shape,y_test.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building Logistic regression model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing necessary libraries\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import classification_report, accuracy_score, plot_confusion_matrix\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zainr\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix : \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Initializing the model\n",
    "logis = LogisticRegression()\n",
    "pred_logis = logis.fit(x_train,y_train).predict(x_test)\n",
    "print('Confusion Matrix : \\n')\n",
    "fig, ax = plt.subplots(figsize=(15,15))\n",
    "plot_confusion_matrix(logis,x_test,y_test,ax=ax,cmap=plt.cm.Blues)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "REPORT : \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        24\n",
      "           1       1.00      1.00      1.00        20\n",
      "           2       0.86      0.82      0.84        22\n",
      "           3       1.00      1.00      1.00        21\n",
      "           4       1.00      1.00      1.00        19\n",
      "           5       0.95      1.00      0.98        21\n",
      "           6       0.95      0.95      0.95        22\n",
      "           7       1.00      1.00      1.00        13\n",
      "           8       0.79      0.88      0.83        17\n",
      "           9       1.00      1.00      1.00        20\n",
      "          10       0.71      0.92      0.80        13\n",
      "          11       0.94      0.94      0.94        18\n",
      "          12       1.00      1.00      1.00        18\n",
      "          13       0.85      0.81      0.83        21\n",
      "          14       1.00      0.87      0.93        23\n",
      "          15       1.00      1.00      1.00        21\n",
      "          16       0.96      1.00      0.98        27\n",
      "          17       1.00      1.00      1.00        21\n",
      "          18       1.00      1.00      1.00        18\n",
      "          19       0.94      0.79      0.86        19\n",
      "          20       1.00      1.00      1.00        22\n",
      "\n",
      "    accuracy                           0.95       420\n",
      "   macro avg       0.95      0.95      0.95       420\n",
      "weighted avg       0.96      0.95      0.95       420\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#classification report\n",
    "print('REPORT : \\n',classification_report(y_test,pred_logis))\n",
    "acc_logis = accuracy_score(y_test,pred_logis)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building RandomForest model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing necessary libraries\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9904761904761905"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#initializing the model and fitting for train data\n",
    "rand = RandomForestClassifier()\n",
    "pred_rand = rand.fit(x_train,y_train).predict(x_test)\n",
    "acc_rand = accuracy_score(y_test,pred_rand)\n",
    "acc_rand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['bootstrap', 'ccp_alpha', 'class_weight', 'criterion', 'max_depth', 'max_features', 'max_leaf_nodes', 'max_samples', 'min_impurity_decrease', 'min_impurity_split', 'min_samples_leaf', 'min_samples_split', 'min_weight_fraction_leaf', 'n_estimators', 'n_jobs', 'oob_score', 'random_state', 'verbose', 'warm_start'])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rand.get_params().keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 2400 candidates, totalling 12000 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zainr\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:922: UserWarning: One or more of the test scores are non-finite: [       nan        nan        nan ... 0.98392857 0.98809524 0.99166667]\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=RandomForestClassifier(), n_jobs=-1,\n",
       "             param_grid={'criterion': ['gini'], 'max_depth': range(1, 20, 2),\n",
       "                         'max_features': [1, 2, 3],\n",
       "                         'min_samples_leaf': range(1, 10, 2),\n",
       "                         'min_samples_split': range(1, 40, 10),\n",
       "                         'n_estimators': range(10, 50, 10)},\n",
       "             verbose=1)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#selecting parameters using GridSearchCV\n",
    "param = {'n_estimators':range(10,50,10),\n",
    "         'criterion':['gini'],\n",
    "         'max_depth':range(1,20,2),\n",
    "         'max_features':[1,2,3],\n",
    "         'min_samples_leaf':range(1,10,2),\n",
    "         'min_samples_split':range(1,40,10)}\n",
    "grid_rand = GridSearchCV(rand,param,cv=5,n_jobs=-1,verbose=1)\n",
    "grid_rand.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'criterion': 'gini', 'max_depth': 11, 'max_features': 2, 'min_samples_leaf': 1, 'min_samples_split': 21, 'n_estimators': 40}\n",
      "0.9970238095238095\n"
     ]
    }
   ],
   "source": [
    "print(grid_rand.best_params_)\n",
    "print(grid_rand.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix : \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#predicting for test data and plotting confusion matrix\n",
    "pred_rand = grid_rand.predict(x_test)\n",
    "print('Confusion Matrix : \\n')\n",
    "fig, ax = plt.subplots(figsize=(15,15))\n",
    "plot_confusion_matrix(grid_rand,x_test,y_test,ax=ax,cmap=plt.cm.Blues)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "REPORT : \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        24\n",
      "           1       1.00      1.00      1.00        20\n",
      "           2       1.00      1.00      1.00        22\n",
      "           3       1.00      1.00      1.00        21\n",
      "           4       1.00      1.00      1.00        19\n",
      "           5       1.00      1.00      1.00        21\n",
      "           6       1.00      1.00      1.00        22\n",
      "           7       1.00      1.00      1.00        13\n",
      "           8       0.81      1.00      0.89        17\n",
      "           9       1.00      1.00      1.00        20\n",
      "          10       1.00      1.00      1.00        13\n",
      "          11       1.00      1.00      1.00        18\n",
      "          12       1.00      1.00      1.00        18\n",
      "          13       1.00      1.00      1.00        21\n",
      "          14       1.00      1.00      1.00        23\n",
      "          15       1.00      1.00      1.00        21\n",
      "          16       1.00      1.00      1.00        27\n",
      "          17       1.00      1.00      1.00        21\n",
      "          18       1.00      1.00      1.00        18\n",
      "          19       1.00      0.79      0.88        19\n",
      "          20       1.00      1.00      1.00        22\n",
      "\n",
      "    accuracy                           0.99       420\n",
      "   macro avg       0.99      0.99      0.99       420\n",
      "weighted avg       0.99      0.99      0.99       420\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#classification report\n",
    "print('REPORT : \\n',classification_report(y_test,pred_rand))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing pickle file\n",
    "import pickle\n",
    "pickle_out = open('classifier.pkl','wb')\n",
    "pickle.dump(grid_rand,pickle_out)\n",
    "pickle_out.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = [[80,35,40,30,10000,-1,0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>predicted_values</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>label</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>apple</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>banana</th>\n",
       "      <td>7.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>blackgram</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chickpea</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>coconut</th>\n",
       "      <td>7.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>coffee</th>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cotton</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grapes</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jute</th>\n",
       "      <td>4.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kidneybeans</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lentil</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>maize</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mango</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mothbeans</th>\n",
       "      <td>2.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mungbean</th>\n",
       "      <td>11.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>orange</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>papaya</th>\n",
       "      <td>25.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pigeonpeas</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pomegranate</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rice</th>\n",
       "      <td>12.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>watermelon</th>\n",
       "      <td>23.03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             predicted_values\n",
       "label                        \n",
       "apple                    0.00\n",
       "banana                   7.75\n",
       "blackgram                0.00\n",
       "chickpea                 0.00\n",
       "coconut                  7.50\n",
       "coffee                   5.00\n",
       "cotton                   0.00\n",
       "grapes                   0.00\n",
       "jute                     4.28\n",
       "kidneybeans              0.00\n",
       "lentil                   0.00\n",
       "maize                    0.00\n",
       "mango                    0.00\n",
       "mothbeans                2.50\n",
       "mungbean                11.78\n",
       "orange                   0.00\n",
       "papaya                  25.39\n",
       "pigeonpeas               0.00\n",
       "pomegranate              0.00\n",
       "rice                    12.78\n",
       "watermelon              23.03"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pickle_in = open('classifier.pkl','rb')\n",
    "model = pickle.load(pickle_in)\n",
    "pre = model.predict_proba(a)\n",
    "pre = pd.DataFrame(data = np.round(pre.T*100,2), index=classes.index,columns=['predicted_values'])\n",
    "pre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "high = pre.predicted_values.nlargest(5)\n",
    "plt.figure(figsize=(15,10))\n",
    "plt.rcParams['font.size']=15\n",
    "plt.title('Crops Recommendations :',fontdict={'fontsize': 25, 'fontweight': 'medium'})\n",
    "plt.pie(x=high,labels=high.index,autopct='%1.1f%%',explode=(0.1, 0, 0, 0, 0),shadow=True,startangle=90,\n",
    "        colors=['green','red','cyan','brown','orange'])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'papaya'"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "highest = pre.predicted_values.nlargest(1)\n",
    "\n",
    "for ind, val in highest.iteritems():\n",
    "    new_h = ind\n",
    "new_h\n",
    "# print(tmp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>Season</th>\n",
       "      <th>Crop</th>\n",
       "      <th>Area</th>\n",
       "      <th>Production</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2000</td>\n",
       "      <td>Kharif</td>\n",
       "      <td>rice</td>\n",
       "      <td>102.0</td>\n",
       "      <td>321.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2000</td>\n",
       "      <td>Whole Year</td>\n",
       "      <td>banana</td>\n",
       "      <td>176.0</td>\n",
       "      <td>641.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2001</td>\n",
       "      <td>Kharif</td>\n",
       "      <td>rice</td>\n",
       "      <td>83.0</td>\n",
       "      <td>300.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\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>20</th>\n",
       "      <td>2002</td>\n",
       "      <td>Whole Year</td>\n",
       "      <td>banana</td>\n",
       "      <td>213.0</td>\n",
       "      <td>1278.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246072</th>\n",
       "      <td>2014</td>\n",
       "      <td>Rabi</td>\n",
       "      <td>chickpea</td>\n",
       "      <td>198.0</td>\n",
       "      <td>203.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246078</th>\n",
       "      <td>2014</td>\n",
       "      <td>Rabi</td>\n",
       "      <td>mungbean</td>\n",
       "      <td>64.0</td>\n",
       "      <td>40.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246085</th>\n",
       "      <td>2014</td>\n",
       "      <td>Summer</td>\n",
       "      <td>maize</td>\n",
       "      <td>325.0</td>\n",
       "      <td>2039.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246086</th>\n",
       "      <td>2014</td>\n",
       "      <td>Summer</td>\n",
       "      <td>rice</td>\n",
       "      <td>306.0</td>\n",
       "      <td>801.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246089</th>\n",
       "      <td>2014</td>\n",
       "      <td>Winter</td>\n",
       "      <td>rice</td>\n",
       "      <td>279151.0</td>\n",
       "      <td>597899.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>58461 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Crop_Year       Season      Crop      Area  Production\n",
       "2            2000  Kharif           rice     102.0      321.00\n",
       "3            2000  Whole Year     banana     176.0      641.00\n",
       "12           2001  Kharif           rice      83.0      300.00\n",
       "18           2002  Kharif           rice     189.2      510.84\n",
       "20           2002  Whole Year     banana     213.0     1278.00\n",
       "...           ...          ...       ...       ...         ...\n",
       "246072       2014  Rabi         chickpea     198.0      203.00\n",
       "246078       2014  Rabi         mungbean      64.0       40.00\n",
       "246085       2014  Summer          maize     325.0     2039.00\n",
       "246086       2014  Summer           rice     306.0      801.00\n",
       "246089       2014  Winter           rice  279151.0   597899.00\n",
       "\n",
       "[58461 rows x 5 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# loading the dataset\n",
    "\n",
    "crop_data=pd.read_csv(\"crop_production.csv\")\n",
    "crop_data['Crop'] = crop_data['Crop'].str.lower()\n",
    "#lst = list(crop_data['Crop'].str.lower().unique())\n",
    "#lst.sort()\n",
    "#print(lst)\n",
    "\n",
    "crop_data['Crop'] = crop_data['Crop'].replace(['moth','peas  (vegetable)','bean','moong(green gram)','pome granet','water melon','cotton(lint)','gram'],['mothbeans','pigeonpeas','kidneybeans','mungbean','pomegranate','watermelon','cotton','chickpea'])\n",
    "\n",
    "crop_data = crop_data[crop_data['Crop'].isin(['rice', 'maize', 'chickpea', 'kidneybeans', 'pigeonpeas','mothbeans', 'mungbean', 'blackgram', 'lentil', 'pomegranate','banana', 'mango', 'grapes', 'watermelon', 'apple','orange', 'papaya', 'coconut', 'cotton', 'jute', 'coffee'])]\n",
    "\n",
    "crop_data = crop_data.drop(['State_Name','District_Name'],axis = 1)\n",
    "\n",
    "crop_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['rice', 'banana', 'maize', 'mungbean', 'cotton', 'chickpea',\n",
       "       'grapes', 'mango', 'orange', 'papaya', 'pomegranate', 'pigeonpeas',\n",
       "       'jute', 'blackgram', 'mothbeans', 'coffee', 'watermelon', 'lentil',\n",
       "       'kidneybeans', 'apple'], dtype=object)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crop_data['Crop'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Crop_Year', 'Season', 'Crop', 'Area', 'Production'], dtype='object')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dataset columns\n",
    "crop_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>58461.000000</td>\n",
       "      <td>58461.000000</td>\n",
       "      <td>5.756500e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2005.755119</td>\n",
       "      <td>21056.352565</td>\n",
       "      <td>4.596474e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.018032</td>\n",
       "      <td>48398.529629</td>\n",
       "      <td>1.275836e+05</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>154.000000</td>\n",
       "      <td>1.890000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2006.000000</td>\n",
       "      <td>1800.000000</td>\n",
       "      <td>2.958000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2010.000000</td>\n",
       "      <td>15370.000000</td>\n",
       "      <td>2.670100e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2015.000000</td>\n",
       "      <td>687000.000000</td>\n",
       "      <td>2.589591e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Crop_Year           Area    Production\n",
       "count  58461.000000   58461.000000  5.756500e+04\n",
       "mean    2005.755119   21056.352565  4.596474e+04\n",
       "std        5.018032   48398.529629  1.275836e+05\n",
       "min     1997.000000       1.000000  0.000000e+00\n",
       "25%     2002.000000     154.000000  1.890000e+02\n",
       "50%     2006.000000    1800.000000  2.958000e+03\n",
       "75%     2010.000000   15370.000000  2.670100e+04\n",
       "max     2015.000000  687000.000000  2.589591e+06"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crop_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Crop_Year       0\n",
       "Season          0\n",
       "Crop            0\n",
       "Area            0\n",
       "Production    896\n",
       "dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking missing values of the dataset in each column\n",
    "crop_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>Season</th>\n",
       "      <th>Crop</th>\n",
       "      <th>Area</th>\n",
       "      <th>Production</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2000</td>\n",
       "      <td>Kharif</td>\n",
       "      <td>rice</td>\n",
       "      <td>102.0</td>\n",
       "      <td>321.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2000</td>\n",
       "      <td>Whole Year</td>\n",
       "      <td>banana</td>\n",
       "      <td>176.0</td>\n",
       "      <td>641.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2001</td>\n",
       "      <td>Kharif</td>\n",
       "      <td>rice</td>\n",
       "      <td>83.0</td>\n",
       "      <td>300.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\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>20</th>\n",
       "      <td>2002</td>\n",
       "      <td>Whole Year</td>\n",
       "      <td>banana</td>\n",
       "      <td>213.0</td>\n",
       "      <td>1278.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246072</th>\n",
       "      <td>2014</td>\n",
       "      <td>Rabi</td>\n",
       "      <td>chickpea</td>\n",
       "      <td>198.0</td>\n",
       "      <td>203.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246078</th>\n",
       "      <td>2014</td>\n",
       "      <td>Rabi</td>\n",
       "      <td>mungbean</td>\n",
       "      <td>64.0</td>\n",
       "      <td>40.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246085</th>\n",
       "      <td>2014</td>\n",
       "      <td>Summer</td>\n",
       "      <td>maize</td>\n",
       "      <td>325.0</td>\n",
       "      <td>2039.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246086</th>\n",
       "      <td>2014</td>\n",
       "      <td>Summer</td>\n",
       "      <td>rice</td>\n",
       "      <td>306.0</td>\n",
       "      <td>801.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246089</th>\n",
       "      <td>2014</td>\n",
       "      <td>Winter</td>\n",
       "      <td>rice</td>\n",
       "      <td>279151.0</td>\n",
       "      <td>597899.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>57565 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Crop_Year       Season      Crop      Area  Production\n",
       "2            2000  Kharif           rice     102.0      321.00\n",
       "3            2000  Whole Year     banana     176.0      641.00\n",
       "12           2001  Kharif           rice      83.0      300.00\n",
       "18           2002  Kharif           rice     189.2      510.84\n",
       "20           2002  Whole Year     banana     213.0     1278.00\n",
       "...           ...          ...       ...       ...         ...\n",
       "246072       2014  Rabi         chickpea     198.0      203.00\n",
       "246078       2014  Rabi         mungbean      64.0       40.00\n",
       "246085       2014  Summer          maize     325.0     2039.00\n",
       "246086       2014  Summer           rice     306.0      801.00\n",
       "246089       2014  Winter           rice  279151.0   597899.00\n",
       "\n",
       "[57565 rows x 5 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dropping missing values \n",
    "crop_data = crop_data.dropna()\n",
    "crop_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking\n",
    "crop_data.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x210690b4e20>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x540 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing the features\n",
    "\n",
    "ax = sns.pairplot(crop_data)\n",
    "ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = crop_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>Crop_Year</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.003892</td>\n",
       "      <td>0.030581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Area</th>\n",
       "      <td>-0.003892</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.770546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Production</th>\n",
       "      <td>0.030581</td>\n",
       "      <td>0.770546</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Crop_Year      Area  Production\n",
       "Crop_Year    1.000000 -0.003892    0.030581\n",
       "Area        -0.003892  1.000000    0.770546\n",
       "Production   0.030581  0.770546    1.000000"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Correlation Matrix')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(data.corr(), annot =True)\n",
    "plt.title('Correlation Matrix')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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",
       "      <th>Season_Autumn</th>\n",
       "      <th>Season_Kharif</th>\n",
       "      <th>Season_Rabi</th>\n",
       "      <th>Season_Summer</th>\n",
       "      <th>Season_Whole Year</th>\n",
       "      <th>Season_Winter</th>\n",
       "      <th>Crop_apple</th>\n",
       "      <th>...</th>\n",
       "      <th>Crop_maize</th>\n",
       "      <th>Crop_mango</th>\n",
       "      <th>Crop_mothbeans</th>\n",
       "      <th>Crop_mungbean</th>\n",
       "      <th>Crop_orange</th>\n",
       "      <th>Crop_papaya</th>\n",
       "      <th>Crop_pigeonpeas</th>\n",
       "      <th>Crop_pomegranate</th>\n",
       "      <th>Crop_rice</th>\n",
       "      <th>Crop_watermelon</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2000</td>\n",
       "      <td>102.0</td>\n",
       "      <td>321.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2000</td>\n",
       "      <td>176.0</td>\n",
       "      <td>641.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2001</td>\n",
       "      <td>83.0</td>\n",
       "      <td>300.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2002</td>\n",
       "      <td>189.2</td>\n",
       "      <td>510.84</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2002</td>\n",
       "      <td>213.0</td>\n",
       "      <td>1278.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\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>246072</th>\n",
       "      <td>2014</td>\n",
       "      <td>198.0</td>\n",
       "      <td>203.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246078</th>\n",
       "      <td>2014</td>\n",
       "      <td>64.0</td>\n",
       "      <td>40.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246085</th>\n",
       "      <td>2014</td>\n",
       "      <td>325.0</td>\n",
       "      <td>2039.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246086</th>\n",
       "      <td>2014</td>\n",
       "      <td>306.0</td>\n",
       "      <td>801.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246089</th>\n",
       "      <td>2014</td>\n",
       "      <td>279151.0</td>\n",
       "      <td>597899.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>57565 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Crop_Year      Area  Production  Season_Autumn       \\\n",
       "2            2000     102.0      321.00                   0   \n",
       "3            2000     176.0      641.00                   0   \n",
       "12           2001      83.0      300.00                   0   \n",
       "18           2002     189.2      510.84                   0   \n",
       "20           2002     213.0     1278.00                   0   \n",
       "...           ...       ...         ...                 ...   \n",
       "246072       2014     198.0      203.00                   0   \n",
       "246078       2014      64.0       40.00                   0   \n",
       "246085       2014     325.0     2039.00                   0   \n",
       "246086       2014     306.0      801.00                   0   \n",
       "246089       2014  279151.0   597899.00                   0   \n",
       "\n",
       "        Season_Kharif       Season_Rabi         Season_Summer       \\\n",
       "2                        1                   0                   0   \n",
       "3                        0                   0                   0   \n",
       "12                       1                   0                   0   \n",
       "18                       1                   0                   0   \n",
       "20                       0                   0                   0   \n",
       "...                    ...                 ...                 ...   \n",
       "246072                   0                   1                   0   \n",
       "246078                   0                   1                   0   \n",
       "246085                   0                   0                   1   \n",
       "246086                   0                   0                   1   \n",
       "246089                   0                   0                   0   \n",
       "\n",
       "        Season_Whole Year   Season_Winter       Crop_apple  ...  Crop_maize  \\\n",
       "2                        0                   0           0  ...           0   \n",
       "3                        1                   0           0  ...           0   \n",
       "12                       0                   0           0  ...           0   \n",
       "18                       0                   0           0  ...           0   \n",
       "20                       1                   0           0  ...           0   \n",
       "...                    ...                 ...         ...  ...         ...   \n",
       "246072                   0                   0           0  ...           0   \n",
       "246078                   0                   0           0  ...           0   \n",
       "246085                   0                   0           0  ...           1   \n",
       "246086                   0                   0           0  ...           0   \n",
       "246089                   0                   1           0  ...           0   \n",
       "\n",
       "        Crop_mango  Crop_mothbeans  Crop_mungbean  Crop_orange  Crop_papaya  \\\n",
       "2                0               0              0            0            0   \n",
       "3                0               0              0            0            0   \n",
       "12               0               0              0            0            0   \n",
       "18               0               0              0            0            0   \n",
       "20               0               0              0            0            0   \n",
       "...            ...             ...            ...          ...          ...   \n",
       "246072           0               0              0            0            0   \n",
       "246078           0               0              1            0            0   \n",
       "246085           0               0              0            0            0   \n",
       "246086           0               0              0            0            0   \n",
       "246089           0               0              0            0            0   \n",
       "\n",
       "        Crop_pigeonpeas  Crop_pomegranate  Crop_rice  Crop_watermelon  \n",
       "2                     0                 0          1                0  \n",
       "3                     0                 0          0                0  \n",
       "12                    0                 0          1                0  \n",
       "18                    0                 0          1                0  \n",
       "20                    0                 0          0                0  \n",
       "...                 ...               ...        ...              ...  \n",
       "246072                0                 0          0                0  \n",
       "246078                0                 0          0                0  \n",
       "246085                0                 0          0                0  \n",
       "246086                0                 0          1                0  \n",
       "246089                0                 0          1                0  \n",
       "\n",
       "[57565 rows x 29 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummy = pd.get_dummies(data)\n",
    "dummy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train : (43173, 28)\n",
      "x_test : (14392, 28)\n",
      "y_train : (43173,)\n",
      "y_test : (14392,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x = dummy.drop([\"Production\"], axis=1)\n",
    "y = dummy[\"Production\"]\n",
    "\n",
    "# Splitting data set - 25% test dataset and 75% train\n",
    "\n",
    "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.25, random_state=5)\n",
    "\n",
    "print(\"x_train :\",x_train.shape)\n",
    "print(\"x_test :\",x_test.shape)\n",
    "print(\"y_train :\",y_train.shape)\n",
    "print(\"y_test :\",y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5.21181818e+02, 1.35172727e+03, 4.03870000e+04, ...,\n",
       "       2.16363636e+01, 2.77829091e+03, 4.88849818e+05])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "model = RandomForestRegressor(n_estimators = 11)\n",
    "model.fit(x_train,y_train)\n",
    "rf_predict = model.predict(x_test)\n",
    "rf_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.838158949580062"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score :  0.838158949580062\n"
     ]
    }
   ],
   "source": [
    "\n",
    "from sklearn.metrics import r2_score\n",
    "r1 = r2_score(y_test,rf_predict)\n",
    "print(\"R2 score : \",r1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adj. R-Squared : 0.8378434479848689\n"
     ]
    }
   ],
   "source": [
    "# Calculating Adj. R2 score: \n",
    "\n",
    "Adjr2_1 = 1 - (1-r1)*(len(y_test)-1)/(len(y_test)-x_test.shape[1]-1)\n",
    "print(\"Adj. R-Squared : {}\".format(Adjr2_1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zainr\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: 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 `kdeplot` (an axes-level function for kernel density plots).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\zainr\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: 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 `kdeplot` (an axes-level function for kernel density plots).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Random Forest Regression')"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BjgF+nFi1FDhC0uxqHHdc+TxdFg6b3ThQ88M751y9mm4dDq6IrhOtkXSppLbYun0IzXgrEts8TjjP/WpVyC36+uiiE/Dg45xzcdPlJtMB4FuEprPNwCLgk4SAc3KUpzN63JjYtiuxfhuSzgbOBthjjz0qVd4gm90SfPq785Xdt3POTWPTIviY2RrgnFjSMkkvA5dJep2Z/X4S+74cuBxg4cKFle0P3dfHxug6T3azBx/nnBsx3Zrd4q6NHg+LHkdqOMlrO52J9bUTb3brHqz54Z1zrl5N5+BjicengTywfyLf/sAw8GSNyrVVrNkt21uVfhbOOTctTefgc2r0+CCAmQ0AdwLvTORbDNxrZptqWDYArLfPg49zzhVQ82s+ktoJN38C7AbMkjQSSG4ysz5JK4G7zOyMaJslQAfhBtPNwFHAx4GfmNmjsd1fRLge9DXg+ug4JwEnVvOcxtKzfoCh6CXuz3rwcc65EVPR4WBH4JpE2sjzvYBVhHI1xdavIIxucCbQBjwPfAX4QnwnZnZPFMj+BfhH4FngtCkb3WD90Ja/s1lNRRGcc64u1Tz4mNkqYNxvYjNbkHi+lHCzaDH7v55Q65lyXetiwaffg49zzo2Yztd86l7Xhq09t7MD/lI759wI/0asoq6oc/eMVB/ZXNP4mZ1zroF48Kmirk2hqW2Xto1kB6fF/bzOOVcTHnyqaOPm8PLu2tFNvwcf55zbwoNPFXV1pxHD7NSRJTuUmeriOOdc3fDgU0VdPWlmazPt7UZ2uAWG/V4f55wDDz5V1dXXQmdqM23tIksb9PZOdZGcc64uePCpoq5sC53N3bTNaArBZ/PmqS6Sc87VBQ8+VbSxv5U56V7aZjbRT6sHH+eci3jwqaLNuTZmZ7K0dTQxSJrBru6pLpJzztUFDz5VlBtKkUkbre3hZfY5fZxzLvDgU0X54SbSaaPNg49zzm3Dg08V5YabyaShrT2MdJDtGZpgC+ecawwefKooP9xEOgNtM6Kaj08o55xzgAefqspbM+lMiraZYVDR/j6v+TjnHHjwqaqcpUlnROuMEHyyvTbBFs451xg8+FTL8DB50mRatKXmk+3z4OOcc+DBp3r6+8mTJt3aRFtHGNHag49zzgUefKpkqCeLkSLdkvLg45xzCR58qiS3KQtApm1rh4Ns/1SWyDnn6kfNg4+kfSV9R9KjkoYkLStim8MlXSFppaQ+SU9I+pyk1kS+JZKswHJi1U5oDPnNIfikW5tp7UgD0O/BxznnAJiK6TUPBE4C7gPSRW6zGNgH+DLwFHAwcFH0+I5E3k1AMtg8Xm5hy5XvzQGEZrdZ4TSzWdW6GM45V5emIvjcaGY3AEi6FphfxDb/ambrYs+XSeoHviNpTzN7LrZu0Mzuq2B5y5LPhqF00i2prTeZDnjwcc45mIJmNzMr+Tb/ROAZ8XD0uOvkSlQduWy4oTTTIlqjxsFsv19ic845mN4dDo4AhoGnE+lzJK2TlJf0sKRTpqBs5PtD8Em3pEiloIV+srnp/HI751zlTMtvQ0k7A+cBPzCzV2KrVgKfAN5JuBb0InDdeAFI0tmSlktavnbt2oqVcWvwCU1trRqgf2BavtzOOVdxU3HNZ1IkZYAfAz3AR+PrzOy/EnlvBH4LXAD8pND+zOxy4HKAhQsXVuxGnK3NbiHgtKmfbK6pUrt3zrlpbVr9FJck4GqiHnNm1jVefjMzQtA5WFJNv/nzA+HSVro1HLYtNUA278HHOedg+tV8vgacDPylma0ochuLlprK92/t7QbQlsqRzRfbs9w557Zv0yb4SPo0cA7wLjO7p8htRLj283szq+l8Bvn+RM2naYBsftq83M45V1U1/zaU1E64yRRgN2CWpFOj5zeZWZ+klcBdZnZGtM1pwBeBK4E/SXpjbJdPm9naKN9dwHXACmAGcBbw58Dbq3pSBeQGQmUr0xaCT2tTnuygBx/nnIOpqfnsCFyTSBt5vhewilCu+AWSE6LH06Ml7n2EoASht9tHgF0I3bAfAv7KzG6ebKFLlbzm09I0SG6opdbFcM65ulTz4GNmq4Bxb/U3swWJ56czOugU2u6M8ktWWcngk2kaosc7HDjnHDDNertNJ/lcaHZLt4X4nmkaIjfkzW7OOQcefKomec0n0zRMbthrPs45Bx58qmZUzad5mNyw13yccw48+FTNluDTHu7t8eDjnHNbefCpklyYzofMjCj4pIfJmd9k6pxz4MGnavL58DgywkEmbeSGPfg45xx48KmaLcEnijeZtJEreuJW55zbvnnwqZLRwQdvdnPOuYgHnyrJRcEnk4ke00aOFqzmQ5w651z98eBTJfl8GMShKbq1ZyQIDQ5OUYGcc66OePCpkvwgpMmhaCChkeAzMsmcc841Mg8+VZLLp8gov+V5JppOO9ebH2sT55xrGB58qiQ/KNLa2saWiQa0zvV5u5tzznnwqZJRwSfjNR/nnBvhwadK8kMp0tp6fceb3ZxzbisPPlWSG0yRScWu+bSGlzrX58HHOec8+FRJfihFOlWo5uPXfJxzzoNPlYwKPm0jNR8PPs4558GnSkYFn2g67YE+v8/HOec8+FRJbriJTFO85hOCj99k6pxzUxB8JO0r6TuSHpU0JGlZkdvNlnSFpC5JmyT9UNK8AvlOlvS/kvolPSZpccVPogj5oSbSqeEtz7c0u3nwcc65Kan5HAicBDwBPFnCdj8GFgFnAqcDhwPXxzNIOhK4DrgTeAvwC+BHkk6YZJlLlh9Okd6m5hNmMfXg45xzMBXzOt9oZjcASLoWmD/RBpKOAE4Ajjazu6O0PwH3SzrezG6Psp4P3G1m50bP75R0IHABcGuFz2NcueFmOpriNZ+o2a1/eKxNnHOuYdS85mNm5Xz7vgV4eSTwRPv5HfBstA5JLcAxhBpS3FLgCEmzyytxefLDzaTjwafdaz7OOTeipOAj6TpJJ0mqddDaH1hRIP3xaB3APkC6QL7HCee5X9VKV0Demkg3Fwg+A17zcc65UoPIPOBGYLWkf5X0Z1UoUyGdwMYC6V3ROmKPyXxdifXbkHS2pOWSlq9du3aSxdwq1Hy2zhy3Jfj0+2xyzjlXUvAxs0XAq4HvAouBxyT9VtKZkjqqUL6qM7PLzWyhmS3cYYcdKrbfnDWTidd8ZoQptHMDHnycc67k5jMze8bMLjCzvQidAFYCXwXWSLpK0qLKFhEItZdC12w62VqzGXlM5utMrK+JvDWTbi5Q8/FmN+ecm3SHg3sJ3ZqfANqBY4E7JD0i6ZDJFi5mBVuv7cTFrwU9DeQL5NsfGKa0bt2Tlrc06XQs+Gyp+dSyFM45V5/KCj6SjpZ0BfAScAnwO+BwM9sdeC2wHri6YqWEm4Gdo/t4RsqwENg7WoeZDRAC4TsT2y4G7jWzTRUsz4TyNJOJ13xmhnm0vdnNOedKvM9H0gXAewhf+ncDHwKuMbP+kTxm9pik84Ffj7GPdsJNpgC7AbMknRo9v8nM+iStBO4yszOifd4r6VbgakkfI9RkvgzcE7vHB+AiYJmkrxFuQD0pWk4s5TwnzYwcGdLprUlNbRmaGCTnMyo451zJN5n+X+Aq4PtmtnKcfCuA94+xbkfgmkTayPO9gFVRuZoSeRYTri19n1Bj+zlwbjyDmd0TBbJ/Af6RcB/QaWZW0xtMyefJs22zG+k0GQbI5VTTojjnXD0qNfjsXsxNoma2gRCkCq1bBYz7DWxmCwqkbQTeFy3jbXs9iWF3ai6fJ5+o+dDURIYcudyUlco55+pGqdd88pLeUGiFpMMk+e37gOXyDJImk942PUPOm92cc47Sg894NZY04DOlAfloqux0Ztv0jPLk8t7s5pxzEza7SdoDWBBLOkRSayJbK/BewjWWhpfPhhicTm8baDIaJJf3KZScc66Yaz7vAz4HWLT8xxj5soTpDhreSM0n07JteiblNR/nnIPigs9lwLWEJrdHgb+LHuNywPPRvTYNb2Tk6nSmQM1n0Gs+zjk3YfAxs7XAWgBJewFrzMz7bI1jS7NbZttAk0l58HHOOSjumk+7mfVFT9cCzZLG3C6Wt2FtDT6Jmk+TBx/nnIPimt26JR0RTd7WQ7juM57kzaENZ6TZLdOSCD6pIXJD6UKbOOdcQykm+LyfMGjnyN8+ONkEttR8WhLNbs1D9AwmOwo651zjKeaaz1Wxv6+samm2E/n+qMNBMvg0DZPLN3zF0DnnSh5YtBloivdqk3QCcABwt5k9VOHyTUtjBp/mIXLDHnycc67Usd3+B9hENGiopHOBrwEDQJOkU8zs5xUt4TSU6w/D32Vak8FnmNxwqS+5c85tf0rtevVG4KbY848Dl5hZG2Fq7c9WqmDTWT6arTTdum0tJ9NsHnycc47Sg888wgRySDoI2BX4drTuGkLzW8MbM/ikPfg45xyUHnxeZus4bycCz5nZSE+4NsIkbw1vJPhk2goEH/Ou1s45V+rP8GuAL0t6HWHMt2/G1h0CPFWpgk1nI9d80m3bvryZjAcf55yD0oPPp4DNwOGEAUa/FFt3GKFDQsPL58KtUKOb3fDg45xzlBh8zGwQuHCMdadUpETbgXxurJoP5MgU2sQ55xpK2Ve/JTUBLcl0H9sNctFdUC3tiZpPixiimaHcEE0Zv9/HOde4SupwIGmWpG9KepFwb093gaXh5QZCs1umPVHziUJ1vsdnnnDONbZSaz7fAd5KuKfnMcI8PiWRdADwDeAIYGO0r8+b2dA42ywhTGhXyGfM7EtRvisJM6omvcbMVpRa1nLloldldPAJA43mugdondteq+I451zdKTX4vBn4qJl9t5yDSeoEbicErpOBfYBLCDWw88bZ9LvALxNpbwc+CdycSF9B6IkXt6qc8pZrS/CZsW3ngkw03E6u22s+zrnGVmrw6QVWT+J4HyDcD3SKmW0GbpM0C1gi6eIobRQzW508rqTzgRVm9kiyjGZ23yTKOGljBp9ouJ1cb77WRXLOubpS6k2mlwAflFTujGhvAW5JBJmlhIB0dLE7kTQP+EvgR2WWo6py+dC8Nmpg0ZHg0+MTwTrnGlupNZ/dgNcBT0i6k3DNJs7M7JPjbL8/cEdig+cl9UXrbiyyHO8A0hQOPgdI2kzoifcA8Fkzu6vI/VZELg8phmhqSvR285qPc84BpQefUwlD6DQTah5JRrgOM5ZORgcsgK5oXbHeDTxkZskRFR4G7idcU9oB+GdC096R0Uyso0g6GzgbYI899iihCGPL5USGHKFCt9XIcDu5vsGKHMc556arUm8y3ataBSmWpF0ITXSjgpyZfT2R9ybgj8BnCB0URjGzy4HLARYuXFiRWVpzgyJDnrGCz4DXfJxzDa7cazfl6gJmF0jvjNYV412AKGIon+iG15uAQ4stYCXk8ikyGn1dZ6Trtdd8nHONruTgI+lgSf8j6WlJA5IOjdK/IOktE2y+gnBtJ76/3YH2aF0x3g3cY2YvFJnfoqVmcoMio9EBZmvwGfOWJuecawiljnDwFuBBYGfgasJF/xEDwD9NsIubgTdL6oilLQaywISdAiQtIExoV1QvN0ltwF9FZa6Z3GCKTGp001rLjBB8Bjz4OOcaXKk1ny8BV5rZ0cAXEuseAV4/wfbfJgSpn0g6PrrYvwS4NN79WtJKSd8rsP27gUHC1A7bkDRb0q8l/V9Jx0laDNxJmPDui8WcXKWEZrfRNZ/WmSH49Pd68HHONbZSe7vtD3ws+jvZlLUZmDvexmbWJek4wjxANxJ6vn2VEICS5So08ua7gV+Z2boC6waAtYSREnYE+oF7gaPNbPl45aq0/KDINI0OPm0d4eXO9tW0FdA55+pOqcHnFWDvMdYdCDw/0Q7M7DHg2AnyLBgj/fXjbNMP1MW0DrmhFJlUgeAzK7RSevBxzjW6UpvdlgIXSjoylmaS9iN0ff5hxUo2jeUGm8ikRjetefBxzrmg1JrP+cABwN3AmijtBkIHhFup8bWVepUbSpFpKhB8ZoeJ5LLZWpfIOefqS6k3mQ4Ab5V0LHA8MB/YQLgOc1sVyjct5YaaaW8p0OFgdpjQJ9uvWhfJOefqStHBR5IIQ+q8EdgpSl5NuKh/e+WLNn3lhpqY0zR62oTMjDRi2IOPc67hFRV8JB1CuN6zLzAErCOMMjCP0CvtKUnvLjC9QUPKDTeTaR4ela6UaKOP/gEPPs65xjZhhwNJOwG3ELounwR0mNmuZrYL0EGY2TQH3CJpx2oWdroIwadwp4I29ZP14OOca3DF9Hb7J8IIBG8ys1ui6z5AuAZkZjcDR0V5zqlOMaeXsWo+MBJ8Ct3C5JxzjaOY4HMCcNlYs4wCmNlG4D+AEytUrmktZ81k0mMEn9QA2ZwHH+dcYysm+OwLPFREvgejvA0vZ2ky6cLr2lIDZPMefJxzja2Y4DMb2FREvm5g1uSKs30IwafwNZ/WVJ5srtTbq5xzbvtSTPARxU9J4FfSgRwZMpkxOhw058gOevBxzjW2Yr8Fb5EKDNNc3r62b8PDUfApHIfbmvKsH5xZ40I551x9KSZgfL7qpdiODPfnGKSVTKbw+rZ0nmzfGCudc65BTBh8zMyDTwnyvTkYL/g0D5IdGqM3gnPONYiSp9F248v1hhlMMy1jNLtlBskOtdSySM45V3c8+FRYricHjB18WtPDZIe92c0519g8+FTYxDWfIbLWWssiOedc3fHgU2Fbgk9r4Ze2rWWYfmvFfD4551wD8+BTYbm+0CN9vOADMDB6xgXnnGsYHnwqbMKaT2uo8vhsps65Rlbz4CPpAEm/ktQn6UVJF0oad7AzSQskWYFlaYG8J0v6X0n9kh6TtLh6ZzNaLhumz860FT4lDz7OOVfjUQkkdRJmPX0MOBnYB7iEEATPK2IXHwN+E3u+LrH/I4HrgMuAcwnzD/1IUpeZ3TrpEyjClma3sYJPW3jM9g7jFU/nXKOq9ZA4HwDagFOiKRpukzQLWCLp4vGmbYg8YWb3jbP+fOBuMzs3en6npAOBC4DaBJ/s+MGnNerolt2cB/x+H+dcY6r1T++3ALckgsxSQkA6ejI7ltQCHAP8OLFqKXCEpNmT2X+xctnQoWDMmk976IIdgo9zzjWmWgef/YEV8QQzex7oi9ZN5ApJQ5LWSLpUUlts3T5AOrl/4HHCee5XfrGLt+WaT3vhSqUHH+ecq32zWyewsUB6V7RuLAPAtwhNZ5uBRcAnCQHn5Ni+KbD/rsT6bUg6GzgbYI899hinCMUZCT7ptjGCz4wQ7/u7Pfg45xrXtJgGwczWAOfEkpZJehm4TNLrzOz3k9j35cDlAAsXLpz0rZ+5gajZbayaz+wwtE52Y/9kD+Wcc9NWrZvduggzoyZ1srWGUqxro8fDYvumwP47E+urKtcf4ldmRuGRq9s6Q4+D7Ea/y9Q517hqHXxWkLi2I2l3oJ3R12omYonHp4F8cv/R82HgyRL3X5Zcf1TzmTD45GpRHOecq0u1Dj43A2+W1BFLWwxkgbtK3Nep0eODAGY2ANwJvDORbzFwr5ltKr24pcsNjF/zaZ3bDniHA+dcY6v1NZ9vE27+/ImkLwN7A0uAS+PdryWtBO4yszOi50uADsINppuBo4CPAz8xs0dj+7+IcD3oa8D1hJtMTwJOrOZJxW0JPjMLT5vQNn8GANnuiWYld8657VdNaz5m1gUcBzQBNxKm6P4q8LlE1uYoz4gVhPuArgBuAk4DvhI9xvd/D6FGdDxwC/A24LRajW4AE9d8tgSfHg8+zrnGVfPebmb2GHDsBHkWJJ4vJdwsWsz+ryfUeqZELrqUM9bAoum5HaQYor93qIalcs65+uKDi1XYSPBJF674oPY22siS7fUJfZxzjcuDT4XlctDEIE1jjdMt0aZ+slkPPs65xuXBp8JyeZFh/G7UbakBn1LBOdfQPPhUWD4PGY3fjbq1KU+2319651zj8m/ACsvlRSY1fk+2tqY82QF/6Z1zjcu/ASssN6gJaz5t6UGyuXEnb3XOue2aB58Kyw2mJq75ZAbJ5j34OOcalwefCism+LS3DNE3WHgEBOecawQefCosBJ/xbyCd055n4+DMGpXIOefqjwefCssNNZFpGr/mM29Wjg3Dc2pTIOecq0MefCosBJ/hcfPMnTXERjoZ7PU5fZxzjcmDT4WF4DN+s9u8uSE4bVzdU4siOedc3fHgU2G5oWYyzeMHn7lzBcD61T7MgXOuMXnwqbDccDOZ5vHHbZu3Q3jZN7zYX4siOedc3fHgU2G54SYyzRNc89kpDHm9fo1Ppe2ca0wefCosZ2ky6fGDz7ydQ/DZ8IpPKOeca0wefCosN5wmkx6/2W3ubq0ArF87fpByzrntlQefCstaC60T1Hxm7zKDFENs2FCjQjnnXJ3x4FNJZnTTQUfb+M1pqTmz6KSL9V3+8jvnGpN/+1XQcH6IXmbS0T5+V2s6OpjHejZs8sFFnXONqebBR9IBkn4lqU/Si5IulDTut7CkwyVdIWlltN0Tkj4nqTWRb4kkK7CcWN2zCnrWhxELZrZNEHzSaeaqiw096RqUyjnn6k9zLQ8mqRO4HXgMOBnYB7iEEATPG2fTxVHeLwNPAQcDF0WP70jk3QQkg83jky17MbrX54AZdMyYuCPBvEwPa7p3rn6hnHOuDtU0+AAfANqAU8xsM3CbpFnAEkkXR2mF/KuZrYs9XyapH/iOpD3N7LnYukEzu686xR9fzyt9QCcdcyZuTps7M8cfe1snzOecc9ujWje7vQW4JRFklhIC0tFjbZQIPCMejh53rVzxJqf7lTBcTsfciZvT5nUOsz7n0yo45xpTrYPP/sCKeIKZPQ/0RetKcQQwDDydSJ8jaZ2kvKSHJZ1SdmlL1L02DJfTMW/iieLm7pCie3gm+dz49wQ559z2qNbBpxPYWCC9K1pXFEk7E64R/cDMXomtWgl8Angn4VrQi8B14wUgSWdLWi5p+dq1a4stQkHdIx0O5rVMmHfeziFAbXh206SO6Zxz01Gtr/lMmqQM8GOgB/hofJ2Z/Vci743Ab4ELgJ8U2p+ZXQ5cDrBw4cJJVUO61+cB6NixbcK8c3efAcCGx19mpz+bM5nDOufctFPrmk8XMLtAeme0blySBFwNHAicZGbjbmNmRgg6B0/UnbsSejaGm0s7dmqfMO+8vcPLsP7J9VUtk3PO1aNa13xWkLi2I2l3oJ3EtaAxfI3QRfsvzayY/AAWLVXXvTHc39Oxy8QdCea+eh4A654Zq4Ofc85tv2pd87kZeLOkjljaYiAL3DXehpI+DZwD/L2Z3VPMwaKa0juA35vZBHd+Tl735hDjZuw0cfDZ6/D5AKxc6R0OnHONp9Y1n28D5wI/kfRlYG9gCXBpvPu1pJXAXWZ2RvT8NOCLwJXAnyS9MbbPp81sbZTvLuA6Qi1qBnAW8OfA26t6VpHuHjGDHlLpImo+OzSxc+plHls1owYlc865+lLT4GNmXZKOA74J3Ejo+fZVQgBKlit+jeaE6PH0aIl7HyEoQejt9hFgF0I37IeAvzKzmydf+ol194qOVC9Q3P07B8x8gcfWzq9uoZxzrg7VvLebmT0GHDtBngWJ56czOugU2u6MSRRt0nr6muhoyhad/4Ad13HV00diBlIVC+acc3XGR7WuoO7+ZjrSJQSfPXvptpn86QWfVM4511g8+FRQ90CGmZlc0fkPOCzcD/THW1ZXq0jOOVeXPPhUUHeulY6WfNH5D3j7fgA8dvuL1SqSc87VJQ8+FdQz2DLhLKZxO7xxH+ZrHY89PFDFUjnnXP3x4FNB3UPtE89iGidxwNyX+OMLHRPndc657YgHn0oxo9tm0jGztJtG//y1fTzQfxAbVrwycWbnnNtOePCpkOG+fnqZycwSp+hZ/N5WBklz3de904FzrnF48KmQnjXdAHTMLu0lPfS0/Xl1aiVLfzrxHEDOObe98OBTIT0v9QAUNYV2nFoy/O1hT3Hnywew5g8+wrVzrjF48KmQ7pf7AOjoLH3QiL+94NUYKb76oZWVLpZzztUlDz4V0r0udJfumJsuedv937ovZ+78cy65eyHLf138CAnOOTddefCpkO61/QDMnD/xFNqF/Nv357IzL/Gekzcxydm8nXOu7nnwqZDuDdEU2jtMPIV2IbPf8hf819/8hGe7ZnPs4d289FIlS+ecc/XFg0+F9HRFwaeIKbTHcswP3s8v9vkwzzyX4rDX9nPvvZUqnXPO1RcPPhWyqSuMTN2x8yQmh5sxg2Pv/xL3vvq9tK1fzdFvGuKybw1jPtmpc24748GnQp54vo12etl5QevkdjRvHgc/8D0eeOuFnDB0Mx86J8Upx29itd+D6pzbjnjwqZDfPzuLg2Y8Q6qpArPCzZ5N58+u4mffXcvF7Uu45Y40r9m7n69/oYehEoaOc865euXBpwJs2Hh00x4c/KoNldupROqM9/Hx5/+JPyz+F47M38lHzpvJgTu8zNVf72Kw+MGznXOu7njwqYAXH3qJDTaX1x1chZ3Pm8feS7/ITX/Yk+uO+jotXS/x3o908mez1nDp3z7AC3/cXIWDOudcdXnwqYBHf/ECAAcfNadqx9CBB3DKXR/m4ac6uOEdV7PD0Ev889LD2eO1s9itdR3H/NmL/P2pWT7xCbjiCnjmGbyjgnOubpU+FswkSToA+AZwBLAR+C7weTMb92qGpNnA14C3E4Lmz4FzzWx9It/JwL8Arwaeifb9PxU9iYRH7w3juh301wuqeRgAUvvuzduu3Zu3mfHkNY/wi/94noeWD/Psk/P57ZO78mLqVQwMh0FKX/Uq4+gjh1j0xgGOfUMPe++ShY4O6OyElP/ucM5NnZoGH0mdwO3AY8DJwD7AJYRgct4Em/8Y2A84ExgGvgxcD7wptv8jgeuAy4BzgZOAH0nqMrNbK3kucY8+nmaP5j8xZ8/dqnWI0ST2e9fr2e9drw9VnD/8Aa69Clv6Pzz+ZIplLOKu1Udz29JF/HDpTsAMFvAsx/FTDm9+hP33GmCnA+ax4yG7MeewfUgd/FrYfXdQBTpMOOfcBGQ1bJuR9GngE8CeZrY5SvsEsATYeSStwHZHAL8Fjjazu6O0NwD3A39pZrdHabcAaTM7NrbtTcAsMztyovItXLjQli9fXvJ5HdTyBAvmbubGNYeXvG3FjQSi5cth9WosnWHF5l2549m9uP2J3Vm2Yic2Zkd3B28mT5o8GeXJpAbpTPewS/tmdpnVy65zs+zQOUS6tYlUpplUphll0rTNSNE5V8yZm6Jzfoo5O7bQuVs7s3edQdOcDmiuecXaOVdjkh40s4Wlblfrb4e3ALckgsxSQi3maODGcbZ7eSTwAJjZ7yQ9G627XVILcAyhxhO3FLhC0mwz21Sh89hiqD/PpuEODt6vTgZkk+Cgg8ICCHhNtHyIEJueew6eegrWroW1z/XR9dQ68mvWkdvYS743z0DfIOu7W1jTO4sHVu/MmlU70EdpN8/OYhNz2ERn02bmpHvozPQxqzXHjNYh2tuM9najvV00tzShphRKCTU3oSahVCqkjSwpbfu8KUVzWtsumZCWbkmFv5shnTaamyCdEc3NRGnhsTktlBIoekylQqUvel5waUqRShH+DpsiQapJpGSkUtHfKbYsSiVqkvGaZbKWWYl1tThGtY7vGkqtg8/+wB3xBDN7XlJftG6s4LM/sKJA+uPROghNeOkC+R4nNOvtBzxQXrHH1tSa5vn8rgzmd6n0rqtCggULwhK0A3tES2FmkO0ZYqi3n+G+foZ7swz19pPdlGPj2jxd64bYuGGIrvXDbNxgdHXBxk3QtbmZjb1puvrm8HT/zmza1ErfhhayQxn6rA1rkP4uYrhq+57q17D0c7Mx/o7vs/KtMdv9PpuaIFV4LrGRGB9/TKbttFPopFRLtQ4+nYROBkld0bpytts7locC+boS67ch6Wzg7Ohpj6QnxilHueYD66qw33rWiOcMBc57e+50GDu3ir3f0+j1qp/P+FC0lOnZZ0uqiCbPe89yjtnwjfJmdjlweTWPIWl5OW2i01kjnjP4eU91OWqpEc8ZKnfeta6zdwGzC6R3srWGUu52I4/JfJ2J9c4556ZYrYPPCrZeowFA0u6ECw+FrumMuV0kfi3oaSBfIN/+hK7ZT5ZRXuecc1VQ6+BzM/BmSR2xtMVAFrhrgu12ju7jAUDSQsL1npsBzGwAuBN4Z2LbxcC91ejpVoKqNuvVqUY8Z/DzbiSNeM5QofOu9X0+nYQbTP9A6F69N3Ap8DUzOy+WbyVwl5mdEUu7hTBqwcfYepPpK2aWvMl0GfBNwg2oJ0X5T6zmTabOOedKU9Oaj5l1AccBTYRu1Z8Hvgp8LpG1OcoTt5hQO/o+cDXwIPA3if3fA5wKHA/cArwNOM0Dj3PO1Zea1nycc8458FGtJ0XSAZJ+JalP0ouSLpRU+E6vbbebLekKSV2SNkn6oaR5tSjzZJVzzpIWSLICy9JalXuyJO0r6TuSHpU0JGlZkdtN5/e65HOe7u+1pHdK+pmkP0nqkfSgpL8tYrsWSZdIekVSr6RfSFpQgyJXxCTOu9B7fV8xx2z4+3zKVe1BUuvRJM8ZwvW338Se18cNesU5kHAN8T7CSBrFmpbvdaTcc4bp+17/P+BZ4KOEMp8E/Lek+Wb2jXG2+3dCk/9HgbWE8Spvk3SQmfVXt8gVUe55Q/gOuDb2vLuoI5qZL2UswKcJ9w7NiqV9AuiLpxXY7gjCTdxHxdLeEKUdP9XnVaVzXhCd31un+hwmce6p2N/XAsuK2GbavteTOOdp/V4D8wuk/Tfw7DjbvAoYBN4TS9sNyAFnTvU5Veu8ozwGnFPOMb3ZrXxjDZLaRhgkdbztRg2SSvjV8ZZqFLSCyj3nac/MyhmgbTq/1+We87RmZoVqaA8Du46z2QnR409i+/kTcA/T4H2Gss97Ujz4lG/UYKdm9jyhFlDohtgxt4vEB0mtV+We84gromsHayRdKqmtGoWsI9P5vZ6s7em9PoLxb1LfH1htZj2J9On+Pk903iOWSBqUtE7S9yXNLWbnfs2nfNUcJLVelXvOA8C3gFuBzcAi4JOEa0YnV7SE9WU6v9fl2q7ea0nHEWZPfv842cr9v6hbRZ43wFWE22bWAguB84HXSXqDTTA7tQcfV3VmtgY4J5a0TNLLwGWSXmdmv5+iorkK257e66i32n8DN5jZlVNbmtop5bzN7PTY07slPQ7cBPw1oWPNmLzZrXzVHCS1XlWy7CO9Yw6bVInq23R+rytp2r3XUdPRzcBzwN9NkH27eZ9LPO9Cfgn0AIdOlNGDT/mqOUhqvSr3nAuxxOP2aDq/15U0rd5rSe3Az4EModde3wSbrAB2l5Sc7ndavc9lnPcoFnWBo4j32oNP+ao2SGodK/ecCzk1enywEgWrU9P5va6kafNeS2oGriGMI3mimb1SxGYjw3dtGe5L0q6Ee7mmxftc5nkX2s+JwEyKeK99eJ0yVXuQ1HpU7jlLWgJ0EG463AwcBXwcuMnM3lHLcyhX9KvwpOjpPwOz2Dom4U1m1rc9vddQ3jlP9/da0uXAWcCHgd8lVj9sZgOSfgVgZsfFtvsO8A62vcl0HjAtbjIt57wVZoFeSLjxfB2hqe084AngLybqcDDlNzdN5wU4ALiD8Mt/DXAR0JTIswq4MpE2B7iC0ENmM+Hi3qibvOpxKeecgXcDy4FNhBvvVgIXAi1TfT4lnPcCQlNCoWXBdvpel3zO0/29js5nonNeRuKGW6CF8ENsLdBLuOi+11SfTzXPmzBI9G+A9YS51F4gjPQwu5hjes3HOedczfk1H+ecczXnwcc551zNefBxzjlXcx58nHPO1ZwHH+ecczXnwcc557ZD5c6+O8a+5kX7eklSVtIKSe+ZTPk8+DgXkbQkMR3wi5Kuk7RPlY731ug4Cyq83zdEN3sm05dImi4zirrJG5mJ9gmKmxqhIEmzgLuB1wP/FO3zG4RheMrmo1o7t61NwInR33sTbqL9laQDzax36opVkjcQRiJYkkj/LmH4e9cYbjSzGwAkXQvML3M/nyHcRLvQzLJR2p2TLZzXfJzb1qCZ3Rct/w28F9iTrcPMbDHdJkgzs9VmVvfjq7nKsCJmopXUKuliSS9IGpD0e0nJz/r7gO/FAk9FePBxbnwjX9YLJK2SdImk8yWtJgyXg6R2Sf8etYf3S3pA0gnxnShYIukVSd2SriaMlRbPsyhqhnttIn1Z9Ms1nnaUpDsl9UjaFOU5RNLphCYRYs2Hy6Lno5rdJO0l6XpJm6Ny3Shp30Qek/RhSV+UtDY6h29JainvJXV15FrgdOCLhDl4HgB+Jun1ED4fwI7ARkk3ScpFn4FLJXmzm3NVtCB6fCl6PA34I/BBtv7//CfwNkLzxErCAI2/kHSMmd0T5TkXuIDwT/5r4BTg4nIKJGkRcBuh6eO9hLHE/g+wG/AL4BLCQKBHRJtsHmM/LcCvCONynQUMAp8H7pJ0kJltiGX/Z8KYfn8PHAx8iTDnS1nn4KaewmylfwUsMrORUelvlbQf8FngncDOUfrFwFJCk/TrCJ/jQeATZRdgqge088WXelkI10jWEYJKM7Af4Qt+M7ALYfDFNUBrbJvXEEarfm8sLUUY+fuW6HkT8CLwH4nj3ca2Azcuip6/NpFvGXBt7Pm9hME7NcZ5nEM0tUqh84s9/0D0BbJ3LO1VhAFBPx1LM+DuxL6uB+6b6vfMl6I/29cyejDUL0Wf5+bE8jng2SjPX0Tv/32JbS8A+oH2csvkzW7ObWseoSaQJ/QS2htYbGF6aIBf2bZD5B8OiDAXCrClrf0aYGQen90JweuGxLF+UmrhFCYs+3PgKou+BSbhDcBDZvbMSIKZrSaMVHxkIu+tieePEQKVm77mE2o2+cSyhPCZha0zsSY7GNxB6IRQdk9Qb3ZzblubgOMJv/ZeAl5MfMm/nMi/C9Bjo2d9fBloj5q2RpoukhN0lTNhVych2K2ZKGMRdmH0+RCl7ZlI25h4ngNaK1AGN3U2AH8C3j5OnqcJ77US6SPPJ+zUMBYPPs5ta9DMlo+zPlnbWAPMlNSeCEA7AX0WJuEauV60Y2Lb5PORGlXyQm4noTkQwi/RYULgmKw1hHtBknYifDG57duvCNfyesys4HTfZpaTdBtwTGLVcUAf4RpnWbzZzbnJeYAQkEamikaSoucjnQ1eINSiTk5se0ri+ero8TWxfe0O7D/y3MK9RvcD74mOU0gu2naimsn9wGFRj6aR4+1GaOe/Z8yt3LQQ9cI8VdKphM4oO4w8j2apvQ24BbhN0jmSjpF0sqTPSfpSbFcXAodIukLSCZI+BnwK+KKZDZRbPq/5ODcJZva4pB8B35TUQWimOIsQMP4xyjMk6WLg36Kuzr8mTLn8msS+VktaDlwkqY/w4/AzjK6FfIowdfHNCtMf9xJ6ti03s58DI79iPyzpDmCzmT1RoPhXAp+M9nMBMES42LwO+E65r4mrGzsSuxYZGXm+l5mtknQK4TP2EWAPwmftEaLu+gBm9jtJf03ooHAaobn4C9Hz8k11LwxffKmXhURvsALrVwH/ViC9PfpnfRkYIPREe3MijwijJawFuoEfRv/IW3q7Rfn2JfRu6yV0eDiZRG+3KN/RhCFP+gjXY+4EXh871sWEHnbDRL2cCp0foUPF9VGZeoCfA69O5DHgnFJeK198mWjxabSdc87VnF/zcc45V3MefJxzztWcBx/nnHM158HHOedczXnwcc45V3MefJxzztWcBx/nnHM158HHOedczf1/HJvR+OxlhfcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.distplot(y_test, hist = False, color = \"r\", label = \"Actual value \")\n",
    "sns.distplot(rf_predict, hist = False, color = \"b\", label = \"Predicted Values\", ax = ax)\n",
    "plt.title('Random Forest Regression')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prouction:  6774.454545454545\n",
      "yield:  67.74454545454545\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zainr\\AppData\\Local\\Temp/ipykernel_19176/1651425909.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_row['Crop_Year'] = inp[0]\n",
      "C:\\Users\\zainr\\AppData\\Local\\Temp/ipykernel_19176/1651425909.py:13: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_row[i] = 0\n",
      "C:\\Users\\zainr\\AppData\\Local\\Temp/ipykernel_19176/1651425909.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_row[i] = 1\n",
      "C:\\Users\\zainr\\AppData\\Local\\Temp/ipykernel_19176/1651425909.py:15: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_row['Area'] = inp[3]\n"
     ]
    }
   ],
   "source": [
    "inp = [2012, 'Kharif',new_h,100]\n",
    "\n",
    "test_row = x_test.head(1)\n",
    "\n",
    "test_row['Crop_Year'] = inp[0]\n",
    "\n",
    "string = \"\"\n",
    "for i in test_row.columns[2:]:\n",
    "    string = str(i)\n",
    "    if inp[1] in string or inp[2] in string:\n",
    "        test_row[i] = 1\n",
    "    else: \n",
    "        test_row[i] = 0\n",
    "\n",
    "test_row['Area'] = inp[3]\n",
    "\n",
    "production = model.predict(test_row)[0]\n",
    "\n",
    "yd = production/test_row['Area']\n",
    "print(\"prouction: \",production)\n",
    "for ind,val in yd.iteritems():\n",
    "    print(\"yield: \",val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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