{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6bdfd636",
   "metadata": {},
   "source": [
    "# Import Libaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7bee9b73",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Noor Saeed\\anaconda3\\lib\\site-packages\\pandas\\core\\computation\\expressions.py:21: UserWarning: Pandas requires version '2.8.4' or newer of 'numexpr' (version '2.8.1' currently installed).\n",
      "  from pandas.core.computation.check import NUMEXPR_INSTALLED\n",
      "C:\\Users\\Noor Saeed\\anaconda3\\lib\\site-packages\\pandas\\core\\arrays\\masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.4' currently installed).\n",
      "  from pandas.core import (\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2822305c",
   "metadata": {},
   "source": [
    "# Importing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5b6f8884",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crop = pd.read_csv(\"Crop_recommendation.csv\")\n",
    "crop.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9ddfb22",
   "metadata": {},
   "source": [
    "# Asq Six Question to yourself"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3ca70c00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2200, 8)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crop.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e2ae9b60",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2200 entries, 0 to 2199\n",
      "Data columns (total 8 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   N            2200 non-null   int64  \n",
      " 1   P            2200 non-null   int64  \n",
      " 2   K            2200 non-null   int64  \n",
      " 3   temperature  2200 non-null   float64\n",
      " 4   humidity     2200 non-null   float64\n",
      " 5   ph           2200 non-null   float64\n",
      " 6   rainfall     2200 non-null   float64\n",
      " 7   label        2200 non-null   object \n",
      "dtypes: float64(4), int64(3), object(1)\n",
      "memory usage: 137.6+ KB\n"
     ]
    }
   ],
   "source": [
    "crop.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9efad4c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "N              0\n",
       "P              0\n",
       "K              0\n",
       "temperature    0\n",
       "humidity       0\n",
       "ph             0\n",
       "rainfall       0\n",
       "label          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crop.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1f7bf8c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crop.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3d5b7413",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2200.000000</td>\n",
       "      <td>2200.000000</td>\n",
       "      <td>2200.000000</td>\n",
       "      <td>2200.000000</td>\n",
       "      <td>2200.000000</td>\n",
       "      <td>2200.000000</td>\n",
       "      <td>2200.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>50.551818</td>\n",
       "      <td>53.362727</td>\n",
       "      <td>48.149091</td>\n",
       "      <td>25.616244</td>\n",
       "      <td>71.481779</td>\n",
       "      <td>6.469480</td>\n",
       "      <td>103.463655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>36.917334</td>\n",
       "      <td>32.985883</td>\n",
       "      <td>50.647931</td>\n",
       "      <td>5.063749</td>\n",
       "      <td>22.263812</td>\n",
       "      <td>0.773938</td>\n",
       "      <td>54.958389</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>20.211267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>21.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>22.769375</td>\n",
       "      <td>60.261953</td>\n",
       "      <td>5.971693</td>\n",
       "      <td>64.551686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>37.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>25.598693</td>\n",
       "      <td>80.473146</td>\n",
       "      <td>6.425045</td>\n",
       "      <td>94.867624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>84.250000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>28.561654</td>\n",
       "      <td>89.948771</td>\n",
       "      <td>6.923643</td>\n",
       "      <td>124.267508</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 N            P            K  temperature     humidity  \\\n",
       "count  2200.000000  2200.000000  2200.000000  2200.000000  2200.000000   \n",
       "mean     50.551818    53.362727    48.149091    25.616244    71.481779   \n",
       "std      36.917334    32.985883    50.647931     5.063749    22.263812   \n",
       "min       0.000000     5.000000     5.000000     8.825675    14.258040   \n",
       "25%      21.000000    28.000000    20.000000    22.769375    60.261953   \n",
       "50%      37.000000    51.000000    32.000000    25.598693    80.473146   \n",
       "75%      84.250000    68.000000    49.000000    28.561654    89.948771   \n",
       "max     140.000000   145.000000   205.000000    43.675493    99.981876   \n",
       "\n",
       "                ph     rainfall  \n",
       "count  2200.000000  2200.000000  \n",
       "mean      6.469480   103.463655  \n",
       "std       0.773938    54.958389  \n",
       "min       3.504752    20.211267  \n",
       "25%       5.971693    64.551686  \n",
       "50%       6.425045    94.867624  \n",
       "75%       6.923643   124.267508  \n",
       "max       9.935091   298.560117  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crop.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1056bfba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "label\n",
       "rice           100\n",
       "maize          100\n",
       "jute           100\n",
       "cotton         100\n",
       "coconut        100\n",
       "papaya         100\n",
       "orange         100\n",
       "apple          100\n",
       "muskmelon      100\n",
       "watermelon     100\n",
       "grapes         100\n",
       "mango          100\n",
       "banana         100\n",
       "pomegranate    100\n",
       "lentil         100\n",
       "blackgram      100\n",
       "mungbean       100\n",
       "mothbeans      100\n",
       "pigeonpeas     100\n",
       "kidneybeans    100\n",
       "chickpea       100\n",
       "coffee         100\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crop['label'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e3af150",
   "metadata": {},
   "source": [
    "# Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8c35d395",
   "metadata": {},
   "outputs": [],
   "source": [
    "crop_dict = {\n",
    "    'rice': 1,\n",
    "    'maize': 2,\n",
    "    'jute': 3,\n",
    "    'cotton': 4,\n",
    "    'coconut': 5,\n",
    "    'papaya': 6,\n",
    "    'orange': 7,\n",
    "    'apple': 8,\n",
    "    'muskmelon': 9,\n",
    "    'watermelon': 10,\n",
    "    'grapes': 11,\n",
    "    'mango': 12,\n",
    "    'banana': 13,\n",
    "    'pomegranate': 14,\n",
    "    'lentil': 15,\n",
    "    'blackgram': 16,\n",
    "    'mungbean': 17,\n",
    "    'mothbeans': 18,\n",
    "    'pigeonpeas': 19,\n",
    "    'kidneybeans': 20,\n",
    "    'chickpea': 21,\n",
    "    'coffee': 22\n",
    "}\n",
    "crop['crop_num']=  crop['label'].map(crop_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b1a53f7f",
   "metadata": {},
   "outputs": [
    {
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       "      <td>6.502985</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>40</td>\n",
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       "    <tr>\n",
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       "      <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",
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       "      <th>2195</th>\n",
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       "      <td>34</td>\n",
       "      <td>32</td>\n",
       "      <td>26.774637</td>\n",
       "      <td>66.413269</td>\n",
       "      <td>6.780064</td>\n",
       "      <td>177.774507</td>\n",
       "      <td>coffee</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2196</th>\n",
       "      <td>99</td>\n",
       "      <td>15</td>\n",
       "      <td>27</td>\n",
       "      <td>27.417112</td>\n",
       "      <td>56.636362</td>\n",
       "      <td>6.086922</td>\n",
       "      <td>127.924610</td>\n",
       "      <td>coffee</td>\n",
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       "    <tr>\n",
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       "      <td>33</td>\n",
       "      <td>30</td>\n",
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       "      <td>67.225123</td>\n",
       "      <td>6.362608</td>\n",
       "      <td>173.322839</td>\n",
       "      <td>coffee</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2198</th>\n",
       "      <td>117</td>\n",
       "      <td>32</td>\n",
       "      <td>34</td>\n",
       "      <td>26.272418</td>\n",
       "      <td>52.127394</td>\n",
       "      <td>6.758793</td>\n",
       "      <td>127.175293</td>\n",
       "      <td>coffee</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2199</th>\n",
       "      <td>104</td>\n",
       "      <td>18</td>\n",
       "      <td>30</td>\n",
       "      <td>23.603016</td>\n",
       "      <td>60.396475</td>\n",
       "      <td>6.779833</td>\n",
       "      <td>140.937041</td>\n",
       "      <td>coffee</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2200 rows × 9 columns</p>\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",
       "2195  107  34  32    26.774637  66.413269  6.780064  177.774507  coffee   \n",
       "2196   99  15  27    27.417112  56.636362  6.086922  127.924610  coffee   \n",
       "2197  118  33  30    24.131797  67.225123  6.362608  173.322839  coffee   \n",
       "2198  117  32  34    26.272418  52.127394  6.758793  127.175293  coffee   \n",
       "2199  104  18  30    23.603016  60.396475  6.779833  140.937041  coffee   \n",
       "\n",
       "      crop_num  \n",
       "0            1  \n",
       "1            1  \n",
       "2            1  \n",
       "3            1  \n",
       "4            1  \n",
       "...        ...  \n",
       "2195        22  \n",
       "2196        22  \n",
       "2197        22  \n",
       "2198        22  \n",
       "2199        22  \n",
       "\n",
       "[2200 rows x 9 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "dff5caca",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>N</th>\n",
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       "      <td>82.320763</td>\n",
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       "      <td>6.980401</td>\n",
       "      <td>242.864034</td>\n",
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       "    <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>1</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    N   P   K  temperature   humidity        ph    rainfall  crop_num\n",
       "0  90  42  43    20.879744  82.002744  6.502985  202.935536         1\n",
       "1  85  58  41    21.770462  80.319644  7.038096  226.655537         1\n",
       "2  60  55  44    23.004459  82.320763  7.840207  263.964248         1\n",
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       "4  78  42  42    20.130175  81.604873  7.628473  262.717340         1"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crop.drop(['label'],axis=1,inplace=True)\n",
    "crop.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5494675",
   "metadata": {},
   "source": [
    "# Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "5a049f55",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = crop.drop(['crop_num'],axis=1)\n",
    "y = crop['crop_num']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9d223a69",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
       "      <td>85</td>\n",
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       "      <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",
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       "      <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",
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       "      <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",
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       "      <td>262.717340</td>\n",
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       "    <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",
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       "    <tr>\n",
       "      <th>2195</th>\n",
       "      <td>107</td>\n",
       "      <td>34</td>\n",
       "      <td>32</td>\n",
       "      <td>26.774637</td>\n",
       "      <td>66.413269</td>\n",
       "      <td>6.780064</td>\n",
       "      <td>177.774507</td>\n",
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       "    <tr>\n",
       "      <th>2196</th>\n",
       "      <td>99</td>\n",
       "      <td>15</td>\n",
       "      <td>27</td>\n",
       "      <td>27.417112</td>\n",
       "      <td>56.636362</td>\n",
       "      <td>6.086922</td>\n",
       "      <td>127.924610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2197</th>\n",
       "      <td>118</td>\n",
       "      <td>33</td>\n",
       "      <td>30</td>\n",
       "      <td>24.131797</td>\n",
       "      <td>67.225123</td>\n",
       "      <td>6.362608</td>\n",
       "      <td>173.322839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2198</th>\n",
       "      <td>117</td>\n",
       "      <td>32</td>\n",
       "      <td>34</td>\n",
       "      <td>26.272418</td>\n",
       "      <td>52.127394</td>\n",
       "      <td>6.758793</td>\n",
       "      <td>127.175293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2199</th>\n",
       "      <td>104</td>\n",
       "      <td>18</td>\n",
       "      <td>30</td>\n",
       "      <td>23.603016</td>\n",
       "      <td>60.396475</td>\n",
       "      <td>6.779833</td>\n",
       "      <td>140.937041</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2200 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        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",
       "2195  107  34  32    26.774637  66.413269  6.780064  177.774507\n",
       "2196   99  15  27    27.417112  56.636362  6.086922  127.924610\n",
       "2197  118  33  30    24.131797  67.225123  6.362608  173.322839\n",
       "2198  117  32  34    26.272418  52.127394  6.758793  127.175293\n",
       "2199  104  18  30    23.603016  60.396475  6.779833  140.937041\n",
       "\n",
       "[2200 rows x 7 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d2601fcf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        1\n",
       "1        1\n",
       "2        1\n",
       "3        1\n",
       "4        1\n",
       "        ..\n",
       "2195    22\n",
       "2196    22\n",
       "2197    22\n",
       "2198    22\n",
       "2199    22\n",
       "Name: crop_num, Length: 2200, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c561ea31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2200,)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "caba8efb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "6774a9dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "41b6bcbb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1656</th>\n",
       "      <td>17</td>\n",
       "      <td>16</td>\n",
       "      <td>14</td>\n",
       "      <td>16.396243</td>\n",
       "      <td>92.181519</td>\n",
       "      <td>6.625539</td>\n",
       "      <td>102.944161</td>\n",
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       "      <th>752</th>\n",
       "      <td>37</td>\n",
       "      <td>79</td>\n",
       "      <td>19</td>\n",
       "      <td>27.543848</td>\n",
       "      <td>69.347863</td>\n",
       "      <td>7.143943</td>\n",
       "      <td>69.408782</td>\n",
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       "    <tr>\n",
       "      <th>892</th>\n",
       "      <td>7</td>\n",
       "      <td>73</td>\n",
       "      <td>25</td>\n",
       "      <td>27.521856</td>\n",
       "      <td>63.132153</td>\n",
       "      <td>7.288057</td>\n",
       "      <td>45.208411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1041</th>\n",
       "      <td>101</td>\n",
       "      <td>70</td>\n",
       "      <td>48</td>\n",
       "      <td>25.360592</td>\n",
       "      <td>75.031933</td>\n",
       "      <td>6.012697</td>\n",
       "      <td>116.553145</td>\n",
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       "    <tr>\n",
       "      <th>1179</th>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>30</td>\n",
       "      <td>35.474783</td>\n",
       "      <td>47.972305</td>\n",
       "      <td>6.279134</td>\n",
       "      <td>97.790725</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1638</th>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>21.213070</td>\n",
       "      <td>91.353492</td>\n",
       "      <td>7.817846</td>\n",
       "      <td>112.983436</td>\n",
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       "    <tr>\n",
       "      <th>1095</th>\n",
       "      <td>108</td>\n",
       "      <td>94</td>\n",
       "      <td>47</td>\n",
       "      <td>27.359116</td>\n",
       "      <td>84.546250</td>\n",
       "      <td>6.387431</td>\n",
       "      <td>90.812505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1130</th>\n",
       "      <td>11</td>\n",
       "      <td>36</td>\n",
       "      <td>31</td>\n",
       "      <td>27.920633</td>\n",
       "      <td>51.779659</td>\n",
       "      <td>6.475449</td>\n",
       "      <td>100.258567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1294</th>\n",
       "      <td>11</td>\n",
       "      <td>124</td>\n",
       "      <td>204</td>\n",
       "      <td>13.429886</td>\n",
       "      <td>80.066340</td>\n",
       "      <td>6.361141</td>\n",
       "      <td>71.400430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>860</th>\n",
       "      <td>32</td>\n",
       "      <td>78</td>\n",
       "      <td>22</td>\n",
       "      <td>23.970814</td>\n",
       "      <td>62.355576</td>\n",
       "      <td>7.007038</td>\n",
       "      <td>53.409060</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1760 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        N    P    K  temperature   humidity        ph    rainfall\n",
       "1656   17   16   14    16.396243  92.181519  6.625539  102.944161\n",
       "752    37   79   19    27.543848  69.347863  7.143943   69.408782\n",
       "892     7   73   25    27.521856  63.132153  7.288057   45.208411\n",
       "1041  101   70   48    25.360592  75.031933  6.012697  116.553145\n",
       "1179    0   17   30    35.474783  47.972305  6.279134   97.790725\n",
       "...   ...  ...  ...          ...        ...       ...         ...\n",
       "1638   10    5    5    21.213070  91.353492  7.817846  112.983436\n",
       "1095  108   94   47    27.359116  84.546250  6.387431   90.812505\n",
       "1130   11   36   31    27.920633  51.779659  6.475449  100.258567\n",
       "1294   11  124  204    13.429886  80.066340  6.361141   71.400430\n",
       "860    32   78   22    23.970814  62.355576  7.007038   53.409060\n",
       "\n",
       "[1760 rows x 7 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab13cdf8",
   "metadata": {},
   "source": [
    "\n",
    "# Scale the features using MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f19981a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "ms = MinMaxScaler()\n",
    "\n",
    "X_train = ms.fit_transform(X_train)\n",
    "X_test = ms.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f3f50c64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.12142857, 0.07857143, 0.045     , ..., 0.9089898 , 0.48532225,\n",
       "        0.29685161],\n",
       "       [0.26428571, 0.52857143, 0.07      , ..., 0.64257946, 0.56594073,\n",
       "        0.17630752],\n",
       "       [0.05      , 0.48571429, 0.1       , ..., 0.57005802, 0.58835229,\n",
       "        0.08931844],\n",
       "       ...,\n",
       "       [0.07857143, 0.22142857, 0.13      , ..., 0.43760347, 0.46198144,\n",
       "        0.28719815],\n",
       "       [0.07857143, 0.85      , 0.995     , ..., 0.76763665, 0.44420505,\n",
       "        0.18346657],\n",
       "       [0.22857143, 0.52142857, 0.085     , ..., 0.56099735, 0.54465022,\n",
       "        0.11879596]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "752a08ae",
   "metadata": {},
   "source": [
    "# Training Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "ac6ef55e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression  with accuracy : 0.9181818181818182\n",
      "Confusion matrix :  [[16  0  3  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0 20  0  1  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 6  0 16  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  1]\n",
      " [ 0  0  0 17  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0 27  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 3  0  0  0  0 17  0  0  0  0  0  0  0  0  0  0  3  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0 14  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0 23  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0 17  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0 19  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0 14  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0 19  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0 21  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0 23  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0 11  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  3 17  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 19  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0 10  0  0  1  0  0 13  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  1  2  0  0 18  2  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 20  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 26  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 17]]\n",
      "==========================================================\n",
      "Naive Bayes  with accuracy : 0.9954545454545455\n",
      "Confusion matrix :  [[17  0  2  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0 21  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0 23  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0 17  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0 27  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0 23  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0 14  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0 23  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0 17  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0 19  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0 14  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0 19  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0 21  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0 23  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0 11  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 20  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 19  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 24  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 23  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 20  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 26  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 17]]\n",
      "==========================================================\n",
      "Support Vector Machine  with accuracy : 0.9681818181818181\n",
      "Confusion matrix :  [[14  0  5  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0 20  0  1  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0 22  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  1]\n",
      " [ 0  0  0 17  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0 27  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0 23  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0 14  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0 23  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0 17  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0 19  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0 14  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0 19  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0 21  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0 23  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0 11  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  1 19  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 19  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  3  0  0 21  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  1  0  0 20  2  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 20  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 26  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 17]]\n",
      "==========================================================\n",
      "K-Nearest Neighbors  with accuracy : 0.9704545454545455\n",
      "Confusion matrix :  [[14  0  5  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0 21  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 1  0 22  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0 17  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0 27  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0 23  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0 14  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0 23  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0 17  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0 19  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0 14  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0 19  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0 21  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0 23  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0 11  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  1 19  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 19  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  3  0  0 21  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  1  0  0 20  2  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 20  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 26  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 17]]\n",
      "==========================================================\n",
      "Decision Tree  with accuracy : 0.9818181818181818\n",
      "Confusion matrix :  [[17  0  2  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0 20  0  0  1  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 3  0 20  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0 17  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0 27  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0 23  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0 14  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0 23  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0 17  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0 19  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0 14  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0 19  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0 21  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0 23  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0 11  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 20  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 19  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  1  1  0 22  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 23  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 20  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 26  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 17]]\n",
      "==========================================================\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest  with accuracy : 0.9931818181818182\n",
      "Confusion matrix :  [[17  0  2  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0 21  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0 23  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0 17  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0 27  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0 23  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0 14  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0 23  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0 17  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0 19  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0 14  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0 19  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0 21  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0 23  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0 11  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 20  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 19  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  1  0  0 23  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 23  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 20  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 26  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 17]]\n",
      "==========================================================\n",
      "Bagging  with accuracy : 0.9886363636363636\n",
      "Confusion matrix :  [[17  0  2  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0 21  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 1  0 22  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0 17  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0 27  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0 23  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0 14  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0 23  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0 17  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0 19  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0 14  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0 19  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0 21  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0 23  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0 11  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 20  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 19  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  1  1  0 22  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 23  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 20  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 26  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 17]]\n",
      "==========================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Noor Saeed\\AppData\\Roaming\\Python\\Python39\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AdaBoost  with accuracy : 0.09545454545454546\n",
      "Confusion matrix :  [[ 0  0  0  0  0  0  0  0  0  0  0  0  0  0 19  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0 21  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0 23  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0 17  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0 27  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0 23  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0 14  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0 23  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0 17  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0 19  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0 14  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0 19  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0 21  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0 23  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0 11  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0 20  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0 19  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0 24  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0 23  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0 20  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0 26  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0 17  0  0  0  0  0  0  0]]\n",
      "==========================================================\n",
      "Gradient Boosting  with accuracy : 0.9818181818181818\n",
      "Confusion matrix :  [[15  0  4  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0 20  0  1  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0 23  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0 17  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  1  0 26  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0 23  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0 14  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0 23  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0 17  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0 19  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0 14  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0 19  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0 21  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0 23  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0 11  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 20  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 19  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  1  0  0 23  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  1  0  0 22  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 20  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 26  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 17]]\n",
      "==========================================================\n",
      "Extra Trees  with accuracy : 0.8863636363636364\n",
      "Confusion matrix :  [[12  0  7  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0 18  0  2  0  0  0  0  0  0  0  0  0  0  0  1  0  0  0  0  0  0]\n",
      " [ 6  0 15  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  2]\n",
      " [ 0  0  0 17  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0 27  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 2  0  0  0  0 20  0  0  0  0  0  0  0  1  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0 14  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0 23  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0 16  1  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0 19  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0 14  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0 19  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0 21  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0 23  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0 10  0  0  1  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  2 15  0  3  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  2 17  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  1  0  0  1  2  2 17  1  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  1  0  0  0  2  0  0 16  4  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  1  4 15  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 26  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  1  0  0  0  0  0  0  0  0  0 16]]\n",
      "==========================================================\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.tree import ExtraTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.metrics import accuracy_score,confusion_matrix\n",
    "\n",
    "# create instances of all models\n",
    "models = {\n",
    "    'Logistic Regression': LogisticRegression(),\n",
    "    'Naive Bayes': GaussianNB(),\n",
    "    'Support Vector Machine': SVC(),\n",
    "    'K-Nearest Neighbors': KNeighborsClassifier(),\n",
    "    'Decision Tree': DecisionTreeClassifier(),\n",
    "    'Random Forest': RandomForestClassifier(),\n",
    "    'Bagging': BaggingClassifier(),\n",
    "    'AdaBoost': AdaBoostClassifier(),\n",
    "    'Gradient Boosting': GradientBoostingClassifier(),\n",
    "    'Extra Trees': ExtraTreeClassifier(),\n",
    "}\n",
    "\n",
    "\n",
    "for name, model in models.items():\n",
    "    model.fit(X_train,y_train)\n",
    "    ypred = model.predict(X_test)\n",
    "    \n",
    "    print(f\"{name}  with accuracy : {accuracy_score(y_test,ypred)}\")\n",
    "    print(\"Confusion matrix : \",confusion_matrix(y_test,ypred))\n",
    "    print(\"==========================================================\")\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "4659be4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9931818181818182"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# finally selected randomforest model\n",
    "\n",
    "rfc = RandomForestClassifier()\n",
    "rfc.fit(X_train,y_train)\n",
    "ypred = rfc.predict(X_test)\n",
    "accuracy_score(y_test,ypred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "3e72d7f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9954545454545455"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# or gausianjb\n",
    "gnb = GaussianNB()\n",
    "gnb.fit(X_train,y_train)\n",
    "ypred = gnb.predict(X_test)\n",
    "accuracy_score(y_test,ypred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "859d9922",
   "metadata": {},
   "source": [
    "# Predictive System"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "17f3a3fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "def recommendation(N,P,k,temperature,humidity,ph,rainfal):\n",
    "    features = np.array([[N,P,k,temperature,humidity,ph,rainfal]])\n",
    "    transformed_features = ms.fit_transform(features)\n",
    "    prediction = rfc.predict(transformed_features)\n",
    "    print(prediction)\n",
    "    return prediction[0] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "64ffd9d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[9]\n",
      "Muskmelon is a best crop to be cultivated \n"
     ]
    }
   ],
   "source": [
    "# new inputs\n",
    "\n",
    "\n",
    "N = 40\n",
    "P = 50\n",
    "k = 50\n",
    "temperature = 40.0\n",
    "humidity = 20\n",
    "ph = 100\n",
    "rainfall = 100\n",
    "\n",
    "predict = recommendation(N,P,k,temperature,humidity,ph,rainfall)\n",
    "\n",
    "crop_dict = {1: \"Rice\", 2: \"Maize\", 3: \"Jute\", 4: \"Cotton\", 5: \"Coconut\", 6: \"Papaya\", 7: \"Orange\",\n",
    "                 8: \"Apple\", 9: \"Muskmelon\", 10: \"Watermelon\", 11: \"Grapes\", 12: \"Mango\", 13: \"Banana\",\n",
    "                 14: \"Pomegranate\", 15: \"Lentil\", 16: \"Blackgram\", 17: \"Mungbean\", 18: \"Mothbeans\",\n",
    "                 19: \"Pigeonpeas\", 20: \"Kidneybeans\", 21: \"Chickpea\", 22: \"Coffee\"}\n",
    "\n",
    "if predict in crop_dict:\n",
    "    crop = crop_dict[predict]\n",
    "    print(\"{} is a best crop to be cultivated \".format(crop))\n",
    "else:\n",
    "    print(\"Sorry are not able to recommend a proper crop for this environment\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "2ea8ffda",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[9]\n",
      "Muskmelon is a best crop to be cultivated \n"
     ]
    }
   ],
   "source": [
    "# new inputs 2\n",
    "\n",
    "\n",
    "N = 100\n",
    "P = 90\n",
    "k = 100\n",
    "temperature = 50.0\n",
    "humidity = 90.0\n",
    "ph = 100\n",
    "rainfall = 202.0\n",
    "\n",
    "predict = recommendation(N,P,k,temperature,humidity,ph,rainfall)\n",
    "\n",
    "crop_dict = {1: \"Rice\", 2: \"Maize\", 3: \"Jute\", 4: \"Cotton\", 5: \"Coconut\", 6: \"Papaya\", 7: \"Orange\",\n",
    "                 8: \"Apple\", 9: \"Muskmelon\", 10: \"Watermelon\", 11: \"Grapes\", 12: \"Mango\", 13: \"Banana\",\n",
    "                 14: \"Pomegranate\", 15: \"Lentil\", 16: \"Blackgram\", 17: \"Mungbean\", 18: \"Mothbeans\",\n",
    "                 19: \"Pigeonpeas\", 20: \"Kidneybeans\", 21: \"Chickpea\", 22: \"Coffee\"}\n",
    "\n",
    "if predict in crop_dict:\n",
    "    crop = crop_dict[predict]\n",
    "    print(\"{} is a best crop to be cultivated \".format(crop))\n",
    "else:\n",
    "    print(\"Sorry are not able to recommend a proper crop for this environment\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "d0dccd4e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[9]\n",
      "Muskmelon is a best crop to be cultivated \n"
     ]
    }
   ],
   "source": [
    "# new inputs 2\n",
    "N = 10\n",
    "P = 10\n",
    "k = 10\n",
    "temperature = 15.0\n",
    "humidity = 80.0\n",
    "ph = 4.5\n",
    "rainfall = 10.0\n",
    "\n",
    "predict = recommendation(N,P,k,temperature,humidity,ph,rainfall)\n",
    "\n",
    "crop_dict = {1: \"Rice\", 2: \"Maize\", 3: \"Jute\", 4: \"Cotton\", 5: \"Coconut\", 6: \"Papaya\", 7: \"Orange\",\n",
    "                 8: \"Apple\", 9: \"Muskmelon\", 10: \"Watermelon\", 11: \"Grapes\", 12: \"Mango\", 13: \"Banana\",\n",
    "                 14: \"Pomegranate\", 15: \"Lentil\", 16: \"Blackgram\", 17: \"Mungbean\", 18: \"Mothbeans\",\n",
    "                 19: \"Pigeonpeas\", 20: \"Kidneybeans\", 21: \"Chickpea\", 22: \"Coffee\"}\n",
    "\n",
    "if predict in crop_dict:\n",
    "    crop = crop_dict[predict]\n",
    "    print(\"{} is a best crop to be cultivated \".format(crop))\n",
    "else:\n",
    "    print(\"Sorry are not able to recommend a proper crop for this environment\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "fa3d3b8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "pickle.dump(rfc,open('model.pkl','wb'))\n",
    "pickle.dump(ms,open('minmaxscaler.pkl','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a55a48a3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c97733fc",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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