This project aims at predicting hotel prices for better optimization of revenue using machine learning techniques. The dataset is collected from Expedia.com and has shape of 1324526 rows and 22 columns.
Expedia Group, Inc. is an American online travel shopping company for consumer and small business travel.
Expedia is the world’s largest online travel agency (OTA) and powers search results for millions of travel shoppers every day. In this competitive market matching users to hotel inventory is very important since users easily jump from website to website. As such, having the best ranking of hotels (“sort”) for specific users with the best integration of price competitiveness gives an OTA the best chance of winning the sale.
I have implemented Random Forest Regressor, Linear Regression with Regularization, Decision Tree Regressor, Neural Network using Sk-learn and Keras libraries and Used Gridsearch to find best parameters for models.
The Random Forest Regressor algorithm worked best with 0.62 R Squared score and Root Mean Square Error of 40.56 on test dataset.