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Leveraging machine learning to identify at-risk customers in an imbalanced dataset (16.8% churn rate), with strongest business value from recall-focused targeting and scenario-based retention planning.
Churner Recall
Decision Tree ClassifierCurrent Churn Rate
947 of 5,630 customersRevenue at Risk
Immediate intervention neededAnnual Retention Potential
With targeted strategiesThis project addresses a critical business challenge: 16.8% customer churn resulting in significant revenue loss for an e-commerce platform serving 5,630 customers.
Using machine learning techniques, I developed a predictive model where the priority metric is 52% churner recall (on a 16.8% churn base rate), enabling proactive intervention while accounting for class imbalance.
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Explore the dataset, understand the business context, and identify the key challenges
Explore DatasetDeep dive into the technical analysis, feature engineering, and model development
View AnalysisExplore the data through interactive Tableau visualizations and segmentation analysis
Open DashboardDiscover actionable recommendations and the 90-day implementation plan
View StrategyDownload presentation materials and access code repositories