• Gained insights into the New York City Airbnb rental properties and discovered the trends in price and customer satisfaction level. Also discovered the kind of rentals receive what type of satisfaction level and predicted the likely satisfaction level of the new rentals leveraging advanced machine learning clustering algorithms such as k-means and estimation algorithms such as linear regression, decision tree and Gradient Boosted Trees.

• Evaluated the performance of all the models and chose the model with best estimation performance measure such as root mean square error, mean absolute error (lowest). Furthermore, optimized the best performing model by changing the hyperparameters such as maximal depth in decision tree estimator for more precise prediction.

• Applied the trained model to a completely new data not utilized in the process of model building and reported the results including perceptible visualizations in a documented form.