Naive_Bayes_Bernoulli_Vectorizer.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "98c3ed35-830e-43fb-be4d-fda6b91e281b",
   "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')\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.model_selection import train_test_split,cross_val_score\n",
    "from sklearn.naive_bayes import BernoulliNB\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f79fe773-87ba-4967-9af2-ad35a807ff70",
   "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>Unnamed: 0</th>\n",
       "      <th>text</th>\n",
       "      <th>target</th>\n",
       "      <th>Clean_text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Our Deeds are the Reason of this #earthquake M...</td>\n",
       "      <td>1</td>\n",
       "      <td>deed reason earthquake may forgive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Forest fire near La Ronge Sask. Canada</td>\n",
       "      <td>1</td>\n",
       "      <td>forest fire near la canada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>All residents asked to 'shelter in place' are ...</td>\n",
       "      <td>1</td>\n",
       "      <td>resident shelter place notified officer evacua...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>13,000 people receive #wildfires evacuation or...</td>\n",
       "      <td>1</td>\n",
       "      <td>people receive wildfire evacuation order</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Just got sent this photo from Ruby #Alaska as ...</td>\n",
       "      <td>1</td>\n",
       "      <td>got sent photo ruby smoke wildfire school</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>Two giant cranes holding a bridge collapse int...</td>\n",
       "      <td>1</td>\n",
       "      <td>two giant crane holding bridge collapse nearby...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7575</th>\n",
       "      <td>7609</td>\n",
       "      <td>@aria_ahrary @TheTawniest The out of control w...</td>\n",
       "      <td>1</td>\n",
       "      <td>aria control wild fire even northern part stat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7576</th>\n",
       "      <td>7610</td>\n",
       "      <td>M1.94 [01:04 UTC]?5km S of Volcano Hawaii. htt...</td>\n",
       "      <td>1</td>\n",
       "      <td>volcano</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7577</th>\n",
       "      <td>7611</td>\n",
       "      <td>Police investigating after an e-bike collided ...</td>\n",
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       "      <td>police investigating e bike car little e bike ...</td>\n",
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       "    <tr>\n",
       "      <th>7578</th>\n",
       "      <td>7612</td>\n",
       "      <td>The Latest: More Homes Razed by Northern Calif...</td>\n",
       "      <td>1</td>\n",
       "      <td>latest home northern wildfire news</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7579 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0                                               text  target  \\\n",
       "0              0  Our Deeds are the Reason of this #earthquake M...       1   \n",
       "1              1             Forest fire near La Ronge Sask. Canada       1   \n",
       "2              2  All residents asked to 'shelter in place' are ...       1   \n",
       "3              3  13,000 people receive #wildfires evacuation or...       1   \n",
       "4              4  Just got sent this photo from Ruby #Alaska as ...       1   \n",
       "...          ...                                                ...     ...   \n",
       "7574        7608  Two giant cranes holding a bridge collapse int...       1   \n",
       "7575        7609  @aria_ahrary @TheTawniest The out of control w...       1   \n",
       "7576        7610  M1.94 [01:04 UTC]?5km S of Volcano Hawaii. htt...       1   \n",
       "7577        7611  Police investigating after an e-bike collided ...       1   \n",
       "7578        7612  The Latest: More Homes Razed by Northern Calif...       1   \n",
       "\n",
       "                                             Clean_text  \n",
       "0                    deed reason earthquake may forgive  \n",
       "1                            forest fire near la canada  \n",
       "2     resident shelter place notified officer evacua...  \n",
       "3              people receive wildfire evacuation order  \n",
       "4             got sent photo ruby smoke wildfire school  \n",
       "...                                                 ...  \n",
       "7574  two giant crane holding bridge collapse nearby...  \n",
       "7575  aria control wild fire even northern part stat...  \n",
       "7576                                            volcano  \n",
       "7577  police investigating e bike car little e bike ...  \n",
       "7578                 latest home northern wildfire news  \n",
       "\n",
       "[7579 rows x 4 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"Cleaned_Data.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1acda00e-2355-42bf-a988-84f0cf0972d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "cv = CountVectorizer(max_features=5000)\n",
    "X = cv.fit_transform(df[\"Clean_text\"]).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "aed16fa8-3203-429b-b711-c7ed7e1fcab8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "target\n",
       "0    4314\n",
       "1    3265\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df[\"target\"]\n",
    "y.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2338ebeb-2676-450e-83db-b411c08dcf59",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.1,random_state=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3e6e822e-22bc-4844-865a-74e788ecc681",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training data\n",
      "0.8668816889019205\n",
      "Accuracy on testing data\n",
      "0.8179419525065963\n",
      "[[378  49]\n",
      " [ 89 242]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.89      0.85       427\n",
      "           1       0.83      0.73      0.78       331\n",
      "\n",
      "    accuracy                           0.82       758\n",
      "   macro avg       0.82      0.81      0.81       758\n",
      "weighted avg       0.82      0.82      0.82       758\n",
      "\n"
     ]
    }
   ],
   "source": [
    "bnb = BernoulliNB(alpha=1)\n",
    "bnb.fit(X_train,y_train)\n",
    "y_predBN = bnb.predict(X_test)\n",
    "\n",
    "print(\"Accuracy on training data\")\n",
    "print(bnb.score(X_train,y_train))\n",
    "\n",
    "print(\"Accuracy on testing data\")\n",
    "print(bnb.score(X_test,y_test))\n",
    "\n",
    "cm = confusion_matrix(y_test,y_predBN)\n",
    "print(cm)\n",
    "\n",
    "print(classification_report(y_test,y_predBN))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e3c5eb53-16fa-4e20-ab47-d67f92b1f6ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 500x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5, 4))\n",
    "sns.heatmap(cm,annot=True, fmt=\"d\",cmap=\"Blues\")\n",
    "plt.xlabel('Predicted Label')\n",
    "plt.ylabel('True Label')\n",
    "plt.title('Confusion Matrix Heatmap')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5424f25e-646a-42ef-8d14-bd0420bdbe79",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import joblib\n",
    "# joblib.dump(bnb, 'NB_Vect.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3ce8f27c-b70b-443d-b460-0a210d0d6667",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, auc\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_predBN)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "plt.figure(figsize=(5,4))\n",
    "plt.plot(fpr, tpr, color='blue', lw=2, label=f'ROC curve (AUC = {roc_auc:.2f})')\n",
    "plt.plot([0, 1], [0, 1], color='gray', linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.0])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "54e2748c-aaec-4358-8d9d-f05a5b6a1f9b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "47b8af14-e861-4725-af1f-a90c4ccb5874",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}