Kaggle Competition: Expedia Hotel Recommendations
For this project, we participated in the Kaggle Competition: Expedia Hotel Recommendations. https://www.kaggle.com/c/expedia-hotel-recommendations
For this assignment, I used two models to make my predictions. I used Decision Tree Regressor and Logistic Regression to make predictions. Currently, Expedia uses search parameters to adjust their hotel recommendations, but there aren’t enough customer specific data to personalize them for each user. In this competition, Expedia is challenging Kagglers to contextualize customer data and predict the likelihood a user will stay at 100 different hotel groups.
Submissions are evaluated according to the Mean Average Precision @ 5 (MAP@5): My Final Submissions ended with a public score of 0.07595 and a private score 0.07547.
Requirements
Python 3.6
Python Libraries
- seaborn
- pandas
- numpy
- sklearn