Merchant Churn Prediction Using SparkML at PayPal – Databricks

Merchant Churn Prediction Using SparkML at PayPal

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In this session, PayPal will present the techniques used to retain merchants using some of the Machine Learning models using SparkML platform. Retaining merchants directly equates to Dollar value. So, it was very critical for us to identify the right model that trains on our data and predicts merchant behavior giving us insights that help us prevent merchant churn. We will also deep dive on how we captured the right signals filtering the noise that could skew the predictions and some of the challenges we faced in scaling this solution. Lastly, we will see how SparkML orchestrated various events in the pipeline we built thereby enabling us to perform feature engineering, train it, validate and cross-validate it at scale across the different data samples we had.

Session hashtag: #DSSAIS10



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About Chetan Nadgire

Chetan Nadgire is a Member of Technical Staff at Paypal. He works in the Strategy and Growth team focusing on analytics solutions for Merchants using big data technologies like Apache Spark and ML. Chetan holds a degree in Computer Science and over a decade of industry experience with a focus on big data analysis.

About Aniket Kulkarni

Aniket is a software engineer at PayPal. He is currently focussed on developing analytics solutions for PayPal merchants using big data technologies such as Apache Spark and ML focussing on domains such as conversion,churn etc. He has previous experience as a big data engineer in Cerner Corporation and holds a Masters degree in Computer Science from RIT.