Logistic Regression: Ad Click Prediction

This project demonstrates how to use logistic regression to predict whether a user will click on an online advertisement. The model is built and evaluated using Python and popular data science libraries.

📄 Project Overview

Given a user's demographic and behavioral features, the model predicts the likelihood of an ad click (Clicked on Ad: 1 = Clicked, 0 = Not Clicked).

Dataset Features

• Daily Time Spent on Site: Minutes user spends daily on the website
• Age: Age of the user
• Area Income: Average income of the user's geographic area
• Daily Internet Usage: Average minutes per day user spends on the internet
• Ad Topic Line: Headline of the advertisement
• City: User's city
• Male: 1 if user is male, 0 otherwise
• Country: User's country
• Timestamp: Time when user clicked or closed the ad
• Clicked on Ad: Target variable (1 if clicked, 0 if not)

🚀 Steps in the Notebook

1. Data Loading & Exploration

   • Import required libraries
   • Load the dataset (advertising.csv)
   • Explore data types and summary statistics
   • Visualize distributions and feature relationships

2. Data Preparation

   • Select relevant features
   • Split data into training and test sets

3. Model Building

   • Train a logistic regression model using scikit-learn

4. Evaluation

   • Predict on the test set
   • Evaluate using classification metrics (precision, recall, f1-score, accuracy)

Sample Evaluation Output

             precision    recall  f1-score   support

          0       0.87      0.96      0.91       162
          1       0.96      0.86      0.91       168

avg / total       0.91      0.91      0.91       330

📊 Example Visualizations

• Distribution of user ages
• Joint plots of features (e.g., Area Income vs Age, Daily Time Spent on Site vs Age)
• Pair plots colored by ad click outcome

🛠️ How to Run

You can run this project locally or on Google Colab:

1. Clone the repository or upload the notebook to Colab

2. Upload the advertising.csv dataset

   • To Colab: Use the file upload feature or from google.colab import files; files.upload()

3. Install dependencies (if needed)

   !pip install pandas numpy matplotlib seaborn scikit-learn

4. Run all cells in the notebook

📂 Files

• Logistic Regression Project.ipynb — Main notebook
• advertising.csv — Dataset (make sure it is in the same directory or uploaded in Colab)

✨ Credits

• Dataset and project structure inspired by typical machine learning course exercises.

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For questions or improvements, feel free to open an issue or pull request!