Email_classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importing The Necessary Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loading the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "The Shape Of the dataset is :  (4601, 58)\n",
      "\n",
      "\n",
      "The Attributes of the dataset is :  Index(['word_freq_make', 'word_freq_address', 'word_freq_all', 'word_freq_3d',\n",
      "       'word_freq_our', 'word_freq_over', 'word_freq_remove',\n",
      "       'word_freq_internet', 'word_freq_order', 'word_freq_mail',\n",
      "       'word_freq_receive', 'word_freq_will', 'word_freq_people',\n",
      "       'word_freq_report', 'word_freq_addresses', 'word_freq_free',\n",
      "       'word_freq_business', 'word_freq_email', 'word_freq_you',\n",
      "       'word_freq_credit', 'word_freq_your', 'word_freq_font', 'word_freq_000',\n",
      "       'word_freq_money', 'word_freq_hp', 'word_freq_hpl', 'word_freq_george',\n",
      "       'word_freq_650', 'word_freq_lab', 'word_freq_labs', 'word_freq_telnet',\n",
      "       'word_freq_857', 'word_freq_data', 'word_freq_415', 'word_freq_85',\n",
      "       'word_freq_technology', 'word_freq_1999', 'word_freq_parts',\n",
      "       'word_freq_pm', 'word_freq_direct', 'word_freq_cs', 'word_freq_meeting',\n",
      "       'word_freq_original', 'word_freq_project', 'word_freq_re',\n",
      "       'word_freq_edu', 'word_freq_table', 'word_freq_conference',\n",
      "       'char_freq_%3B', 'char_freq_%28', 'char_freq_%5B', 'char_freq_%21',\n",
      "       'char_freq_%24', 'char_freq_%23', 'capital_run_length_average',\n",
      "       'capital_run_length_longest', 'capital_run_length_total', 'class'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "Email_df=pd.read_csv(\"spambase_csv.csv\")\n",
    "print(\"\\n\\nThe Shape Of the dataset is : \",Email_df.shape)\n",
    "print(\"\\n\\nThe Attributes of the dataset is : \",Email_df.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pre-Processing the data (Handling missing values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Number of Missing Values in the dataset\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "word_freq_make                0\n",
       "word_freq_address             0\n",
       "word_freq_all                 0\n",
       "word_freq_3d                  0\n",
       "word_freq_our                 0\n",
       "word_freq_over                0\n",
       "word_freq_remove              0\n",
       "word_freq_internet            0\n",
       "word_freq_order               0\n",
       "word_freq_mail                0\n",
       "word_freq_receive             0\n",
       "word_freq_will                0\n",
       "word_freq_people              0\n",
       "word_freq_report              0\n",
       "word_freq_addresses           0\n",
       "word_freq_free                0\n",
       "word_freq_business            0\n",
       "word_freq_email               0\n",
       "word_freq_you                 0\n",
       "word_freq_credit              0\n",
       "word_freq_your                0\n",
       "word_freq_font                0\n",
       "word_freq_000                 0\n",
       "word_freq_money               0\n",
       "word_freq_hp                  0\n",
       "word_freq_hpl                 0\n",
       "word_freq_george              0\n",
       "word_freq_650                 0\n",
       "word_freq_lab                 0\n",
       "word_freq_labs                0\n",
       "word_freq_telnet              0\n",
       "word_freq_857                 0\n",
       "word_freq_data                0\n",
       "word_freq_415                 0\n",
       "word_freq_85                  0\n",
       "word_freq_technology          0\n",
       "word_freq_1999                0\n",
       "word_freq_parts               0\n",
       "word_freq_pm                  0\n",
       "word_freq_direct              0\n",
       "word_freq_cs                  0\n",
       "word_freq_meeting             0\n",
       "word_freq_original            0\n",
       "word_freq_project             0\n",
       "word_freq_re                  0\n",
       "word_freq_edu                 0\n",
       "word_freq_table               0\n",
       "word_freq_conference          0\n",
       "char_freq_%3B                 0\n",
       "char_freq_%28                 0\n",
       "char_freq_%5B                 0\n",
       "char_freq_%21                 0\n",
       "char_freq_%24                 0\n",
       "char_freq_%23                 0\n",
       "capital_run_length_average    0\n",
       "capital_run_length_longest    0\n",
       "capital_run_length_total      0\n",
       "class                         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"The Number of Missing Values in the dataset\\n\")\n",
    "Email_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feature Engineering Techniques"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = Email_df.drop(columns=['class']) \n",
    "y = Email_df['class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=15)\n",
    "X_pca = pca.fit_transform(X)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_pca, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Split the data into training, testing and validation sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train the model. Test the model. Measure the performance of the trained model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1) Linear Kernal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy (Linear Kernel): 0.9229098805646037\n"
     ]
    }
   ],
   "source": [
    "svm_linear = SVC(kernel='linear')\n",
    "svm_linear.fit(X_train, y_train)\n",
    "y_pred = svm_linear.predict(X_test)\n",
    "accuracy_linear = svm_linear.score(X_test, y_test)\n",
    "print(\"Accuracy (Linear Kernel):\", accuracy_linear)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(conf_matrix, annot=True, fmt=\"d\", cmap=\"Purples\")\n",
    "#colors available: Blues, inferno, Greens\n",
    "plt.xlabel(\"Predicted\")\n",
    "plt.ylabel(\"Actual\")\n",
    "plt.title(\"Confusion Matrix\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2) Polynomial kernal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy (Polynomial Kernel): 0.6254071661237784\n"
     ]
    }
   ],
   "source": [
    "svm_poly = SVC(kernel='poly')\n",
    "svm_poly.fit(X_train, y_train)\n",
    "y_pred = svm_poly.predict(X_test)\n",
    "accuracy_poly = svm_poly.score(X_test, y_test)\n",
    "print(\"Accuracy (Polynomial Kernel):\", accuracy_poly)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(conf_matrix, annot=True, fmt=\"d\", cmap=\"Blues\")\n",
    "plt.xlabel(\"Predicted\")\n",
    "plt.ylabel(\"Actual\")\n",
    "plt.title(\"Confusion Matrix\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3) Sigmoid kernal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy (Sigmoid Kernel): 0.6351791530944625\n"
     ]
    }
   ],
   "source": [
    "svm_sigmoid = SVC(kernel='sigmoid')\n",
    "svm_sigmoid.fit(X_train, y_train)\n",
    "y_pred = svm_sigmoid.predict(X_test)\n",
    "accuracy_sigmoid = svm_sigmoid.score(X_test, y_test)\n",
    "print(\"Accuracy (Sigmoid Kernel):\", accuracy_sigmoid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Represent the results using graphs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(conf_matrix, annot=True, fmt=\"d\", cmap=\"Blues\")\n",
    "plt.xlabel(\"Predicted\")\n",
    "plt.ylabel(\"Actual\")\n",
    "plt.title(\"Confusion Matrix\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Accuracy (Linear Kernel)     :  92.29098805646036\n",
      "Accuracy (Polynomial Kernel) :  62.54071661237784\n",
      "Accuracy (Sigmoid Kernel)    :  63.51791530944625\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n\\nAccuracy (Linear Kernel)     : \", accuracy_linear*100)\n",
    "print(\"Accuracy (Polynomial Kernel) : \", accuracy_poly*100)\n",
    "print(\"Accuracy (Sigmoid Kernel)    : \", accuracy_sigmoid*100)"
   ]
  }
 ],
 "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": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}