graph.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Windows 10\\AppData\\Local\\Temp\\ipykernel_23720\\61707497.py:1: DeprecationWarning: \n",
      "Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),\n",
      "(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)\n",
      "but was not found to be installed on your system.\n",
      "If this would cause problems for you,\n",
      "please provide us feedback at https://github.com/pandas-dev/pandas/issues/54466\n",
      "        \n",
      "  import pandas as pd\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "\n",
    "# Load the dataset (assuming we have access to 'spambase.data')\n",
    "# Since I don't have the actual dataset, I will simulate it here\n",
    "# For the actual case, use: data = pd.read_csv('spambase.data', header=None, names=columns)\n",
    "\n",
    "# Simulated data for demonstration\n",
    "np.random.seed(42)\n",
    "num_samples = 4601  # as per the original spambase dataset\n",
    "num_features = 57  # 57 features\n",
    "\n",
    "# Simulate some random feature data\n",
    "X = np.random.rand(num_samples, num_features)\n",
    "y = np.random.randint(0, 2, num_samples)  # binary target: 0 (non-spam), 1 (spam)\n",
    "\n",
    "# Split the data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Scale the data\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)\n",
    "\n",
    "# Train a Logistic Regression model\n",
    "model = LogisticRegression(max_iter=10000)\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# Predict and calculate accuracy\n",
    "y_pred = model.predict(X_test)\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "\n",
    "# 1. Plot Accuracy\n",
    "plt.figure(figsize=(6, 4))\n",
    "plt.bar([\"Accuracy\"], [accuracy], color='skyblue')\n",
    "plt.title(f\"Model Accuracy: {accuracy:.2f}\")\n",
    "plt.ylim(0, 1)\n",
    "plt.ylabel('Accuracy')\n",
    "plt.show()\n",
    "\n",
    "# 2. Plot Feature Importance (Logistic Regression Coefficients)\n",
    "coefficients = model.coef_[0]\n",
    "sorted_idx = np.argsort(np.abs(coefficients))[::-1]\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.bar(range(10), coefficients[sorted_idx[:10]], align='center')\n",
    "plt.xticks(range(10), [f'Feature {i+1}' for i in sorted_idx[:10]], rotation=45)\n",
    "plt.title(\"Top 10 Important Features (Coefficients)\")\n",
    "plt.ylabel('Coefficient Value')\n",
    "plt.show()\n",
    "\n",
    "# 3. Plot Feature Distribution for Spam vs. Non-Spam\n",
    "feature_idx = sorted_idx[0]  # Use the most important feature for the plot\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.hist(X[y == 0, feature_idx], bins=30, alpha=0.5, label=\"Non-Spam\", color='green')\n",
    "plt.hist(X[y == 1, feature_idx], bins=30, alpha=0.5, label=\"Spam\", color='red')\n",
    "plt.title(f\"Distribution of Feature {feature_idx+1} for Spam vs Non-Spam\")\n",
    "plt.xlabel('Feature Value')\n",
    "plt.ylabel('Frequency')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "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": "3.10.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}