Interview with Halla Yang: 2nd Place Winner of the Recruit Coupon Purchase Challenge | Kaggle Blog

Interview with Halla Yang: 2nd Place Winner of the Recruit Coupon Purchase Challenge | Kaggle Blog

Introduction:

Introducing the Recruit Coupon Purchase Prediction challenge by Ponpare, Japan’s leading joint coupon site. This challenge aims to predict which coupons a customer will purchase based on past purchase and browsing behavior. In this competition, Halla Yang emerged as the 2nd place winner, outperforming over 1,191 other data scientists. With his expertise in working with time series data and utilizing unsupervised methods alongside gradient boosting, Halla shares his approach and key visualizations that aided his understanding and analysis of the dataset. With almost a decade of experience in finance and a track record of success in Kaggle competitions, Halla’s insights prove valuable in tackling similar forecasting tasks.

Full Article: Interview with Halla Yang: 2nd Place Winner of the Recruit Coupon Purchase Challenge | Kaggle Blog

Japan’s Leading Joint Coupon Site, Ponpare, hosted the Recruit Coupon Purchase Prediction challenge on Kaggle. The challenge required data scientists to predict which coupons a customer would purchase based on their past purchase and browsing behavior.

Halla Yang Secures 2nd Place

Out of 1,191 data scientists, Halla Yang finished in an impressive 2nd place in the Recruit Coupon Purchase Prediction challenge. Halla’s extensive experience working with time series data proved beneficial in effectively utilizing unsupervised methods alongside gradient boosting.

Approach and Key Visualizations

In his blog post, Halla provides a detailed walkthrough of his approach and shares key visualizations that helped him gain a better understanding of the dataset. His decade-long experience in finance as a quantitative researcher and portfolio manager, coupled with his previous success in Kaggle competitions, contributed to his strong performance in this challenge.

Similarities Between Stock Price Prediction and Coupon Purchase Prediction

Halla highlights the similarities between predicting stock prices for thousands of stocks and predicting purchases by thousands of Japanese internet users. Both problems involve analyzing time series data, such as past returns or purchases, as well as cross-sectional data, such as industry averages or peer group averages.

Utilizing Gradient Boosting Classifiers

Halla utilized a gradient boosting classifier to calculate the probability of a user purchasing a specific coupon during the test period for each (user, coupon) pair. This approach allowed him to make accurate predictions based on the user’s browsing and purchase history.

Conclusion

Halla Yang’s 2nd place finish in the Recruit Coupon Purchase Prediction challenge showcases his expertise in analyzing time series data and utilizing unsupervised methods effectively. His extensive experience in finance and previous success in Kaggle competitions sets him apart as a skilled data scientist.

Summary: Interview with Halla Yang: 2nd Place Winner of the Recruit Coupon Purchase Challenge | Kaggle Blog

Ponpare, Japan’s leading joint coupon site, hosted the Recruit Coupon Purchase Prediction challenge on Kaggle. It required participants to predict which coupons a customer would buy based on past purchase and browsing behavior. Halla Yang, a data scientist with experience in time series data, finished 2nd out of 1,191 contestants. In this blog post, he shares his approach and key visualizations that helped him better understand the dataset. With his background in finance and previous success in Kaggle competitions, Halla used a gradient boosting classifier to calculate the probability of a user purchasing a particular coupon.

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