Project 2: Hotel Booking Demand Prediction
The dataset for this project contains booking information for a City hotel and a Resort hotel. By utilizing the predictive model for this project, hotels will be able to identify the reasons and proffer answers to these questions. Furthermore, this analysis will help hotels get prepared with adequate and timely arrangements on heavy and low tourist visits period. Using exploratory data analysis, I was able to visualize the different prospective, which gave me different insights into: Which are the busiest months? How many bookings were cancelled? etc. From the summary table presented on this project, I observe through using various classification model that Random Forest algorithm was the best algorithm for this project analysis. I also presented that bookings got cancelled 37% of the time. While booking guest checked-in (did not cancel the bookings) almost 63% of the time.
