This EDA project on Hotel Booking Analysis investigates cancellations, and their underlying patterns; and suggests measures that can be implemented to reduce cancellations and secure revenue1:

The project covers booking information for a city hotel and a resort hotel including information such as when the booking was made, length of stay, the number of adults, children. The project went through the basic idea of the EDA and visualization process.

In this project I will do Exploratory Data Analysis on the given dataset. The project suggests measures that can be implemented to reduce cancellations and secure revenue. For example, hotels can offer discounts or promotions to customers who book early or who book for longer stays. Hotels can also offer incentives such as free parking or free breakfast to customers who book directly with them instead of through third-party websites.

This EDA involves following steps where in first step involves exploration and inspection over raw data, and second in second step I have dealt with data impurities and cleaned the data by andling null values and dropping irrelevent data from the dataset.

This EDA is divided into following 3 analysis: Univariate analysis: Univariate analysis is the simplest of the three analyses where the data, you are analyzing is only one variable. Bivariate analysis: Bivariate analysis is where you are comparing two variables to study their relationships. Multivariate analysis: Multivariate analysis is similar to Bivariate analysis but you are comparing more than two variables.

The project concludes that by analyzing hotel bookings data and understanding cancellations patterns, hotels can take steps to reduce cancellations and increase revenue.