Ad Click Prediction: Leveraging Data Analytics for Targeted Marketing 

This project analyzes user engagement with online advertisements, leveraging data-driven methods to predict whether a user will click on an ad. The insights aim to optimize advertising strategies, improve audience targeting, and maximize return on investment (ROI) 

Exploratory Data Analysis:

• Explored features such as age, daily internet usage, income, and time spent on site.

• Visualized relationships and identified key predictors of ad clicks.

Modeling:

• Applied Logistic Regression to predict user behavior.

• Split data into 67% training and 33% testing sets for robust evaluation.

Performance Metrics:

• Evaluated the model using precision, recall, F1-score, and overall accuracy.

Accuracy: High predictive power, balancing precision and recall effectively.

Business Impact:

• Optimized ad targeting and audience segmentation.

• Data-driven resource allocation for marketing campaigns.

Tools & Technologies

• Data Analysis: Python (Pandas, NumPy, Matplotlib, Seaborn)

• Modeling: Scikit-learn (Logistic Regression)

• Dataset: Advertising data containing user demographic and engagement metrics.

Click here to visit the GitHub repository for more details: GitHub Repository