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Customer Churn Predicted Using Gradient Boosted Decision Tree

 

 

Sayali Tanpure … (Department of Data Science, Dr. D. Y. Patil Arts, Commerce and Science College, Pimpri)

Pooja Patil ... (Department of Data Science ,Dr. D. Y. Patil Arts, Commerce and Science College, Pimpri)

 

 

Abstract :
Predicting when customers might leave is a big deal for companies. It helps them keep people around and make more money in the long run. Thing is, this paper looks at using Gradient Boosted Decision Trees, or GBDT, to figure that out. You know, it’s this ensemble method that puts together a bunch of simple models into one strong one for predictions. We checked how well it did with stuff like accuracy, precision, recall, the F1-score, and AUC-ROC. Turns out, the results show it spots those at- risk customers pretty well. That way, businesses can jump in early with ways to hold onto them.

When customers churn, revenue drops, and it messes with the whole business staying solid over time. Machine learning comes in handy here. It digs through old data on customers, like how they use things, their backgrounds, interactions, all that. Gradient Boosted Decision Trees fit right in. They get high accuracy, deal with missing info okay, and don’t overfit as easy. So yeah, this report goes into the churn issue in detail. Covers the methods, the outcomes, shows how it all applies in real life.

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