Binary Classification Model for In-Vehicle Coupon Recommendation Using Scikit-learn

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 average performance of the machine learning algorithms achieved an accuracy benchmark of 0.7109 using the training dataset. We selected Extra Trees as the final model as it processed the training dataset with a final accuracy score of 0.7577. When we processed the test dataset with the final model, the model achieved an accuracy score of 0.7717.

CONCLUSION: In this iteration, the Extra Trees 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.