SVM (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": [
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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>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": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.83794466 -0.225       0.37931034 ...  0.0515245   0.08187409\n",
      "   1.80970317]\n",
      " [ 0.75889328  0.175       0.31034483 ... -0.0051707   0.64399517\n",
      "   2.20691785]\n",
      " [ 0.36363636  0.1         0.4137931  ...  0.06223696  1.48659293\n",
      "   2.83168879]\n",
      " ...\n",
      " [ 1.28063241 -0.45       -0.06896552 ... -0.44625943 -0.06558898\n",
      "   1.31380951]\n",
      " [ 1.26482213 -0.475       0.06896552 ... -0.9548262   0.35059336\n",
      "   0.54102359]\n",
      " [ 1.05928854 -0.825      -0.06896552 ... -0.67628235  0.37269542\n",
      "   0.77147757]]\n"
     ]
    }
   ],
   "source": [
    "#Normalization\n",
    "from sklearn.preprocessing import RobustScaler\n",
    "data = crop[['N', 'P','K','temperature', 'humidity', 'ph', 'rainfall']]\n",
    "target = crop['label']\n",
    "scaler = RobustScaler()\n",
    "# transform data\n",
    "scaled = scaler.fit_transform(data)\n",
    "print(scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 16 candidates, totalling 48 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done  48 out of  48 | elapsed:    2.1s finished\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "params = {'kernel' : ['linear', 'poly','rbf','sigmoid'],\n",
    "          'gamma':['scale', 'auto'],\n",
    "         'decision_function_shape':['ovo', 'ovr']}\n",
    "gs_SVC = GridSearchCV(SVC(), params, verbose=1, cv=3)\n",
    "g_res_SVC= gs_SVC.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.98522694016935"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g_res_SVC.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'decision_function_shape': 'ovo', 'gamma': 'scale', 'kernel': 'linear'}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g_res_SVC.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(decision_function_shape='ovo', kernel='linear')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm = SVC(kernel = 'linear', gamma = 'scale',decision_function_shape = 'ovo')\n",
    "svm.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "svm_train_accuracy =  0.9914772727272727\n",
      "svm_test_accuracy =  0.9818181818181818\n"
     ]
    }
   ],
   "source": [
    "# Training & Testing accuracy after applying hyper parameter\n",
    "svm_train_accuracy = svm.score(x_train, y_train)\n",
    "print(\"svm_train_accuracy = \",svm.score(x_train, y_train))\n",
    "#Print Test Accuracy\n",
    "svm_test_accuracy = svm.score(x_test, y_test)\n",
    "print(\"svm_test_accuracy = \",svm.score(x_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "acc = []\n",
    "model = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM Accuracy is:  0.9818181818181818\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       0.84      0.93      0.88        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       0.85      0.69      0.76        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.metrics import classification_report\n",
    "from sklearn import metrics\n",
    "\n",
    "predicted_values = svm.predict(x_test)\n",
    "\n",
    "x = metrics.accuracy_score(y_test, predicted_values);\n",
    "acc.append(x)\n",
    "model.append('SVM')\n",
    "print(\"SVM Accuracy is: \", x)\n",
    "\n",
    "print(classification_report(y_test,predicted_values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "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 = svm.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_svm = confusion_matrix(y_true,y_pred)\n",
    "\n",
    "f, ax = plt.subplots(figsize=(15,10))\n",
    "sns.heatmap(cm_svm, 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",
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