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    "# CIS 343-01 - Analytics Programming\n",
    "# Program 4\n",
    "# Due Date: 12/8/2025\n",
    "# Purpose: Build a logistic regression model using social_ads.csv\n",
    "\n",
    "# Import all necessary libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score, classification_report\n",
    "from matplotlib.colors import ListedColormap\n",
    "\n",
    "# Load dataset\n",
    "add_df = pd.read_csv('social_ads.csv')\n",
    "\n",
    "# Prepare features and target\n",
    "x_data = add_df[['Age','EstimatedSalary']].values\n",
    "y_target = add_df['Purchased'].values\n",
    "\n",
    "# Encode target variable (Yes -> 1, No -> 0)\n",
    "le = LabelEncoder()\n",
    "y_target_encoded = le.fit_transform(y_target)\n",
    "\n",
    "# Split the dataset into training and testing sets (75% train, 25% test)\n",
    "X_train, X_test, y_train, y_test = train_test_split(x_data, y_target_encoded, test_size=0.25, random_state=0)\n",
    "\n",
    "# Scale features for better logistic regression performance\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.trans_\n"
   ]
  }
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