Crop_recommendation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0bfd9738",
   "metadata": {},
   "source": [
    "# CROP RECOMMENDATION (DATA ANALYTICS PROJECT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9fa0192b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing libraries\n",
    "\n",
    "from __future__ import print_function\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn import metrics\n",
    "from sklearn import tree\n",
    "from sklearn.metrics import accuracy_score\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5e2810f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d124f330",
   "metadata": {},
   "outputs": [],
   "source": [
    "df= pd.read_csv('Crop_recommendation.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a09ac192",
   "metadata": {},
   "source": [
    "# Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "0637501b",
   "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",
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       "    <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>5</th>\n",
       "      <td>69</td>\n",
       "      <td>37</td>\n",
       "      <td>42</td>\n",
       "      <td>23.058049</td>\n",
       "      <td>83.370118</td>\n",
       "      <td>7.073454</td>\n",
       "      <td>251.055000</td>\n",
       "      <td>rice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>69</td>\n",
       "      <td>55</td>\n",
       "      <td>38</td>\n",
       "      <td>22.708838</td>\n",
       "      <td>82.639414</td>\n",
       "      <td>5.700806</td>\n",
       "      <td>271.324860</td>\n",
       "      <td>rice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>94</td>\n",
       "      <td>53</td>\n",
       "      <td>40</td>\n",
       "      <td>20.277744</td>\n",
       "      <td>82.894086</td>\n",
       "      <td>5.718627</td>\n",
       "      <td>241.974195</td>\n",
       "      <td>rice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>89</td>\n",
       "      <td>54</td>\n",
       "      <td>38</td>\n",
       "      <td>24.515881</td>\n",
       "      <td>83.535216</td>\n",
       "      <td>6.685346</td>\n",
       "      <td>230.446236</td>\n",
       "      <td>rice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>68</td>\n",
       "      <td>58</td>\n",
       "      <td>38</td>\n",
       "      <td>23.223974</td>\n",
       "      <td>83.033227</td>\n",
       "      <td>6.336254</td>\n",
       "      <td>221.209196</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\n",
       "5  69  37  42    23.058049  83.370118  7.073454  251.055000  rice\n",
       "6  69  55  38    22.708838  82.639414  5.700806  271.324860  rice\n",
       "7  94  53  40    20.277744  82.894086  5.718627  241.974195  rice\n",
       "8  89  54  38    24.515881  83.535216  6.685346  230.446236  rice\n",
       "9  68  58  38    23.223974  83.033227  6.336254  221.209196  rice"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "053947a5",
   "metadata": {},
   "source": [
    "This dataset consists of **2200 rows** in total.\n",
    "\n",
    "**Each row has 8 columns representing Nitrogen, Phosphorous, Potassium, Temperature, Humidity, PH, Rainfall and Label**\n",
    "\n",
    "NPK(Nitrogen, Phosphorous and Potassium) values represent the NPK values in the soil. Temperature, humidity and rainfall are the average values of the sorroundings environment respectively. PH is the PH value present in the soil. **The Label column tells us the type of crop that's best suited to grow based on these conditions.**  \n",
    "**Label is the value we will be predicting**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "66634077",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.tail of         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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "911bf901",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th>N</th>\n",
       "      <th>P</th>\n",
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       "      <th>humidity</th>\n",
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       "      <td>57.041511</td>\n",
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       "      <td>23.292503</td>\n",
       "      <td>50.045570</td>\n",
       "      <td>6.020947</td>\n",
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       "      <td>24.914610</td>\n",
       "      <td>53.741447</td>\n",
       "      <td>6.334610</td>\n",
       "      <td>166.254931</td>\n",
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       "      <th>2195</th>\n",
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       "      <td>26.774637</td>\n",
       "      <td>66.413269</td>\n",
       "      <td>6.780064</td>\n",
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       "      <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",
       "      <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",
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       "    <tr>\n",
       "      <th>2198</th>\n",
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       "      <td>32</td>\n",
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       "      <td>26.272418</td>\n",
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      "text/plain": [
       "        N   P   K  temperature   humidity        ph    rainfall   label\n",
       "2190  103  40  30    27.309018  55.196224  6.348316  141.483164  coffee\n",
       "2191  118  31  34    27.548230  62.881792  6.123796  181.417081  coffee\n",
       "2192  106  21  35    25.627355  57.041511  7.428524  188.550654  coffee\n",
       "2193  116  38  34    23.292503  50.045570  6.020947  183.468585  coffee\n",
       "2194   97  35  26    24.914610  53.741447  6.334610  166.254931  coffee\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"
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     "execution_count": 6,
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   "source": [
    "df.tail(10)"
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  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "45fa6175",
   "metadata": {},
   "outputs": [
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     "data": {
      "text/plain": [
       "17600"
      ]
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     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c80a46f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2200, 8)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "08cf031e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['N', 'P', 'K', 'temperature', 'humidity', 'ph', 'rainfall', 'label'], dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "01286977",
   "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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['label'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "44cccb30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "N                int64\n",
       "P                int64\n",
       "K                int64\n",
       "temperature    float64\n",
       "humidity       float64\n",
       "ph             float64\n",
       "rainfall       float64\n",
       "label           object\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8eea1bcd",
   "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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['label'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e979d756",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "numeric_df = df.select_dtypes(include='number')  # Select numeric columns only\n",
    "sns.heatmap(numeric_df.corr(), annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "91b2b4cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "features = df[['N', 'P','K','temperature', 'humidity', 'ph', 'rainfall']]\n",
    "target = df['label']\n",
    "labels = df['label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4e479b45",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initializing empty lists to append all model's name and corresponding name\n",
    "acc = []\n",
    "model = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "240f2d46",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting into train and test data\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "Xtrain, Xtest, Ytrain, Ytest = train_test_split(features,target,test_size = 0.2,random_state =2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24690d0b",
   "metadata": {},
   "source": [
    "# 1. Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e5a2d32c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTrees's Accuracy is:  90.0\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       apple       1.00      1.00      1.00        13\n",
      "      banana       1.00      1.00      1.00        17\n",
      "   blackgram       0.59      1.00      0.74        16\n",
      "    chickpea       1.00      1.00      1.00        21\n",
      "     coconut       0.91      1.00      0.95        21\n",
      "      coffee       1.00      1.00      1.00        22\n",
      "      cotton       1.00      1.00      1.00        20\n",
      "      grapes       1.00      1.00      1.00        18\n",
      "        jute       0.74      0.93      0.83        28\n",
      " kidneybeans       0.00      0.00      0.00        14\n",
      "      lentil       0.68      1.00      0.81        23\n",
      "       maize       1.00      1.00      1.00        21\n",
      "       mango       1.00      1.00      1.00        26\n",
      "   mothbeans       0.00      0.00      0.00        19\n",
      "    mungbean       1.00      1.00      1.00        24\n",
      "   muskmelon       1.00      1.00      1.00        23\n",
      "      orange       1.00      1.00      1.00        29\n",
      "      papaya       1.00      0.84      0.91        19\n",
      "  pigeonpeas       0.62      1.00      0.77        18\n",
      " pomegranate       1.00      1.00      1.00        17\n",
      "        rice       1.00      0.62      0.77        16\n",
      "  watermelon       1.00      1.00      1.00        15\n",
      "\n",
      "    accuracy                           0.90       440\n",
      "   macro avg       0.84      0.88      0.85       440\n",
      "weighted avg       0.86      0.90      0.87       440\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "DecisionTree = DecisionTreeClassifier(criterion=\"entropy\",random_state=2,max_depth=5)\n",
    "\n",
    "DecisionTree.fit(Xtrain,Ytrain)\n",
    "\n",
    "predicted_values = DecisionTree.predict(Xtest)\n",
    "x = metrics.accuracy_score(Ytest, predicted_values)\n",
    "acc.append(x)\n",
    "model.append('Decision Tree')\n",
    "print(\"DecisionTrees's Accuracy is: \", x*100)\n",
    "\n",
    "print(classification_report(Ytest,predicted_values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8d83de16",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "553a16f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cross validation score (Decision Tree)\n",
    "score = cross_val_score(DecisionTree, features, target,cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9126c01d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.93636364, 0.90909091, 0.91818182, 0.87045455, 0.93636364])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "372bb597",
   "metadata": {},
   "source": [
    "### Saving trained Decision Tree model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1188f414",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "# Dump the trained Naive Bayes classifier with Pickle\n",
    "DT_pkl_filename = 'DecisionTree.pkl'\n",
    "# Open the file to save as pkl file\n",
    "DT_Model_pkl = open(DT_pkl_filename, 'wb')\n",
    "pickle.dump(DecisionTree, DT_Model_pkl)\n",
    "# Close the pickle instances\n",
    "DT_Model_pkl.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0eb67fd3",
   "metadata": {},
   "source": [
    "# 2.Guassian Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c824a5c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive Bayes's Accuracy is:  0.990909090909091\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       apple       1.00      1.00      1.00        13\n",
      "      banana       1.00      1.00      1.00        17\n",
      "   blackgram       1.00      1.00      1.00        16\n",
      "    chickpea       1.00      1.00      1.00        21\n",
      "     coconut       1.00      1.00      1.00        21\n",
      "      coffee       1.00      1.00      1.00        22\n",
      "      cotton       1.00      1.00      1.00        20\n",
      "      grapes       1.00      1.00      1.00        18\n",
      "        jute       0.88      1.00      0.93        28\n",
      " kidneybeans       1.00      1.00      1.00        14\n",
      "      lentil       1.00      1.00      1.00        23\n",
      "       maize       1.00      1.00      1.00        21\n",
      "       mango       1.00      1.00      1.00        26\n",
      "   mothbeans       1.00      1.00      1.00        19\n",
      "    mungbean       1.00      1.00      1.00        24\n",
      "   muskmelon       1.00      1.00      1.00        23\n",
      "      orange       1.00      1.00      1.00        29\n",
      "      papaya       1.00      1.00      1.00        19\n",
      "  pigeonpeas       1.00      1.00      1.00        18\n",
      " pomegranate       1.00      1.00      1.00        17\n",
      "        rice       1.00      0.75      0.86        16\n",
      "  watermelon       1.00      1.00      1.00        15\n",
      "\n",
      "    accuracy                           0.99       440\n",
      "   macro avg       0.99      0.99      0.99       440\n",
      "weighted avg       0.99      0.99      0.99       440\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "NaiveBayes = GaussianNB()\n",
    "\n",
    "NaiveBayes.fit(Xtrain,Ytrain)\n",
    "\n",
    "predicted_values = NaiveBayes.predict(Xtest)\n",
    "x = metrics.accuracy_score(Ytest, predicted_values)\n",
    "acc.append(x)\n",
    "model.append('Naive Bayes')\n",
    "print(\"Naive Bayes's Accuracy is: \", x)\n",
    "\n",
    "print(classification_report(Ytest,predicted_values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "dcf3ac57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.99772727, 0.99545455, 0.99545455, 0.99545455, 0.99090909])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross validation score (NaiveBayes)\n",
    "score = cross_val_score(NaiveBayes,features,target,cv=5)\n",
    "score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b13fc470",
   "metadata": {},
   "source": [
    "### Saving trained Guassian Naive Bayes model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "65b96af5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "# Dump the trained Naive Bayes classifier with Pickle\n",
    "NB_pkl_filename = 'NBClassifier.pkl'\n",
    "# Open the file to save as pkl file\n",
    "NB_Model_pkl = open(NB_pkl_filename, 'wb')\n",
    "pickle.dump(NaiveBayes, NB_Model_pkl)\n",
    "# Close the pickle instances\n",
    "NB_Model_pkl.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a183b697",
   "metadata": {},
   "source": [
    "# 3.Support Vector Machine (SVM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "13660750",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM's Accuracy is:  0.10681818181818181\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       apple       1.00      0.23      0.38        13\n",
      "      banana       1.00      0.24      0.38        17\n",
      "   blackgram       1.00      0.19      0.32        16\n",
      "    chickpea       1.00      0.05      0.09        21\n",
      "     coconut       1.00      0.05      0.09        21\n",
      "      coffee       0.00      0.00      0.00        22\n",
      "      cotton       1.00      0.05      0.10        20\n",
      "      grapes       1.00      0.06      0.11        18\n",
      "        jute       1.00      0.07      0.13        28\n",
      " kidneybeans       0.03      1.00      0.07        14\n",
      "      lentil       0.00      0.00      0.00        23\n",
      "       maize       0.00      0.00      0.00        21\n",
      "       mango       0.00      0.00      0.00        26\n",
      "   mothbeans       0.00      0.00      0.00        19\n",
      "    mungbean       1.00      0.12      0.22        24\n",
      "   muskmelon       1.00      0.30      0.47        23\n",
      "      orange       1.00      0.03      0.07        29\n",
      "      papaya       1.00      0.05      0.10        19\n",
      "  pigeonpeas       0.00      0.00      0.00        18\n",
      " pomegranate       1.00      0.12      0.21        17\n",
      "        rice       0.50      0.06      0.11        16\n",
      "  watermelon       1.00      0.13      0.24        15\n",
      "\n",
      "    accuracy                           0.11       440\n",
      "   macro avg       0.66      0.13      0.14       440\n",
      "weighted avg       0.66      0.11      0.13       440\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "SVM = SVC(gamma='auto')\n",
    "\n",
    "SVM.fit(Xtrain,Ytrain)\n",
    "\n",
    "predicted_values = SVM.predict(Xtest)\n",
    "\n",
    "x = metrics.accuracy_score(Ytest, predicted_values)\n",
    "acc.append(x)\n",
    "model.append('SVM')\n",
    "print(\"SVM's Accuracy is: \", x)\n",
    "\n",
    "print(classification_report(Ytest,predicted_values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ac47ecee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.27727273, 0.28863636, 0.29090909, 0.275     , 0.26818182])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross validation score (SVM)\n",
    "score = cross_val_score(SVM,features,target,cv=5)\n",
    "score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b27f5df",
   "metadata": {},
   "source": [
    "# 4.Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "20b46acc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RF's Accuracy is:  0.9954545454545455\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       apple       1.00      1.00      1.00        13\n",
      "      banana       1.00      1.00      1.00        17\n",
      "   blackgram       1.00      1.00      1.00        16\n",
      "    chickpea       1.00      1.00      1.00        21\n",
      "     coconut       1.00      1.00      1.00        21\n",
      "      coffee       1.00      1.00      1.00        22\n",
      "      cotton       1.00      1.00      1.00        20\n",
      "      grapes       1.00      1.00      1.00        18\n",
      "        jute       0.93      1.00      0.97        28\n",
      " kidneybeans       1.00      1.00      1.00        14\n",
      "      lentil       1.00      1.00      1.00        23\n",
      "       maize       1.00      1.00      1.00        21\n",
      "       mango       1.00      1.00      1.00        26\n",
      "   mothbeans       1.00      1.00      1.00        19\n",
      "    mungbean       1.00      1.00      1.00        24\n",
      "   muskmelon       1.00      1.00      1.00        23\n",
      "      orange       1.00      1.00      1.00        29\n",
      "      papaya       1.00      1.00      1.00        19\n",
      "  pigeonpeas       1.00      1.00      1.00        18\n",
      " pomegranate       1.00      1.00      1.00        17\n",
      "        rice       1.00      0.88      0.93        16\n",
      "  watermelon       1.00      1.00      1.00        15\n",
      "\n",
      "    accuracy                           1.00       440\n",
      "   macro avg       1.00      0.99      1.00       440\n",
      "weighted avg       1.00      1.00      1.00       440\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "RF = RandomForestClassifier(n_estimators=20, random_state=5)\n",
    "RF.fit(Xtrain,Ytrain)\n",
    "\n",
    "predicted_values = RF.predict(Xtest)\n",
    "\n",
    "x = metrics.accuracy_score(Ytest, predicted_values)\n",
    "acc.append(x)\n",
    "model.append('RF')\n",
    "print(\"RF's Accuracy is: \", x)\n",
    "\n",
    "print(classification_report(Ytest,predicted_values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "61d4f4a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.99545455, 1.        , 0.99545455, 0.99545455, 0.98636364])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross validation score (Random Forest)\n",
    "score = cross_val_score(RF,features,target,cv=5)\n",
    "score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f82d743",
   "metadata": {},
   "source": [
    "### Saving Trained Random Forest Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "fabaa150",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "# Dump the trained Naive Bayes classifier with Pickle\n",
    "RF_pkl_filename = 'RF.pkl'\n",
    "# Open the file to save as pkl file\n",
    "RF_Model_pkl = open(RF_pkl_filename, 'wb')\n",
    "pickle.dump(RF, RF_Model_pkl)\n",
    "# Close the pickle instances\n",
    "RF_Model_pkl.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f41298d4",
   "metadata": {},
   "source": [
    "# ACCURACY COMPARISON"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "02b7773c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Accuracy Comparison'}, xlabel='Accuracy', ylabel='Algorithm'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[10,5],dpi = 100)\n",
    "plt.title('Accuracy Comparison')\n",
    "plt.xlabel('Accuracy')\n",
    "plt.ylabel('Algorithm')\n",
    "sns.barplot(x = acc,y = model,palette='dark')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a426fa7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[10,5], dpi=100)\n",
    "plt.title('Accuracy Comparison')\n",
    "plt.xlabel('Algorithm')\n",
    "plt.ylabel('Accuracy')\n",
    "sns.barplot(x=model, y=acc, palette='dark')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b01f5656",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[10, 5], dpi=100)\n",
    "plt.title('Accuracy Comparison')\n",
    "plt.xlabel('Algorithm')\n",
    "plt.ylabel('Accuracy')\n",
    "sns.barplot(x=model, y=acc, palette='dark')\n",
    "\n",
    "# Add accuracy percentages above each bar\n",
    "for i, accuracy in enumerate(acc):\n",
    "    plt.text(i, accuracy + 0.01, f'{accuracy:.2%}', ha='center')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8ea58a8",
   "metadata": {},
   "source": [
    "# Making a Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "6107aca8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['coffee']\n"
     ]
    }
   ],
   "source": [
    "data = np.array([[104,18, 30, 23.603016, 60.3, 6.7, 140.91]])\n",
    "prediction = RF.predict(data)\n",
    "print(prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "3c507421",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['jute']\n"
     ]
    }
   ],
   "source": [
    "data = np.array([[83, 45, 60, 28, 70.3, 7.0, 150.9]])\n",
    "prediction = RF.predict(data)\n",
    "print(prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0dd44627",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['coffee']\n"
     ]
    }
   ],
   "source": [
    "data = np.array([[104,18, 30, 23.603016, 60.3, 6.7, 140.91]])\n",
    "prediction = RF.predict(data)\n",
    "print(prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "656ae483",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['coffee']\n"
     ]
    }
   ],
   "source": [
    "data = np.array([[101,11, 36, 23.603016, 60.3, 6.1, 140.91]])\n",
    "prediction = RF.predict(data)\n",
    "print(prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d9a732eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['jute']\n"
     ]
    }
   ],
   "source": [
    "data = np.array([[83, 45, 60, 28, 70.3, 7.0, 150.9]])\n",
    "prediction = RF.predict(data)\n",
    "print(prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "bbd7e13a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['jute']\n"
     ]
    }
   ],
   "source": [
    "data = np.array([[83, 45, 60, 28, 70.3, 7.0, 150.9]])\n",
    "prediction = RF.predict(data)\n",
    "print(prediction)"
   ]
  }
 ],
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