ML Assignment 2.ipynb

{
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   "source": [
    "# Classify the email using the binary classification method. \n",
    "## Email Spam detection has two states: \n",
    "    a) Normal State – Not Spam\n",
    "    b) Abnormal State – Spam. \n",
    "Use K-Nearest Neighbors and Support Vector Machine for classification. Analyze their performance.\n",
    "\n",
    "Dataset link: The emails.csv dataset on the Kaggle\n",
    "https://www.kaggle.com/datasets/balaka18/email-spam-classification-dataset-csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1f622335",
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   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'sklearn'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[1;32mIn [1], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mpandas\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mpd\u001b[39;00m\n\u001b[0;32m      2\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mnumpy\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mnp\u001b[39;00m\n\u001b[1;32m----> 3\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39msklearn\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mmodel_selection\u001b[39;00m \u001b[39mimport\u001b[39;00m train_test_split\n\u001b[0;32m      4\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39msklearn\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mneighbors\u001b[39;00m \u001b[39mimport\u001b[39;00m KNeighborsClassifier\n\u001b[0;32m      5\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39msklearn\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39msvm\u001b[39;00m \u001b[39mimport\u001b[39;00m SVC\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'sklearn'"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import confusion_matrix, classification_report,accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db221406",
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   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Email No.</th>\n",
       "      <th>the</th>\n",
       "      <th>to</th>\n",
       "      <th>ect</th>\n",
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       "      <th>connevey</th>\n",
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       "      <th>lay</th>\n",
       "      <th>infrastructure</th>\n",
       "      <th>military</th>\n",
       "      <th>allowing</th>\n",
       "      <th>ff</th>\n",
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       "      <th>Prediction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Email 1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>1</th>\n",
       "      <td>Email 2</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>24</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Email 3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Email 4</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Email 5</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 3002 columns</p>\n",
       "</div>"
      ],
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       "  Email No.  the  to  ect  and  for  of    a  you  hou  ...  connevey  jay  \\\n",
       "0   Email 1    0   0    1    0    0   0    2    0    0  ...         0    0   \n",
       "1   Email 2    8  13   24    6    6   2  102    1   27  ...         0    0   \n",
       "2   Email 3    0   0    1    0    0   0    8    0    0  ...         0    0   \n",
       "3   Email 4    0   5   22    0    5   1   51    2   10  ...         0    0   \n",
       "4   Email 5    7   6   17    1    5   2   57    0    9  ...         0    0   \n",
       "\n",
       "   valued  lay  infrastructure  military  allowing  ff  dry  Prediction  \n",
       "0       0    0               0         0         0   0    0           0  \n",
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       "3       0    0               0         0         0   0    0           0  \n",
       "4       0    0               0         0         0   1    0           0  \n",
       "\n",
       "[5 rows x 3002 columns]"
      ]
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     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"emails.csv\") #Reading the Dataset\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c1ad4120",
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   "outputs": [
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       "   the  to  ect  and  for  of    a  you  hou  in  ...  connevey  jay  valued  \\\n",
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       "4    7   6   17    1    5   2   57    0    9   3  ...         0    0       0   \n",
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(columns=['Email No.'], inplace=True) #Dropping Email No. as it is irrelevant.\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f0b24581",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Splitting the Dataset\n",
    "X=df.iloc[:, :df.shape[1]-1]\n",
    "Y=df.iloc[:, -1]\n",
    "X_train, X_test, Y_train, Y_test=train_test_split(X, Y, test_size=0.20, random_state=8) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a2a38bda",
   "metadata": {},
   "outputs": [],
   "source": [
    "def apply_model(model):#Model to print the scores of various models\n",
    "    model.fit(X_train,Y_train)\n",
    "    print(\"Training score = \",model.score(X_train,Y_train))\n",
    "    print(\"Testing score = \",model.score(X_test,Y_test))\n",
    "    print(\"Accuracy = \",model.score(X_test,Y_test))\n",
    "    Y_pred = model.predict(X_test)\n",
    "    print(\"Predicted values:\\n\",Y_pred)\n",
    "    print(\"Confusion Matrix:\\n\",confusion_matrix(Y_test,Y_pred))\n",
    "    print(\"Classification Report:\\n\",classification_report(Y_test,Y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7b9cdd23",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training score =  0.883248730964467\n",
      "Testing score =  0.8695652173913043\n",
      "Accuracy =  0.8695652173913043\n",
      "Predicted values:\n",
      " [0 0 0 ... 0 0 0]\n",
      "Confusion Matrix:\n",
      " [[653  73]\n",
      " [ 62 247]]\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.90      0.91       726\n",
      "           1       0.77      0.80      0.79       309\n",
      "\n",
      "    accuracy                           0.87      1035\n",
      "   macro avg       0.84      0.85      0.85      1035\n",
      "weighted avg       0.87      0.87      0.87      1035\n",
      "\n"
     ]
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=17) #KNN Model\n",
    "apply_model(knn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c3712b36",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\candr\\anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning: Solver terminated early (max_iter=10000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training score =  0.9951655789219241\n",
      "Testing score =  0.9671497584541063\n",
      "Accuracy =  0.9671497584541063\n",
      "Predicted values:\n",
      " [0 1 0 ... 1 0 0]\n",
      "Confusion Matrix:\n",
      " [[710  16]\n",
      " [ 18 291]]\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.98      0.98       726\n",
      "           1       0.95      0.94      0.94       309\n",
      "\n",
      "    accuracy                           0.97      1035\n",
      "   macro avg       0.96      0.96      0.96      1035\n",
      "weighted avg       0.97      0.97      0.97      1035\n",
      "\n"
     ]
    }
   ],
   "source": [
    "svm = SVC(kernel='linear',random_state=3,max_iter=10000) #SVM Model\n",
    "apply_model(svm)"
   ]
  }
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