L9. Real World Machine Learning - Cooking Predictions
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Valencian Summer School 2015 Day 1 Lecture 9 Real World Machine Learning - Cooking Predictions Andrés González (CleverTask) https://bigml.com/events/valencian-summer-school-in-machine-learning-2015
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L9. Real World Machine Learning - Cooking Predictions
- 1. Cooking Predictions A real case in the hotel sector Andrés González Big Data Prediction Manager andresg@clevertask.com Twitter: @data_lytics
- 2. CleverTask Solutions SL - Big Data Business Unit 3 Agenda Business Need1 “Cooking” Predictions2 Gathering ingredients3 Cleaning and Transforming4 The recipe (the model)5 Tasting the dish6
- 3. CleverTask Solutions SL - Big Data Business Unit 4 Hotel Sector • % room occupation. • Cancellation risk. • Income.
- 4. CleverTask Solutions SL - Big Data Business Unit 5 Business Need Predict client’s NATIONALITY BEFORE client check-in
- 5. CleverTask Solutions SL - Big Data Business Unit 6 Staff Arrangement Languages
- 6. CleverTask Solutions SL - Big Data Business Unit 7 Prepare Activities
- 7. CleverTask Solutions SL - Big Data Business Unit 8 Kitchen Arrangement
- 8. CleverTask Solutions SL - Big Data Business Unit 9 Customize Stay
- 9. CleverTask Solutions SL - Big Data Business Unit 10 … Details Make the Difference In short, because…
- 10. CleverTask Solutions SL - Big Data Business Unit 11 Machine Learning basics
- 11. CleverTask Solutions SL - Big Data Business Unit 12 Machine Learning basics Can you find patterns in this data?
- 12. CleverTask Solutions SL - Big Data Business Unit 13 Machine Learning basics Historical Data Training Prediction New Data Re-Training
- 13. CleverTask Solutions SL - Big Data Business Unit 14 Agenda Business Need1 “Cooking” Predictions2 Gathering ingredients3 Cleaning and Transforming4 The recipe (the model)5 Tasting the dish6
- 14. CleverTask Solutions SL - Big Data Business Unit Tasting the Dish Cooking Transforming 15 “Cooking” Predictions2 Go to the market to buy ingredients Cleaning
- 15. CleverTask Solutions SL - Big Data Business Unit Evaluating Prediction Quality Training the Model Transforming and Feature Engineering 15 “Cooking” Predictions2 Gathering RAW data Cleaning Data
- 16. CleverTask Solutions SL - Big Data Business Unit 16 Agenda Business Need1 “Cooking” Predictions2 Gathering ingredients3 Cleaning and Transforming4 The recipe (the model)5 Tasting the dish6
- 17. CleverTask Solutions SL - Big Data Business Unit 17 Where does Data come from? Own Website Partners Websites RAW Data
- 18. CleverTask Solutions SL - Big Data Business Unit 18 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
- 19. CleverTask Solutions SL - Big Data Business Unit 19 RAW Data
- 20. CleverTask Solutions SL - Big Data Business Unit 20 Agenda Business Need1 “Cooking” Predictions2 Gathering ingredients3 Cleaning and Transforming4 The recipe (the model)5 Tasting the dish6
- 21. CleverTask Solutions SL - Big Data Business Unit “Dirty” RAW Data Gathering Data 21 The Process New Fields 1 3 4 Transformation and Feature Engineering “Clean” Data Calculated Fields 2 Cleaning Model
- 22. CleverTask Solutions SL - Big Data Business Unit 22 Data Cleaning
- 23. CleverTask Solutions SL - Big Data Business Unit 22 Data Cleaning
- 24. CleverTask Solutions SL - Big Data Business Unit 22 Data Cleaning
- 25. CleverTask Solutions SL - Big Data Business Unit 22 Data Cleaning
- 26. CleverTask Solutions SL - Big Data Business Unit 22 Data Cleaning
- 27. CleverTask Solutions SL - Big Data Business Unit 22 Data Cleaning
- 28. CleverTask Solutions SL - Big Data Business Unit 23 Data Cleaning Row Deletion • Reservations without check-in • Cancelled reservations • Rows with errors Column Deletion • IDs vs names • Columns with little data Other Actions • Give dates a format • Delete accents • Transform .xlsx -> .csv
- 29. CleverTask Solutions SL - Big Data Business Unit 24 Clean Dataset Clean •150.000 reservations •46 fields •26 categorical •9 numeric •10 data •1 text •Size: 75MB Dirty •260.000 reservations •80 fields •57 categorical •9 numeric •10 data •3 text •1 incorrect field •Size: 150 MB
- 30. CleverTask Solutions SL - Big Data Business Unit “Dirty” RAW Data Gathering Data 25 The Process New Fields 1 3 4 Transformations and Feature Engineering “Clean” Data Calculated Fields 2 Cleaning Model
- 31. CleverTask Solutions SL - Big Data Business Unit 26 Transformations Country Grouping •A lot of countries to predict (210) •Some countries have very few instances •Grouping objective: mín. 1% of total instances • Does not affect business objective •Total number of groups: 20 New Fields • RESERV_ANTICIPATION (calculated): (reservation date - checkin date) • COUNTRY_HOTEL (name of the country) • HOTEL_STARS (1-5)
- 32. CleverTask Solutions SL - Big Data Business Unit 27 Clean Dataset Clean •150.000 reservations •46 fields •Size: 75MB Dirty •260.000 reservations •80 fields •Size: 150 MB Transformed •150.000 registers •49 fields •Size: 80MB
- 33. CleverTask Solutions SL - Big Data Business Unit 28 What is Feature Engineering Extract signal from noise
- 34. CleverTask Solutions SL - Big Data Business Unit 29 Feature Engineering Techniques • Detecta fields (features) that are predictorss (signal) and bypass those that are not (noise) • Dependand fields (pax, days, pax*days) • Needless fields (reservation number) • Fields with very little data • Random fields (minute and second of reservation) • Domain knowledge • Experience • Recursive cycle
- 35. CleverTask Solutions SL - Big Data Business Unit 30 Field Selection Algorithm Adjustment Prediction Quality Evaluation Recursive Feature Engineering
- 36. CleverTask Solutions SL - Big Data Business Unit 31 Clean Dataset Clean •150.000 reservations •46 fields •Size: 75MB Dirty •260.000 reservations •80 fields •Size: 150 MB Transformed •150.000 registers •49 fields •Size: 80MB Final Dataset •150.000 registers •10 fields •Size: 55MB
- 37. CleverTask Solutions SL - Big Data Business Unit 32 Agenda Business Need1 “Cooking” Predictions2 Gathering ingredients3 Cleaning and Transforming4 The recipe (the model)5 Tasting the dish6
- 38. CleverTask Solutions SL - Big Data Business Unit 33 The Process “Dirty” RAW Data New Fields 1 3 4 Gathering Data Transformation and Feature Engineering “Clean” Data Calculated 2 Cleaning Modeling
- 39. CleverTask Solutions SL - Big Data Business Unit 34 Modeling Training Learning
- 40. CleverTask Solutions SL - Big Data Business Unit 35 Modeling
- 41. CleverTask Solutions SL - Big Data Business Unit 37 Agenda Business Need1 “Cooking” Predictions2 Gathering ingredients3 Cleaning and Transforming4 The recipe (the model)5 Tasting the dish6
- 42. CleverTask Solutions SL - Big Data Business Unit 38 Quality Evaluation 80% 20% Evaluation Training Test Dataset 100% Modelo
- 43. CleverTask Solutions SL - Big Data Business Unit 39 Quality Evaluation Accuracy Confusion Matrix
- 44. CleverTask Solutions SL - Big Data Business Unit 40 Quality Evaluation 54% 75%
- 45. CleverTask Solutions SL - Big Data Business Unit 41 Quality Evaluation Predicted vs Real Distribution
- 46. CleverTask Solutions SL - Big Data Business Unit 42 Cooking Predictions 80% 20% Tasting the Dish Cooking Transforming Go to the market to buy ingredients Cleaning
- 47. CleverTask Solutions SL - Big Data Business Unit 42 Cooking Predictions 80% 20% Evaluating Prediction Quality Training the Model Transforming and Feature Engineering Gathering RAW data Cleaning Data
- 48. CleverTask Solutions SL - Big Data Business Unit 43 Other Techniques Ensembles Clusters Weight Analysis Anomaly Detection
- 49. CleverTask Solutions SL - Big Data Business Unit 44 END email: andresg@clevertask.com Twitter: @data_lytics www.clevertask.com