ML App in the hospitality industry : optimization of HR management
summary
Hotel chain Maestra builds an automated staff-planning web app for Amarin (280 rooms, 850 guests) that forecasts working hours and maps them to job categories to produce schedules, using Python, Flask/Bootstrap front end, Oracle/MS SQL and SQLite storage, Power BI dashboards and Darts time-series library for modelling with past and future covariates. Models are trained daily and saved to MS SQL, evaluated with MAE, MAPE and overall percentage error, and the prototype cuts planning time, reduces human error and improves staff allocation.
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chapters (9)
- 0:00ML in hospitality HR
- 2:36HR management challenges
- 8:15Amarin hotel ML project
- 11:46Staff forecasting approach
- 15:24Data science lifecycle & model selection
- 21:31Model performance metrics
- 24:20ML solution impact & benefits
- 26:25Deployment & future apps
- 27:30Q&A on scalability
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