This project is a Kaggle competition amongst 72 participants in the APAN5200 Applied Analytics Frameworks and Methods 1 course at Columbia University, to which I came in 4th in the leaderboard for the lowest RMSE on the predicted price on test data.
In this project, I sought to predict Airbnb rental listing prices in New York City using various machine learning techniques such as Linear Regression, Trees, Random Forests and XGBoost. In sum, XGBoost worked really well for me as it resulted in the lowest test RMSE of 53.186 and 60.469 on the Kaggle public and private leaderboards respectively.