Predicting New User Bookings

Anaelia Ovalle, Michael Liston, Brent Rucker

Table of Contents

I. Introduction to Project & Goal

II. Data Pre-Processing

III. Models

IV. Results

V. Discussion

Data Sources

Libraries

GoalUsing a dataset of 15 basic features, predict

where the user will make their first booking

Country Destination

12 countries

14 predictors possible

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

Age Feature Imputed by Mean

Modeling with Multi-Class Classification

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

How Many Trees?

Sample Code with Tuning Parameters

Feature Importances

Results

Discussion

Best Accuracy: Random Forest

Best Accuracy != Best Model

Best Precision: Gradient Boosting

Challenges

Access to more structured data

More sophisticated imputation methods

Evaluate more models

Motives of Airbnb

Time

Business Applications

Precision vs Recall

Use Recall

Increase FN

Increase Spam

Negative impact on Reputation

Use Precision

Decrease Spam

More bang for buck

Smarter Decisions

Thank you