In today’s competitive digital marketing landscape, predicting Click-Through Rate (CTR) is vital for optimizing ad performance and maximizing ROI. This project tackled the challenge of CTR prediction by leveraging advanced machine learning techniques and feature engineering.

Main Highlights of the Project:

Feature Engineering:

• Conducted detailed correlation analysis and ANOVA to identify the most impactful features.

• Used XGBoost’s feature importance analysis to refine the feature set, ultimately dropping 10 less predictive variables for better model performance.

Model Selection:

• Tested multiple machine learning models, identifying XGBoost as the best performer based on RMSE and generalization to unseen data.

• Fine-tuned hyperparameters such as learning rate, tree depth, and sampling ratios using 5-fold cross-validation, ensuring both accuracy and reliability.

Impactful Results

• Achieved a strong CTR prediction model, ranking 58/387 in the competition, with valuable insights into ad performance optimization.

Reflections and Improvements:

• Explored model stacking with XGBoost, Random Forest, and LightGBM, which showed promise for further improvements.

• Highlighted areas for enhanced time management and experiment tracking to streamline future projects.

Why It Matters:

This project demonstrates how data-driven approaches can transform digital marketing strategies by improving ad targeting, resource allocation, and overall campaign effectiveness. The insights gained here empower businesses to make informed decisions, enhancing both user engagement and ROI.