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Analyze another fileLogistic regression on hotel booking data predicting cancellation from lead time deposit type customer type market segment and prior cancellation history with seasonal trend analysis and model evaluation metrics
Use this when you have hotel booking records with a binary cancellation outcome and want to identify which booking characteristics drive cancellation risk and how rates trend over time
Do not use if your goal is survival analysis or if you lack a clear binary cancellation flag in the dataset
Dataset with 15 columns
Minimum 100 rows
Cornerstone #20 — logistic + time series on hotel booking demand (2,682 votes)
Month-over-month cancellation rate by hotel type revealing seasonal peaks and trends
Cancellation rates grouped by deposit policy (No Deposit, Non Refund, Refundable)
Cancellation rates grouped by booking channel and market segment
Box plot of lead time in days split by whether booking was canceled or kept
Cancellation rates by customer segment Transient Contract Group Transient-Party
Logistic regression odds ratios showing which predictors most increase or decrease cancellation risk
Heatmap of reserved vs assigned room type showing frequency of room reassignment
Model performance metrics accuracy AUC-ROC sensitivity and specificity on the test set
Plain-English interpretation — what the numbers mean, what's significant, and what to do next.
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