Repository for the Data Mining course (VU University, 2017)
We designed and trained a recommender system based on a Machine Learning approach. The algorithms are able to predict what hotel a user is most likely to book. The whole implementation is based in Python2, using data science packages (scikit-learn, numpy, pandas, ...).
The trained algorithms include:
- Random Forest regression (RF)
- Support Vector Machine (SVM)
- LambdaMART, based on Gradient Boosted Regression trees
- Coordinate Ascent
The overall implementation is exhaustively detailed in the report included in the report directory: https://github.com/tropicalberto/expedia_ranking_hotels/blob/master/report/report__hotel_recommender_system_v1.pdf
A public dataset, released by Expedia, was retrieved from Kaggle (https://www.kaggle.com/c/expedia-personalized-sort)
- Marie Corradi @MarieCo (https://github.com/MarieCo)
- Elena Garcia @egarcialara (https://github.com/egarcialara)
- Alberto Gil @tropicalberto (https://github.com/tropicalberto)
All authors contributed equally to this work.