PolySVM.ipynb

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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split,cross_val_score\n",
    "from sklearn.svm import LinearSVC,SVC\n",
    "import timeit\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import confusion_matrix,classification_report,accuracy_score,f1_score,precision_score,recall_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9be7977a",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_resampled_encoding=pd.read_csv('resampled_with_encoding.csv')\n",
    "y_resampled=pd.read_csv('y_resampled.csv')"
   ]
  },
  {
   "cell_type": "code",
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     "name": "stdout",
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     "text": [
      "Time taken to fit the model in sec is  62353.18923\n"
     ]
    }
   ],
   "source": [
    "X = x_resampled_encoding.values\n",
    "Y = np.squeeze(y_resampled.values)\n",
    "X_train,X_test2,y_train,y_test2=train_test_split(X,Y,test_size=0.2,random_state=10)\n",
    "\n",
    "scaler = StandardScaler().fit(X_train)\n",
    "train_sc = scaler.transform(X_train)\n",
    "test_sc = scaler.transform(X_test2)\n",
    "\n",
    "poly_svm_clf=SVC(kernel='poly',random_state=48)\n",
    "start_time=timeit.default_timer()\n",
    "poly_svm_clf.fit(train_sc,y_train)\n",
    "print(\"Time taken to fit the model in sec is \",timeit.default_timer() - start_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e79c2d57",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=poly_svm_clf.predict(test_sc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9a890a01",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score: 0.9421\n",
      "F1 score : 0.9406\n",
      "Recall score : 0.9139\n",
      "Precision score : 0.9689\n"
     ]
    }
   ],
   "source": [
    "print(f\"Accuracy score: {accuracy_score(y_test2, y_pred):.4f}\")\n",
    "print(f\"F1 score : {f1_score(y_test2, y_pred):.4f}\")\n",
    "print(f\"Recall score : {recall_score(y_test2, y_pred):.4f}\")\n",
    "print(f\"Precision score : {precision_score(y_test2, y_pred):.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2f5454a",
   "metadata": {},
   "source": [
    "### PolynomialSVM has higher accuracy than LinearSVM"
   ]
  }
 ],
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