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 Gradient Boosted Trees model achieved an accuracy benchmark of 0.7678 on the validation dataset. The final model processed the training dataset with an accuracy score of 0.7368. When we applied the finalized model to the validation dataset, the model achieved an accuracy score of 0.7678.
CONCLUSION: In this iteration, the TensorFlow Decision Forests 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: https://archive-beta.ics.uci.edu/ml/datasets/in+vehicle+coupon+recommendation
The HTML formatted report can be found here on GitHub.