Binary Classification Model for In-Vehicle Coupon Recommendation Using XGBoost

Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: This project aims to construct a predictive model using various machine learning algorithms and document the end-to-end steps using a template. The In-Vehicle Coupon Recommendation dataset is a binary classification situation where we attempt to predict one of the two possible outcomes.

INTRODUCTION: This dataset, available from UC Irvine’s Machine Learning Repository, studies whether a person will accept the coupon recommended to him under different driving scenarios.

ANALYSIS: The performance of the preliminary XGBoost model achieved an accuracy benchmark of 0.7547. After a series of tuning trials, the refined XGBoost model processed the training dataset with a final accuracy score of 0.7619. When we applied the last model to the test dataset, the model achieved an accuracy score of 0.7737.

CONCLUSION: In this iteration, the XGBoost model appeared to be a suitable algorithm for modeling this dataset.

Dataset Used: In-Vehicle Coupon Recommendation Data Set

Dataset ML Model: Binary classification with numerical and categorical attributes

Dataset Reference:

The HTML formatted report can be found here on GitHub.