An online retail store is trying to understand the various customer purchase patterns for their firm.
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Updated
Apr 25, 2024 - Jupyter Notebook
An online retail store is trying to understand the various customer purchase patterns for their firm.
This project encompasses feature engineering, exploratory data analysis (EDA), customer retention analysis, RFM segmentation, and in-depth statistical analysis to gain actionable insights.
This project employs XGBoost regression and XGBoost classifier model to predict user order and user churn on online travel agency data. Reach 97% prediction accuracy.
Hypothesizes that customers who have made a purchase recently, make regular or frequent purchases with you and spend a large amount with you, are more likely to respond positively to future engagement and product offers. This might seem intuitively obvious to those of us who have experience in sales – but what the RFM model brings to the table i…
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