train_model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "924db23d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing libraries\n",
    "\n",
    "# from __future__ import print_function\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn import metrics\n",
    "import seaborn as sns\n",
    "from sklearn import tree\n",
    "import warnings\n",
    "import matplotlib.pyplot as plt\n",
    "# warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2e62dfbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "d = pd.read_csv('Data/crop_recommendation.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "64ea12fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": [
    "d.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6b2ad6fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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>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",
       "</div>"
      ],
      "text/plain": [
       "        N   P   K  temperature   humidity        ph    rainfall   label\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"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "38d6052f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17600"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5919201a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2200, 8)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f45b4ccb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['N', 'P', 'K', 'temperature', 'humidity', 'ph', 'rainfall', 'label'], dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e0d6e3ea",
   "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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d['label'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "46797214",
   "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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fe2bce8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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: label, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d['label'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "68c9706e",
   "metadata": {},
   "outputs": [],
   "source": [
    "features = d[['N', 'P','K','temperature', 'humidity', 'ph', 'rainfall']]\n",
    "target = d['label']\n",
    "labels = d['label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "dff9f9b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialzing empty lists to append all model's name and corresponding name\n",
    "acc = []\n",
    "model = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "296de395",
   "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": "code",
   "execution_count": 14,
   "id": "f2e10d8c",
   "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"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1248: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1248: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1248: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\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": 15,
   "id": "41c5e07e",
   "metadata": {},
   "outputs": [],
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b281792b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cross validation score (Decision Tree)\n",
    "score = cross_val_score(DecisionTree, features, target,cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8de3831e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.93636364, 0.90909091, 0.91818182, 0.87045455, 0.93636364])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c15c124a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "# Dump the trained Naive Bayes classifier with Pickle\n",
    "DT_pkl_filename = 'model/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": "code",
   "execution_count": 19,
   "id": "cb8e81f1",
   "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": 20,
   "id": "11b44490",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.99772727, 0.99545455, 0.99545455, 0.99545455, 0.99090909])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross validation score (NaiveBayes)\n",
    "score = cross_val_score(NaiveBayes,features,target,cv=5)\n",
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e32de473",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "# Dump the trained Naive Bayes classifier with Pickle\n",
    "NB_pkl_filename = 'model/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": "code",
   "execution_count": 22,
   "id": "b858c5e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM's Accuracy is:  0.9795454545454545\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      0.95      0.98        22\n",
      "      cotton       0.95      1.00      0.98        20\n",
      "      grapes       1.00      1.00      1.00        18\n",
      "        jute       0.83      0.89      0.86        28\n",
      " kidneybeans       1.00      1.00      1.00        14\n",
      "      lentil       1.00      1.00      1.00        23\n",
      "       maize       1.00      0.95      0.98        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       0.80      0.75      0.77        16\n",
      "  watermelon       1.00      1.00      1.00        15\n",
      "\n",
      "    accuracy                           0.98       440\n",
      "   macro avg       0.98      0.98      0.98       440\n",
      "weighted avg       0.98      0.98      0.98       440\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "# data normalization with sklearn\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "# fit scaler on training data\n",
    "norm = MinMaxScaler().fit(Xtrain)\n",
    "X_train_norm = norm.transform(Xtrain)\n",
    "# transform testing dataabs\n",
    "X_test_norm = norm.transform(Xtest)\n",
    "SVM = SVC(kernel='poly', degree=3, C=1)\n",
    "SVM.fit(X_train_norm,Ytrain)\n",
    "predicted_values = SVM.predict(X_test_norm)\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": 23,
   "id": "47a0ee5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.97954545, 0.975     , 0.98863636, 0.98863636, 0.98181818])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross validation score (SVM)\n",
    "score = cross_val_score(SVM,features,target,cv=5)\n",
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4bcafd09",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "# Dump the trained SVM classifier with Pickle\n",
    "SVM_pkl_filename = 'model/SVMClassifier.pkl'\n",
    "# Open the file to save as pkl file\n",
    "SVM_Model_pkl = open(SVM_pkl_filename, 'wb')\n",
    "pickle.dump(SVM, SVM_Model_pkl)\n",
    "# Close the pickle instances\n",
    "SVM_Model_pkl.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c995b307",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression's Accuracy is:  0.9522727272727273\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       0.86      0.75      0.80        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       0.86      0.90      0.88        20\n",
      "      grapes       1.00      1.00      1.00        18\n",
      "        jute       0.84      0.93      0.88        28\n",
      " kidneybeans       1.00      1.00      1.00        14\n",
      "      lentil       0.88      1.00      0.94        23\n",
      "       maize       0.90      0.86      0.88        21\n",
      "       mango       0.96      1.00      0.98        26\n",
      "   mothbeans       0.84      0.84      0.84        19\n",
      "    mungbean       1.00      0.96      0.98        24\n",
      "   muskmelon       1.00      1.00      1.00        23\n",
      "      orange       1.00      1.00      1.00        29\n",
      "      papaya       1.00      0.95      0.97        19\n",
      "  pigeonpeas       1.00      1.00      1.00        18\n",
      " pomegranate       1.00      1.00      1.00        17\n",
      "        rice       0.85      0.69      0.76        16\n",
      "  watermelon       1.00      1.00      1.00        15\n",
      "\n",
      "    accuracy                           0.95       440\n",
      "   macro avg       0.95      0.95      0.95       440\n",
      "weighted avg       0.95      0.95      0.95       440\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "LogReg = LogisticRegression(random_state=2)\n",
    "\n",
    "LogReg.fit(Xtrain,Ytrain)\n",
    "\n",
    "predicted_values = LogReg.predict(Xtest)\n",
    "\n",
    "x = metrics.accuracy_score(Ytest, predicted_values)\n",
    "acc.append(x)\n",
    "model.append('Logistic Regression')\n",
    "print(\"Logistic Regression's Accuracy is: \", x)\n",
    "\n",
    "print(classification_report(Ytest,predicted_values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d93b3617",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0.95      , 0.96590909, 0.94772727, 0.96818182, 0.94318182])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross validation score (Logistic Regression)\n",
    "score = cross_val_score(LogReg,features,target,cv=5)\n",
    "score\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "371ace33",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "# Dump the trained Naive Bayes classifier with Pickle\n",
    "LR_pkl_filename = 'model/LogisticRegression.pkl'\n",
    "# Open the file to save as pkl file\n",
    "LR_Model_pkl = open(DT_pkl_filename, 'wb')\n",
    "pickle.dump(LogReg, LR_Model_pkl)\n",
    "# Close the pickle instances\n",
    "LR_Model_pkl.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "fc13ea21",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RF'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       0.94      1.00      0.97        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       0.90      1.00      0.95        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      0.95      0.97        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      0.81      0.90        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.ensemble import RandomForestClassifier\n",
    "\n",
    "RF = RandomForestClassifier(n_estimators=20, random_state=0)\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": 29,
   "id": "2933dec7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.99772727, 0.99545455, 0.99772727, 0.99318182, 0.98863636])"
      ]
     },
     "execution_count": 29,
     "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": "code",
   "execution_count": 30,
   "id": "751d6907",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "# Dump the trained Naive Bayes classifier with Pickle\n",
    "RF_pkl_filename = 'model/RandomForest.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": "code",
   "execution_count": 31,
   "id": "0a186f20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Accuracy Comparison'}, xlabel='Accuracy', ylabel='Algorithm'>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": 32,
   "id": "e593c223",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree --> 0.9\n",
      "Naive Bayes --> 0.990909090909091\n",
      "SVM --> 0.9795454545454545\n",
      "Logistic Regression --> 0.9522727272727273\n",
      "RF --> 0.990909090909091\n"
     ]
    }
   ],
   "source": [
    "accuracy_models = dict(zip(model, acc))\n",
    "for k, v in accuracy_models.items():\n",
    "    print (k, '-->', v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "6197126a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['coffee']\n"
     ]
    }
   ],
   "source": [
    "data = np.array([[80,20, 40, 23.603016, 60.3, 6.7, 140.91]])\n",
    "prediction = RF.predict(data)\n",
    "print(prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d9f43b2b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}