This is a machine learning project taken up from the Kaggle competition found here: https://www.kaggle.com/c/airbnb-recruiting-new-user-bookings

1. Predicting New User BookingsAnaelia Ovalle, Michael Liston, Brent Rucker

2. Table of
Contents
I. Introduction to Project & Goal
II. Data Pre-Processing
III. Models
IV. Results
V. Discussion

3. Data Sources

4. Libraries

5. Goal
Using a dataset of 15 basic
features, predict where the
user will make their first
booking

6. Country
Destination
12 countries
14 predictors possible

7. Data Pre-
Processing
1. Observe all distributions
2. Identify NA’s and handle NA
3. Varied Training and Testing
4. Date Feature Extraction
5. One-hot encode categoricals
a. 10/14 predictors categorical
6. Binning

8. Age Feature Imputed by Mean

9. Modeling with Multi-Class
Classification

10. ● 16 Models
○ Decision Trees
○ Random Forests
○ AdaBoost
○ QDA
○ KNN
○ XGBoost
○ SVM
○ Neural Network

11. How Many Trees?

12. Sample Code with Tuning Parameters

13. Feature
Importances

14. Results

18. Discussion

19. Best Accuracy: Random Forest

20. Best Accuracy != Best Model

22. Best Precision: Gradient Boosting

23. Challenges
Access to more structured data
More sophisticated imputation
methods
Evaluate more models
Motives of Airbnb
Time

24. Business
Applications
Precision vs Recall
Use Recall
Increase FN
Increase Spam
Negative impact on
Reputation
Use Precision
Decrease Spam

25. Thank
you