Evaluate Churn Prediction Model Accuracy using Customer Segmentation Data

You are a data scientist tasked with evaluating the accuracy of a churn prediction model.  You have access to a dataset containing customer demographic information, purchase history, engagement metrics (website visits, app usage, etc.), and a binary churn label (1 for churned, 0 for not churned).  The dataset is segmented into distinct customer groups based on RFM analysis (Recency, Frequency, Monetary value).  Your task is to evaluate the model’s performance separately for each customer segment.  The model’s predictions are provided in a separate column.  Focus your analysis on precision, recall, F1-score, and AUC for each segment.  Additionally, identify segments where the model performs significantly better or worse than others, and suggest potential reasons for these discrepancies.  Present your findings in a structured report, including tables summarizing the performance metrics per segment, visualizations comparing performance across segments, and a discussion of the potential causes of varying performance. Your report should be approximately 500 words.