mlp classifier (comparitive study).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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": 14,
   "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": 15,
   "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": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 36 candidates, totalling 108 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 108 out of 108 | elapsed:  1.7min finished\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "params_mlp = {'solver' : ['lbfgs','sgd','adam'],\n",
    "          'activation':['identity', 'logistic','tanh','relu'],\n",
    "            'learning_rate' : ['constant','invscaling','adaptive'] }\n",
    "gs_mlp = GridSearchCV(MLPClassifier(), params_mlp, verbose=1, cv=3)\n",
    "g_res_mlp= gs_mlp.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9812528950158246"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g_res_mlp.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'activation': 'tanh', 'learning_rate': 'invscaling', 'solver': 'lbfgs'}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g_res_mlp.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MLPClassifier(activation='tanh', learning_rate='invscaling', solver='lbfgs')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlp = MLPClassifier( solver='lbfgs',activation='tanh',learning_rate= 'invscaling')\n",
    "mlp.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mlp_train_accuracy =  1.0\n",
      "mlp_test_accuracy =  0.9840909090909091\n"
     ]
    }
   ],
   "source": [
    "# Training & Testing accuracy after applying hyper parameter\n",
    "mlp_train_accuracy = mlp.score(x_train,y_train)\n",
    "print(\"mlp_train_accuracy = \",mlp.score(x_train,y_train))\n",
    "#Print Test Accuracy\n",
    "mlp_test_accuracy = mlp.score(x_test,y_test)\n",
    "print(\"mlp_test_accuracy = \",mlp.score(x_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "acc = []\n",
    "model = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLPClassifier Accuracy is:  0.9840909090909091\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       0.90      0.93      0.91        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       0.97      1.00      0.98        29\n",
      "      papaya       0.90      0.95      0.92        19\n",
      "  pigeonpeas       1.00      1.00      1.00        18\n",
      " pomegranate       1.00      0.94      0.97        17\n",
      "        rice       1.00      0.88      0.93        16\n",
      "  watermelon       1.00      1.00      1.00        15\n",
      "\n",
      "    accuracy                           0.98       440\n",
      "   macro avg       0.99      0.98      0.99       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 = mlp.predict(x_test)\n",
    "\n",
    "x = metrics.accuracy_score(y_test, predicted_values);\n",
    "acc.append(x)\n",
    "model.append('MLPClassifier')\n",
    "print(\"MLPClassifier Accuracy is: \", x)\n",
    "\n",
    "print(classification_report(y_test,predicted_values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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 = mlp.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_mlp = confusion_matrix(y_true,y_pred)\n",
    "\n",
    "f, ax = plt.subplots(figsize=(15,10))\n",
    "sns.heatmap(cm_mlp, 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": []
  }
 ],
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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