Naive Bayes  (comparitive study).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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": [
    "crop = pd.read_csv(r'C:\\Users\\91984\\Downloads\\Crop_recommendation.csv')\n",
    "crop.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = crop[['N', 'P','K','temperature', 'humidity', 'ph', 'rainfall']]\n",
    "target = crop['label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.0687974  -0.34455075 -0.1016875  ...  0.47266646  0.04330173\n",
      "   1.8103605 ]\n",
      " [ 0.93332887  0.14061552 -0.14118477 ...  0.39705125  0.73487256\n",
      "   2.24205791]\n",
      " [ 0.25598625  0.04964684 -0.08193887 ...  0.48695381  1.77151047\n",
      "   2.92106603]\n",
      " ...\n",
      " [ 1.82742114 -0.61745677 -0.35841972 ... -0.19123516 -0.13812031\n",
      "   1.27141766]\n",
      " [ 1.80032743 -0.64777967 -0.27942519 ... -0.86951801  0.37390383\n",
      "   0.43154519]\n",
      " [ 1.44810927 -1.07230015 -0.35841972 ... -0.49802006  0.40109573\n",
      "   0.68200518]]\n"
     ]
    }
   ],
   "source": [
    "#Standardscalar\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "data1 = crop[['N', 'P','K','temperature', 'humidity', 'ph', 'rainfall']]\n",
    "\n",
    "scaler = StandardScaler()\n",
    "# transform data\n",
    "scaled = scaler.fit_transform(data1)\n",
    "print(scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(scaled,target,test_size = 0.2,random_state =2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "acc = []\n",
    "model = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive Bayes 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": [
    "#Naive Bayes Classifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "NaiveBayes = GaussianNB()\n",
    "\n",
    "NaiveBayes.fit(x_train,y_train)\n",
    "\n",
    "predicted_values = NaiveBayes.predict(x_test)\n",
    "x = metrics.accuracy_score(y_test, predicted_values)\n",
    "acc.append(x)\n",
    "model.append('Naive Bayes')\n",
    "print(\"Naive Bayes Accuracy is: \", x)\n",
    "\n",
    "print(classification_report(y_test,predicted_values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training accuracy =  0.9960227272727272\n",
      "Testing accuracy =  0.990909090909091\n"
     ]
    }
   ],
   "source": [
    "#Print Train Accuracy\n",
    "nb_train_accuracy = NaiveBayes.score(x_train,y_train)\n",
    "print(\"Training accuracy = \",NaiveBayes.score(x_train,y_train))\n",
    "#Print Test Accuracy\n",
    "nb_test_accuracy = NaiveBayes.score(x_test,y_test)\n",
    "print(\"Testing accuracy = \",NaiveBayes.score(x_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = NaiveBayes.predict(x_test)\n",
    "y_true = y_test\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "cm_nb = confusion_matrix(y_true,y_pred)\n",
    "\n",
    "f, ax = plt.subplots(figsize=(15,10))\n",
    "sns.heatmap(cm_nb, annot=True, linewidth=0.5, fmt=\".0f\",  cmap='viridis', ax = ax)\n",
    "plt.xlabel(\"Predicted\")\n",
    "plt.ylabel(\"Actual\")\n",
    "plt.title('Predicted vs actual')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}