SVM Vectorizer.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8b49f58f-aa65-417d-9edc-54663bcb2906",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1a137c4f-5a7a-458e-8cce-b12506dd7ba3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"Cleaned_Data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ad01d724-d302-4cb9-9428-17e2cb345944",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[\"Clean_text\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f5b8d996-4af6-43e9-b987-3448841424c3",
   "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": "230a2552-4266-448a-a60c-81e26d5cf92a",
   "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": "21bcafe9-fb6e-4d6f-999c-7c47437e90a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "vectorizer = CountVectorizer(max_features=2000)\n",
    "X_train_counts = vectorizer.fit_transform(X_train)\n",
    "X_test_counts = vectorizer.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7e7681e0-0cec-43e8-81cd-6a3993f3f54a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "SVC(kernel='linear')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm = SVC(kernel='linear')  \n",
    "svm.fit(X_train_counts, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "71c9e7d5-f60e-4908-8646-b4bb780a007e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training data\n",
      "0.8857938718662952\n",
      "Accuracy on testing data\n",
      "0.7955145118733509\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.84      0.82       427\n",
      "           1       0.78      0.74      0.76       331\n",
      "\n",
      "    accuracy                           0.80       758\n",
      "   macro avg       0.79      0.79      0.79       758\n",
      "weighted avg       0.79      0.80      0.79       758\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = svm.predict(X_test_counts)\n",
    "\n",
    "print(\"Accuracy on training data\")\n",
    "print(svm.score(X_train_counts,y_train))\n",
    "\n",
    "print(\"Accuracy on testing data\")\n",
    "print(svm.score(X_test_counts,y_test))\n",
    "\n",
    "\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "report = classification_report(y_test, y_pred)\n",
    "\n",
    "print(\"Classification Report:\")\n",
    "print(report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "705478c0-79df-4bdc-ac73-1c898709f522",
   "metadata": {},
   "outputs": [
    {
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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_pred)\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": "16c31e40-1c5a-49e1-88db-8dd5474f2f64",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "387e6399-69a1-486f-ad5f-afd1463389eb",
   "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
}