[EDA] Exploratory Data Analysis | Data Science Capstone Project | [In English]

Hotel Booking Analysis | EDA | Exploratory Data Analysis

Hotel bookings depend on many factors such as type of hotels, seasonality, days of the week, meals available, parking spaces, charges etc. Hence analysing the patterns available in the past data is very important to help the hotels plan well accordingly in order to benefit the business. The given data set contains booking information for a city hotel and a resort hotel and includes information such as when the booking was made, the number of adults, children, and/or babies, and the number of available parking spaces etc, we tried to understand the customer's behaviour and usage patterns in the data to help hotel managements improve the business by making better decisions. The sequence of processes carried out to analyse the data is mentioned below:
• Data Preparation - It includes basic inspection of the raw data, Data Cleaning by handling missing values. We also treated outliers first by the capping method, we defined some thresholds to cap some of the categorical features, then we handled the outliers in the remaining features by standard IQR method, and after that, we did some feature engineering in order to understand the patterns in latent features.
• EDA - We performed univariate, Hotel Wise comparison, bivariate, multivariate
and correlation analysis by plotting some visualizations and data wrangling to find
out useful insights and make overall inferences to reach a conclusion.
We encountered the following patterns in the given historical data:
• Top Hotel - City Hotel. Top Agent - Agent No. 9. Top room type - A
• One out of every three bookings is cancelled.
• People prefer to tour more in August.
• Most preferred meal is BB (Bread and Breakfast.
• Online marketing is the best way to attract customers.
• People do not want to pre-deposit the money for booking.
• Only 10% of people require parking space.
• Most of the visitors are couples.
• The Resort hotel is preferred mostly for longer stays, and daytime stays. and when
the parking space is needed.
• More than 15 days advance bookings have a high chance of cancellation.
• Assigning a different room is not a reason for cancellation.
• Direct bookings have very less cancellation%.
• Best time to book a hotel is in January.
• Average days in advance booking: 77 days
• Average nights spent by visitors: 3.
• Most visitors are from Portugal, Britain, France, Spain and Germany.
• Total Special requests and the revenue depends more on the total members arrived.
In this project, we also analysed the factors affecting the hotel bookings which may be
useful to predict future bookings, cancellations and the number of special requests.

GitHub Link: - https://github.com/s-kp/CapstoneProject-EDA

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