HarvestO-Notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing the required libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data-sets/crop_recommendation.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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",
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       "  </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",
       "    <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",
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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",
       "      <td>coffee</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",
       "    </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",
       "      <td>coffee</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",
       "    </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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2200 rows × 8 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",
       "[2200 rows x 8 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2200, 8)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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",
       "    </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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['rice', 'maize', 'chickpea', 'kidneybeans', 'pigeonpeas',\n",
       "       'mothbeans', 'mungbean', 'blackgram', 'lentil', 'pomegranate',\n",
       "       'banana', 'mango', 'grapes', 'watermelon', 'muskmelon', 'apple',\n",
       "       'orange', 'papaya', 'coconut', 'cotton', 'jute', 'coffee'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['label'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "blackgram      100\n",
       "pomegranate    100\n",
       "papaya         100\n",
       "muskmelon      100\n",
       "rice           100\n",
       "cotton         100\n",
       "orange         100\n",
       "watermelon     100\n",
       "coffee         100\n",
       "chickpea       100\n",
       "kidneybeans    100\n",
       "mothbeans      100\n",
       "pigeonpeas     100\n",
       "coconut        100\n",
       "jute           100\n",
       "mango          100\n",
       "mungbean       100\n",
       "grapes         100\n",
       "lentil         100\n",
       "maize          100\n",
       "apple          100\n",
       "banana         100\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['label'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting the data-set into dependent and independent variables/features:\n",
    "### After that, we divide the datasets into train and test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop('label', axis=1)\n",
    "y = df['label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['N', 'P', 'K', 'temperature', 'humidity', 'ph', 'rainfall'], dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         rice\n",
       "1         rice\n",
       "2         rice\n",
       "3         rice\n",
       "4         rice\n",
       "         ...  \n",
       "2195    coffee\n",
       "2196    coffee\n",
       "2197    coffee\n",
       "2198    coffee\n",
       "2199    coffee\n",
       "Name: label, Length: 2200, dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=101)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now, we will apply different ML algorithms to find out the best accuracy-yielding algorithm for our application:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating empty lists of accuracy and models to compare.\n",
    "accuracy = []\n",
    "modelName = []"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "       apple       1.00      1.00      1.00        37\n",
      "      banana       1.00      1.00      1.00        32\n",
      "   blackgram       0.91      0.86      0.89        36\n",
      "    chickpea       1.00      1.00      1.00        32\n",
      "     coconut       1.00      1.00      1.00        23\n",
      "      coffee       0.97      1.00      0.98        31\n",
      "      cotton       0.85      0.97      0.90        29\n",
      "      grapes       1.00      1.00      1.00        41\n",
      "        jute       0.85      0.85      0.85        27\n",
      " kidneybeans       1.00      1.00      1.00        30\n",
      "      lentil       1.00      1.00      1.00        29\n",
      "       maize       0.96      0.79      0.87        34\n",
      "       mango       1.00      1.00      1.00        33\n",
      "   mothbeans       0.81      0.88      0.85        25\n",
      "    mungbean       1.00      1.00      1.00        28\n",
      "   muskmelon       1.00      1.00      1.00        26\n",
      "      orange       1.00      1.00      1.00        29\n",
      "      papaya       1.00      0.92      0.96        24\n",
      "  pigeonpeas       0.93      1.00      0.96        25\n",
      " pomegranate       1.00      1.00      1.00        24\n",
      "        rice       0.88      0.91      0.89        32\n",
      "  watermelon       1.00      1.00      1.00        33\n",
      "\n",
      "    accuracy                           0.96       660\n",
      "   macro avg       0.96      0.96      0.96       660\n",
      "weighted avg       0.96      0.96      0.96       660\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jackson/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:762: 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",
      "/home/jackson/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:762: 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"
     ]
    }
   ],
   "source": [
    "logisticReg = LogisticRegression()\n",
    "logisticReg.fit(X_train, y_train)\n",
    "\n",
    "# Making predictions and printing metrics:\n",
    "logisticReg.fit(X_train, y_train)\n",
    "predLogistic = logisticReg.predict(X_test)\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
    "accLR = accuracy_score(y_test, predLogistic)\n",
    "accuracy.append(accLR)\n",
    "modelName.append(\"Logistic Regression\")\n",
    "print(classification_report(y_test, predLogistic))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "       apple       1.00      1.00      1.00        37\n",
      "      banana       1.00      1.00      1.00        32\n",
      "   blackgram       0.69      0.94      0.80        36\n",
      "    chickpea       1.00      1.00      1.00        32\n",
      "     coconut       1.00      1.00      1.00        23\n",
      "      coffee       1.00      0.87      0.93        31\n",
      "      cotton       0.97      1.00      0.98        29\n",
      "      grapes       1.00      1.00      1.00        41\n",
      "        jute       1.00      0.33      0.50        27\n",
      " kidneybeans       1.00      0.73      0.85        30\n",
      "      lentil       0.88      1.00      0.94        29\n",
      "       maize       0.89      0.97      0.93        34\n",
      "       mango       1.00      1.00      1.00        33\n",
      "   mothbeans       1.00      0.56      0.72        25\n",
      "    mungbean       1.00      1.00      1.00        28\n",
      "   muskmelon       0.96      1.00      0.98        26\n",
      "      orange       1.00      1.00      1.00        29\n",
      "      papaya       1.00      1.00      1.00        24\n",
      "  pigeonpeas       0.93      1.00      0.96        25\n",
      " pomegranate       1.00      1.00      1.00        24\n",
      "        rice       0.64      1.00      0.78        32\n",
      "  watermelon       1.00      0.97      0.98        33\n",
      "\n",
      "    accuracy                           0.93       660\n",
      "   macro avg       0.95      0.93      0.93       660\n",
      "weighted avg       0.95      0.93      0.93       660\n",
      "\n"
     ]
    }
   ],
   "source": [
    "dTree = DecisionTreeClassifier(criterion=\"entropy\", max_depth=5)\n",
    "dTree.fit(X_train, y_train)\n",
    "predDT = dTree.predict(X_test)\n",
    "accDT = accuracy_score(y_test, predDT)\n",
    "accuracy.append(accDT)\n",
    "modelName.append(\"Decision Trees\")\n",
    "# Printing the metrics\n",
    "print(classification_report(y_test, predDT))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Support Vector Machine(SVM):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "       apple       1.00      1.00      1.00        37\n",
      "      banana       1.00      1.00      1.00        32\n",
      "   blackgram       1.00      1.00      1.00        36\n",
      "    chickpea       1.00      1.00      1.00        32\n",
      "     coconut       1.00      1.00      1.00        23\n",
      "      coffee       1.00      1.00      1.00        31\n",
      "      cotton       0.91      1.00      0.95        29\n",
      "      grapes       1.00      1.00      1.00        41\n",
      "        jute       0.79      1.00      0.89        27\n",
      " kidneybeans       0.97      1.00      0.98        30\n",
      "      lentil       0.97      0.97      0.97        29\n",
      "       maize       1.00      0.91      0.95        34\n",
      "       mango       1.00      1.00      1.00        33\n",
      "   mothbeans       0.96      0.96      0.96        25\n",
      "    mungbean       1.00      1.00      1.00        28\n",
      "   muskmelon       1.00      1.00      1.00        26\n",
      "      orange       1.00      1.00      1.00        29\n",
      "      papaya       1.00      0.96      0.98        24\n",
      "  pigeonpeas       1.00      0.96      0.98        25\n",
      " pomegranate       1.00      1.00      1.00        24\n",
      "        rice       0.96      0.78      0.86        32\n",
      "  watermelon       1.00      1.00      1.00        33\n",
      "\n",
      "    accuracy                           0.98       660\n",
      "   macro avg       0.98      0.98      0.98       660\n",
      "weighted avg       0.98      0.98      0.98       660\n",
      "\n"
     ]
    }
   ],
   "source": [
    "SVMModel = SVC()\n",
    "SVMModel.fit(X_train, y_train)\n",
    "predSVM = SVMModel.predict(X_test)\n",
    "accSVM = accuracy_score(y_test, predSVM)\n",
    "accuracy.append(accSVM)\n",
    "modelName.append(\"SVM\")\n",
    "# Printing the metrics\n",
    "print(classification_report(y_test, predSVM))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "       apple       1.00      1.00      1.00        37\n",
      "      banana       1.00      1.00      1.00        32\n",
      "   blackgram       1.00      1.00      1.00        36\n",
      "    chickpea       1.00      1.00      1.00        32\n",
      "     coconut       1.00      1.00      1.00        23\n",
      "      coffee       1.00      1.00      1.00        31\n",
      "      cotton       1.00      1.00      1.00        29\n",
      "      grapes       1.00      1.00      1.00        41\n",
      "        jute       0.96      0.96      0.96        27\n",
      " kidneybeans       1.00      1.00      1.00        30\n",
      "      lentil       1.00      1.00      1.00        29\n",
      "       maize       1.00      1.00      1.00        34\n",
      "       mango       1.00      1.00      1.00        33\n",
      "   mothbeans       1.00      1.00      1.00        25\n",
      "    mungbean       1.00      1.00      1.00        28\n",
      "   muskmelon       1.00      1.00      1.00        26\n",
      "      orange       1.00      1.00      1.00        29\n",
      "      papaya       1.00      1.00      1.00        24\n",
      "  pigeonpeas       1.00      1.00      1.00        25\n",
      " pomegranate       1.00      1.00      1.00        24\n",
      "        rice       0.97      0.97      0.97        32\n",
      "  watermelon       1.00      1.00      1.00        33\n",
      "\n",
      "    accuracy                           1.00       660\n",
      "   macro avg       1.00      1.00      1.00       660\n",
      "weighted avg       1.00      1.00      1.00       660\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "RFModel = RandomForestClassifier(n_estimators=20)\n",
    "RFModel.fit(X_train, y_train)\n",
    "predRF = RFModel.predict(X_test)\n",
    "accRF = accuracy_score(y_test, predRF)\n",
    "accuracy.append(accRF)\n",
    "modelName.append(\"Random Forest\")\n",
    "# Printing the metrics\n",
    "print(classification_report(y_test, predRF))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now I will visualize the accuracies of different models and choose the one that's the best:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f0630b532e0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12, 8))\n",
    "plt.title(\"Comparision of Accuracy\")\n",
    "plt.xlabel(\"Accuracy\")\n",
    "plt.ylabel(\"Algorithm\")\n",
    "sns.barplot(accuracy, modelName)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Since the accuracy of Random Forest is the maximum(100%, I KNOW!!), we choose RFModel as our web application's model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['rice']\n"
     ]
    }
   ],
   "source": [
    "# Making a prediction:\n",
    "testData = X.iloc[3]\n",
    "testPrediction = RFModel.predict([testData])\n",
    "print(testPrediction)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Saving the model using Pickle library:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle_out = open(\"RFmodel.pkl\", \"wb\")\n",
    "pickle.dump(RFModel, pickle_out)\n",
    "pickle_out.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}