Crop Recomnedation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9017ff4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d71e9f70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>N</th>\n",
       "      <th>P</th>\n",
       "      <th>K</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "      <th>ph</th>\n",
       "      <th>rainfall</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>90</td>\n",
       "      <td>42</td>\n",
       "      <td>43</td>\n",
       "      <td>20.879744</td>\n",
       "      <td>82.002744</td>\n",
       "      <td>6.502985</td>\n",
       "      <td>202.935536</td>\n",
       "      <td>rice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>85</td>\n",
       "      <td>58</td>\n",
       "      <td>41</td>\n",
       "      <td>21.770462</td>\n",
       "      <td>80.319644</td>\n",
       "      <td>7.038096</td>\n",
       "      <td>226.655537</td>\n",
       "      <td>rice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60</td>\n",
       "      <td>55</td>\n",
       "      <td>44</td>\n",
       "      <td>23.004459</td>\n",
       "      <td>82.320763</td>\n",
       "      <td>7.840207</td>\n",
       "      <td>263.964248</td>\n",
       "      <td>rice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>74</td>\n",
       "      <td>35</td>\n",
       "      <td>40</td>\n",
       "      <td>26.491096</td>\n",
       "      <td>80.158363</td>\n",
       "      <td>6.980401</td>\n",
       "      <td>242.864034</td>\n",
       "      <td>rice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>78</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>20.130175</td>\n",
       "      <td>81.604873</td>\n",
       "      <td>7.628473</td>\n",
       "      <td>262.717340</td>\n",
       "      <td>rice</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    N   P   K  temperature   humidity        ph    rainfall label\n",
       "0  90  42  43    20.879744  82.002744  6.502985  202.935536  rice\n",
       "1  85  58  41    21.770462  80.319644  7.038096  226.655537  rice\n",
       "2  60  55  44    23.004459  82.320763  7.840207  263.964248  rice\n",
       "3  74  35  40    26.491096  80.158363  6.980401  242.864034  rice\n",
       "4  78  42  42    20.130175  81.604873  7.628473  262.717340  rice"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('crop_recommendation.csv')\n",
    "data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f5259c73",
   "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['label'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "545fd5fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17600"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4d8d3d22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2200, 8)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "535469d7",
   "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3a79a1b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "muskmelon      100\n",
       "jute           100\n",
       "grapes         100\n",
       "maize          100\n",
       "orange         100\n",
       "coffee         100\n",
       "watermelon     100\n",
       "kidneybeans    100\n",
       "pigeonpeas     100\n",
       "mungbean       100\n",
       "coconut        100\n",
       "pomegranate    100\n",
       "rice           100\n",
       "banana         100\n",
       "apple          100\n",
       "cotton         100\n",
       "chickpea       100\n",
       "mango          100\n",
       "blackgram      100\n",
       "mothbeans      100\n",
       "lentil         100\n",
       "papaya         100\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['label'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5c3314bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(data.corr(),annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f17c016b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#separting features and label columns\n",
    "features = data[['N', 'P','K','temperature', 'humidity', 'ph', 'rainfall']]\n",
    "target = data['label']\n",
    "label = data['label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c93cc894",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialzing empty lists to append all model's name and corresponding name\n",
    "acc = []\n",
    "model = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4df2667c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#splitting dataset\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": "df71ae62",
   "metadata": {},
   "source": [
    "#Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8994f93c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Decison tree model: 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",
    "from sklearn.metrics import classification_report\n",
    "from sklearn import metrics\n",
    "DTM = DecisionTreeClassifier(criterion='entropy',random_state=2,max_depth=5)\n",
    "DTM.fit(xtrain,ytrain)\n",
    "dtm_pred = DTM.predict(xtest)\n",
    "x = metrics.accuracy_score(ytest,dtm_pred)\n",
    "acc.append(x)\n",
    "model.append('DTM')\n",
    "print(\"Accuracy of Decison tree model:\",x*100)\n",
    "print(classification_report(ytest,dtm_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4abf4828",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decison tree model score: [0.93636364 0.90909091 0.91818182 0.87045455 0.93636364]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "dtm_score = cross_val_score(DTM,features,target,cv=5)\n",
    "print(\"Decison tree model score:\",dtm_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2cab8624",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "# Creating a pickle file for the classifier\n",
    "filename = 'DTM-model.pkl'\n",
    "pickle.dump(DTM, open(filename, 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f9caa640",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of GaussianNB: 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": [
    "#Gausian NB\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "GNB = GaussianNB()\n",
    "GNB.fit(xtrain,ytrain)\n",
    "predicted_values = GNB.predict(xtest)\n",
    "y = metrics.accuracy_score(ytest,predicted_values)\n",
    "acc.append(y)\n",
    "model.append('GaussianNB')\n",
    "print(\"Accuracy of GaussianNB:\",y)\n",
    "print(classification_report(ytest,predicted_values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "bb8e8090",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.99772727, 0.99545455, 0.99545455, 0.99545455, 0.99090909])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross validation score (NaiveBayes)\n",
    "score = cross_val_score(GNB,features,target,cv=5)\n",
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "61a9cee5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a pickle file for the classifier\n",
    "filename = 'GNB-model.pkl'\n",
    "pickle.dump(GNB, open(filename, 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "395a53c2",
   "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": [
    "#Support vector Machine\n",
    "from sklearn.svm import SVC\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": 22,
   "id": "91213bc5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.97954545, 0.975     , 0.98863636, 0.98863636, 0.98181818])"
      ]
     },
     "execution_count": 22,
     "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": 23,
   "id": "508c8638",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a pickle file for the classifier\n",
    "filename = 'SVM-model.pkl'\n",
    "pickle.dump(SVM, open(filename, 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "88d02330",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the model: 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       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": [
    "#Logistic Regression\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "LR = LogisticRegression(random_state=2)\n",
    "LR.fit(xtrain,ytrain)\n",
    "pred_values = LR.predict(xtest)\n",
    "z = metrics.accuracy_score(ytest,pred_values)\n",
    "acc.append(x)\n",
    "model.append('Logistic Regression')\n",
    "print(\"Accuracy of the model:\",z)\n",
    "print(classification_report(ytest,predicted_values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b5e57048",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.95      , 0.96590909, 0.94772727, 0.96590909, 0.94318182])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#cross validation score\n",
    "score = cross_val_score(LR,features,target,cv=5)\n",
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d8296486",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a pickle file for the classifier\n",
    "filename = 'LR-model.pkl'\n",
    "pickle.dump(LR, open(filename, 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "bbc2796e",
   "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": [
    "#Random Forest\n",
    "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": "4f46fc22",
   "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": "f147497a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a pickle file for the classifier\n",
    "filename = 'RF-model.pkl'\n",
    "pickle.dump(RF, open(filename, 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "99119007",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[17:17:06] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "XGBoost's Accuracy is:  0.9931818181818182\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       0.96      1.00      0.98        22\n",
      "      cotton       1.00      1.00      1.00        20\n",
      "      grapes       1.00      1.00      1.00        18\n",
      "        jute       1.00      0.93      0.96        28\n",
      " kidneybeans       1.00      1.00      1.00        14\n",
      "      lentil       0.96      1.00      0.98        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       0.94      1.00      0.97        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": [
    "#XGBoost\n",
    "import xgboost as xgb\n",
    "XB = xgb.XGBClassifier()\n",
    "XB.fit(xtrain,ytrain)\n",
    "\n",
    "predicted_values = XB.predict(xtest)\n",
    "\n",
    "x = metrics.accuracy_score(ytest, predicted_values)\n",
    "acc.append(x)\n",
    "model.append('XGBoost')\n",
    "print(\"XGBoost's Accuracy is: \", x)\n",
    "\n",
    "print(classification_report(ytest,predicted_values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "0986255d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[17:17:30] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[17:17:30] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[17:17:31] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[17:17:31] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[17:17:32] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0.99318182, 0.99318182, 0.99318182, 0.99090909, 0.99090909])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross validation score (XGBoost)\n",
    "score = cross_val_score(XB,features,target,cv=5)\n",
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "2bb471d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a pickle file for the classifier\n",
    "filename = 'XGB-model.pkl'\n",
    "pickle.dump(XB, open(filename, 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c547cbad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Accuracy Comparison'}, xlabel='Accuracy', ylabel='Algorithm'>"
      ]
     },
     "execution_count": 34,
     "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": [
    "#accuracy comparison\n",
    "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "cce159b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DTM --> 0.9\n",
      "GaussianNB --> 0.990909090909091\n",
      "SVM --> 0.9795454545454545\n",
      "Logistic Regression --> 0.9795454545454545\n",
      "RF --> 0.990909090909091\n",
      "XGBoost --> 0.9931818181818182\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": 36,
   "id": "3a4cbe81",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['coffee']\n"
     ]
    }
   ],
   "source": [
    "#predict\n",
    "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": 37,
   "id": "b8ee98ba",
   "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": null,
   "id": "7aaf72e3",
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
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