Naive bayes.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "import pickle\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>anode outlet pressure 1</th>\n",
       "      <th>anode outlet pressure 2</th>\n",
       "      <th>cathode outlet pressure 1</th>\n",
       "      <th>cathode outlet pressure 2</th>\n",
       "      <th>current</th>\n",
       "      <th>anode reactant flow</th>\n",
       "      <th>anode inlet pressure 1</th>\n",
       "      <th>anode inlet pressure 2</th>\n",
       "      <th>cathode air inlet flow</th>\n",
       "      <th>cathode inlet pressure 1</th>\n",
       "      <th>...</th>\n",
       "      <th>cathode outlet temperature 2</th>\n",
       "      <th>cathode inlet temperature 2</th>\n",
       "      <th>cathode outlet temperature 1</th>\n",
       "      <th>cathode inlet temperature 1</th>\n",
       "      <th>primary water inlet pressure 2</th>\n",
       "      <th>primary water inlet pressure 1</th>\n",
       "      <th>primary water inlet flow 2</th>\n",
       "      <th>primary water inlet flow 1</th>\n",
       "      <th>water inlet temperature</th>\n",
       "      <th>flag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>700</td>\n",
       "      <td>700</td>\n",
       "      <td>106</td>\n",
       "      <td>283</td>\n",
       "      <td>1792</td>\n",
       "      <td>1788</td>\n",
       "      <td>1684</td>\n",
       "      <td>1252</td>\n",
       "      <td>...</td>\n",
       "      <td>74</td>\n",
       "      <td>46</td>\n",
       "      <td>75</td>\n",
       "      <td>47</td>\n",
       "      <td>2040</td>\n",
       "      <td>2008</td>\n",
       "      <td>814</td>\n",
       "      <td>816</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>700</td>\n",
       "      <td>700</td>\n",
       "      <td>106</td>\n",
       "      <td>283</td>\n",
       "      <td>1792</td>\n",
       "      <td>1788</td>\n",
       "      <td>1680</td>\n",
       "      <td>1252</td>\n",
       "      <td>...</td>\n",
       "      <td>75</td>\n",
       "      <td>46</td>\n",
       "      <td>75</td>\n",
       "      <td>46</td>\n",
       "      <td>2036</td>\n",
       "      <td>2004</td>\n",
       "      <td>814</td>\n",
       "      <td>812</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>700</td>\n",
       "      <td>700</td>\n",
       "      <td>106</td>\n",
       "      <td>283</td>\n",
       "      <td>1792</td>\n",
       "      <td>1788</td>\n",
       "      <td>1676</td>\n",
       "      <td>1252</td>\n",
       "      <td>...</td>\n",
       "      <td>75</td>\n",
       "      <td>46</td>\n",
       "      <td>75</td>\n",
       "      <td>46</td>\n",
       "      <td>2044</td>\n",
       "      <td>2004</td>\n",
       "      <td>816</td>\n",
       "      <td>816</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>700</td>\n",
       "      <td>700</td>\n",
       "      <td>106</td>\n",
       "      <td>283</td>\n",
       "      <td>1792</td>\n",
       "      <td>1792</td>\n",
       "      <td>1688</td>\n",
       "      <td>1252</td>\n",
       "      <td>...</td>\n",
       "      <td>74</td>\n",
       "      <td>46</td>\n",
       "      <td>75</td>\n",
       "      <td>46</td>\n",
       "      <td>2048</td>\n",
       "      <td>2012</td>\n",
       "      <td>814</td>\n",
       "      <td>818</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>700</td>\n",
       "      <td>700</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>980</td>\n",
       "      <td>992</td>\n",
       "      <td>8</td>\n",
       "      <td>1008</td>\n",
       "      <td>...</td>\n",
       "      <td>39</td>\n",
       "      <td>48</td>\n",
       "      <td>39</td>\n",
       "      <td>47</td>\n",
       "      <td>992</td>\n",
       "      <td>1004</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>7695</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>700</td>\n",
       "      <td>700</td>\n",
       "      <td>106</td>\n",
       "      <td>283</td>\n",
       "      <td>1792</td>\n",
       "      <td>1788</td>\n",
       "      <td>1680</td>\n",
       "      <td>1248</td>\n",
       "      <td>...</td>\n",
       "      <td>74</td>\n",
       "      <td>46</td>\n",
       "      <td>75</td>\n",
       "      <td>46</td>\n",
       "      <td>2040</td>\n",
       "      <td>2004</td>\n",
       "      <td>814</td>\n",
       "      <td>808</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>7696</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>700</td>\n",
       "      <td>700</td>\n",
       "      <td>106</td>\n",
       "      <td>283</td>\n",
       "      <td>1792</td>\n",
       "      <td>1788</td>\n",
       "      <td>1672</td>\n",
       "      <td>1252</td>\n",
       "      <td>...</td>\n",
       "      <td>75</td>\n",
       "      <td>46</td>\n",
       "      <td>75</td>\n",
       "      <td>46</td>\n",
       "      <td>2036</td>\n",
       "      <td>2004</td>\n",
       "      <td>816</td>\n",
       "      <td>818</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7697</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>700</td>\n",
       "      <td>700</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1020</td>\n",
       "      <td>1024</td>\n",
       "      <td>48</td>\n",
       "      <td>1008</td>\n",
       "      <td>...</td>\n",
       "      <td>25</td>\n",
       "      <td>50</td>\n",
       "      <td>27</td>\n",
       "      <td>48</td>\n",
       "      <td>1004</td>\n",
       "      <td>1012</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
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       "      <th>7698</th>\n",
       "      <td>0</td>\n",
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       "      <td>700</td>\n",
       "      <td>106</td>\n",
       "      <td>283</td>\n",
       "      <td>1792</td>\n",
       "      <td>1788</td>\n",
       "      <td>1680</td>\n",
       "      <td>1248</td>\n",
       "      <td>...</td>\n",
       "      <td>74</td>\n",
       "      <td>46</td>\n",
       "      <td>75</td>\n",
       "      <td>46</td>\n",
       "      <td>2044</td>\n",
       "      <td>2012</td>\n",
       "      <td>814</td>\n",
       "      <td>814</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7699</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>700</td>\n",
       "      <td>700</td>\n",
       "      <td>106</td>\n",
       "      <td>283</td>\n",
       "      <td>1792</td>\n",
       "      <td>1788</td>\n",
       "      <td>1680</td>\n",
       "      <td>1248</td>\n",
       "      <td>...</td>\n",
       "      <td>74</td>\n",
       "      <td>46</td>\n",
       "      <td>75</td>\n",
       "      <td>46</td>\n",
       "      <td>2044</td>\n",
       "      <td>2008</td>\n",
       "      <td>814</td>\n",
       "      <td>816</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7700 rows × 21 columns</p>\n",
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      ],
      "text/plain": [
       "      anode outlet pressure 1  anode outlet pressure 2  \\\n",
       "0                           0                        0   \n",
       "1                           0                        0   \n",
       "2                           0                        0   \n",
       "3                           0                        0   \n",
       "4                           0                        0   \n",
       "...                       ...                      ...   \n",
       "7695                        0                        0   \n",
       "7696                        0                        0   \n",
       "7697                        0                        0   \n",
       "7698                        0                        0   \n",
       "7699                        0                        0   \n",
       "\n",
       "      cathode outlet pressure 1  cathode outlet pressure 2   current  \\\n",
       "0                           700                        700       106   \n",
       "1                           700                        700       106   \n",
       "2                           700                        700       106   \n",
       "3                           700                        700       106   \n",
       "4                           700                        700         0   \n",
       "...                         ...                        ...       ...   \n",
       "7695                        700                        700       106   \n",
       "7696                        700                        700       106   \n",
       "7697                        700                        700         0   \n",
       "7698                        700                        700       106   \n",
       "7699                        700                        700       106   \n",
       "\n",
       "      anode reactant flow  anode inlet pressure 1  anode inlet pressure 2  \\\n",
       "0                     283                    1792                    1788   \n",
       "1                     283                    1792                    1788   \n",
       "2                     283                    1792                    1788   \n",
       "3                     283                    1792                    1792   \n",
       "4                       0                     980                     992   \n",
       "...                   ...                     ...                     ...   \n",
       "7695                  283                    1792                    1788   \n",
       "7696                  283                    1792                    1788   \n",
       "7697                    0                    1020                    1024   \n",
       "7698                  283                    1792                    1788   \n",
       "7699                  283                    1792                    1788   \n",
       "\n",
       "      cathode air inlet flow   cathode inlet pressure 1  ...  \\\n",
       "0                       1684                       1252  ...   \n",
       "1                       1680                       1252  ...   \n",
       "2                       1676                       1252  ...   \n",
       "3                       1688                       1252  ...   \n",
       "4                          8                       1008  ...   \n",
       "...                      ...                        ...  ...   \n",
       "7695                    1680                       1248  ...   \n",
       "7696                    1672                       1252  ...   \n",
       "7697                      48                       1008  ...   \n",
       "7698                    1680                       1248  ...   \n",
       "7699                    1680                       1248  ...   \n",
       "\n",
       "      cathode outlet temperature 2  cathode inlet temperature 2  \\\n",
       "0                               74                           46   \n",
       "1                               75                           46   \n",
       "2                               75                           46   \n",
       "3                               74                           46   \n",
       "4                               39                           48   \n",
       "...                            ...                          ...   \n",
       "7695                            74                           46   \n",
       "7696                            75                           46   \n",
       "7697                            25                           50   \n",
       "7698                            74                           46   \n",
       "7699                            74                           46   \n",
       "\n",
       "      cathode outlet temperature 1  cathode inlet temperature 1  \\\n",
       "0                               75                           47   \n",
       "1                               75                           46   \n",
       "2                               75                           46   \n",
       "3                               75                           46   \n",
       "4                               39                           47   \n",
       "...                            ...                          ...   \n",
       "7695                            75                           46   \n",
       "7696                            75                           46   \n",
       "7697                            27                           48   \n",
       "7698                            75                           46   \n",
       "7699                            75                           46   \n",
       "\n",
       "      primary water inlet pressure 2  primary water inlet pressure 1  \\\n",
       "0                               2040                            2008   \n",
       "1                               2036                            2004   \n",
       "2                               2044                            2004   \n",
       "3                               2048                            2012   \n",
       "4                                992                            1004   \n",
       "...                              ...                             ...   \n",
       "7695                            2040                            2004   \n",
       "7696                            2036                            2004   \n",
       "7697                            1004                            1012   \n",
       "7698                            2044                            2012   \n",
       "7699                            2044                            2008   \n",
       "\n",
       "      primary water inlet flow 2  primary water inlet flow 1  \\\n",
       "0                            814                         816   \n",
       "1                            814                         812   \n",
       "2                            816                         816   \n",
       "3                            814                         818   \n",
       "4                              0                           0   \n",
       "...                          ...                         ...   \n",
       "7695                         814                         808   \n",
       "7696                         816                         818   \n",
       "7697                           0                           0   \n",
       "7698                         814                         814   \n",
       "7699                         814                         816   \n",
       "\n",
       "      water inlet temperature  flag  \n",
       "0                          29     0  \n",
       "1                          30     0  \n",
       "2                          30     0  \n",
       "3                          29     0  \n",
       "4                          33     1  \n",
       "...                       ...   ...  \n",
       "7695                       29     0  \n",
       "7696                       30     0  \n",
       "7697                       33     1  \n",
       "7698                       29     0  \n",
       "7699                       29     0  \n",
       "\n",
       "[7700 rows x 21 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(r\"C:\\Users\\Suyash Tambe\\Desktop\\PDS\\PEM_Cell.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 1.0\n",
      "Precision: 1.0\n",
      "Recall: 1.0\n",
      "F1-score: 1.0\n",
      "Confusion Matrix:\n",
      "[[977   0]\n",
      " [  0 563]]\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00       977\n",
      "           1       1.00      1.00      1.00       563\n",
      "\n",
      "    accuracy                           1.00      1540\n",
      "   macro avg       1.00      1.00      1.00      1540\n",
      "weighted avg       1.00      1.00      1.00      1540\n",
      "\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pickle\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn import metrics\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import confusion_matrix, classification_report, roc_curve, auc\n",
    "\n",
    "# Load the data\n",
    "df = pd.read_csv(r\"C:\\Users\\Suyash Tambe\\Desktop\\PDS\\PEM_Cell.csv\")\n",
    "\n",
    "# Selecting features and target\n",
    "features = df[[\" current\",\"primary water inlet pressure 1\", \"primary water inlet pressure 2\"]]\n",
    "target = df['flag']\n",
    "\n",
    "# Split the data into training and testing sets\n",
    "features_train, features_test, target_train, target_test = train_test_split(features, target, test_size=0.2, random_state=42)\n",
    "\n",
    "# Initializing and training the Naive Bayes model\n",
    "model = GaussianNB()\n",
    "model.fit(features_train, target_train)\n",
    "\n",
    "# Make predictions on the test set\n",
    "target_pred = model.predict(features_test)\n",
    "\n",
    "# Evaluate the model\n",
    "accuracy = metrics.accuracy_score(target_test, target_pred)\n",
    "precision = metrics.precision_score(target_test, target_pred)\n",
    "recall = metrics.recall_score(target_test, target_pred)\n",
    "f1_score = metrics.f1_score(target_test, target_pred)\n",
    "\n",
    "print(f'Accuracy: {accuracy}')\n",
    "print(f'Precision: {precision}')\n",
    "print(f'Recall: {recall}')\n",
    "print(f'F1-score: {f1_score}')\n",
    "\n",
    "# Confusion Matrix\n",
    "conf_matrix = confusion_matrix(target_test, target_pred)\n",
    "print(\"Confusion Matrix:\")\n",
    "print(conf_matrix)\n",
    "\n",
    "# Classification Report\n",
    "class_report = classification_report(target_test, target_pred)\n",
    "print(\"Classification Report:\")\n",
    "print(class_report)\n",
    "\n",
    "# ROC Curve and AUC\n",
    "fpr, tpr, thresholds = roc_curve(target_test, target_pred)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.plot(fpr, tpr, color='darkorange', lw=2, label='ROC curve (area = {:.2f})'.format(roc_auc))\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver Operating Characteristic (ROC) Curve')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()\n"
   ]
  },
  {
   "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": "3.11.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}