BigMLSchool: Tourists Nationality Prediction
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This use case presents how Machine Learning can help the Hotel Sector better plan ahead. The lecturer is Andrés González, Associate Professor at EOI Business School and CTO and co-founder at Cleverdata.io *Machine Learning School for Business Schools 2021: Virtual Conference.
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BigMLSchool: Tourists Nationality Prediction
- 1. Andrés González • Associate Professor at EOI • CTO at cleverdata.io Tourists Nationality Prediction Helping the Hotel Sector Better Plan Ahead
- 2. Introduction
- 3. Agenda Business Need 1 “Cooking” Predictions 2 Gathering ingredients 3 Cleaning and Transforming 4 Cooking in the oven 5 Tasting the dish 6
- 4. Agenda Business Need 1 “Cooking” Predictions 2 Gathering ingredients 3 Cleaning and Transforming 4 Cooking in the oven 5 Tasting the dish 6
- 5. Business Need for Hotels Predict client’s NATIONALITY BEFORE arriving to the hotel
- 9. Historical Data Training Prediction New Data Re-Training Machine Learning basics
- 10. Agenda Business Need 1 “Cooking” Predictions 2 Gathering ingredients 3 Cleaning and Transforming 4 Cooking in the oven 5 Tasting the dish 6
- 11. Transforming and Selecting Transforming and Feature Engineering Tasting the Dish Evaluating Prediction Quality Cooking Training the Model ML Phases Go to the market to buy ingredients Cleaning Gathering RAW data Cleaning Data
- 12. Agenda Business Need 1 “Cooking” Predictions 2 Gathering ingredients 3 Cleaning and Transforming 4 Cooking in the oven 5 Tasting the dish 6
- 13. Where does the Data come from? Reservations from Own Website Reservations from Booking, Expedia... RAW Data (reservations)
- 14. RAW Data
- 15. RAW Data One year historical reservation data (.xlsx file) Characteristics •260.000 reservations •80 fields •57 categorical •9 numeric •10 date •3 text •1 incorrect field •Size: 150 MB
- 16. Agenda Business Need 1 “Cooking” Predictions 2 Gathering ingredients 3 Cleaning and Transforming 4 Cooking in the oven 5 Tasting the dish 6
- 17. Data Cleaning
- 18. Data Cleaning • Reservations without check-in • Cancelled reservations • Rows with errors • IDs vs names • Columns with very few data • Give dates a format • Delete accents • Transform .xlsx -> .csv Row Deletion Column Deletion Other Actions
- 19. Dirty •260.000 reservations •80 fields •57 categorical •9 numeric •10 date •3 text •1 incorrect field •Size: 150 MB Clean Dataset Clean •150.000 rows •46 fields •26 categorical •9 numeric •10 date •1 text •Size: 75MB
- 20. Agenda Business Need 1 “Cooking” Predictions 2 Gathering ingredients 3 Cleaning and Transforming 4 Cooking in the oven 5 Tasting the dish 6
- 21. Transformations & FE Country Grouping •A lot of countries to predict (210) •Total number of groups: 20 New Fields • RESERV_ANTICIPATION (calculated): (checkin date - reservation date) • HOTEL_COUNTRY (name of the country) • HOTEL_STARS (1-5)
- 22. Clean •150.000 registers •46 fields •Size: 75MB Dirty •260.000 registers •80 fields •Size: 150 MB Transformed •150.000 registers •49 fields •Size: 80MB Transformations & FE
- 23. What is Feature Engineering Extract signal from noise
- 25. Final Dataset Clean •150.000 registers •46 fields •Size: 75MB Dirty •260.000 registers •80 fields •Size: 150 MB Transformed •150.000 registers •49 fields •Size: 80MB Final Dataset •150.000 registers •10 fields •Size: 55MB
- 26. Agenda Business Need 1 “Cooking” Predictions 2 Gathering ingredients 3 Cleaning and Transforming 4 Cooking in the oven 5 Tasting the dish 6
- 28. Algorithms Tree Ensemble Logistic Regression Neurral Networks OptiML
- 29. Modeling
- 30. Agenda Business Need 1 “Cooking” Predictions 2 Gathering ingredients 3 Cleaning and Transforming 4 Cooking in the oven 5 Tasting the dish 6
- 31. Accuracy Confusion Matrix Quality Evaluation
- 33. Evaluating Prediction Quality Training the Model Transforming and Feature Engineering Cleaning Data Gathering RAW data Cooking Predictions 90% 10%