EDA-Hotel-Booking-Python
EDA - Hotel Booking Using Python
Overview
This project aims to conduct an exploratory data analysis (EDA) on a dataset of hotel bookings to gain insights on booking patterns and customer behaviour. The analysis will focus on dentifying patterns in bookings and cancellations, customer demographics and preferences, and the effectiveness of different distribution channels.
The dataset used for this project is available here.
[Built using jupyter notebook and Python 3]
View presentation on Youtube
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Recommendations
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Increase staffing and inventory during peak booking months (July and August) to accommodate increased demand.
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Optimize pricing and availability of room types based on customer preferences, with a focus on making room type A available during peak months and adjusting pricing accordingly.
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Improve the online booking process and customer support to increase customer satisfaction.
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Offer popular amenities and services, such as breakfast in bed, more often or as a package deal to increase revenue and customer satisfaction.
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Focus on attracting customers with shorter stays, as they generate higher ADR.
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Allocate resources based on average stay duration, which is relatively higher from June to September.
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Encourage customers to make a deposit by offering additional incentives and reduced rates, to reduce cancellation rates.
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Direct marketing efforts towards online and social media channels to capture more bookings.
Conclusion
In summary, the hotel booking data can be used to gain insights into booking patterns and customer behavior, which can inform strategies for increasing occupancy, revenue, and customer satisfaction.
The hotel should focus on increasing staffing and inventory during peak booking months, improve pricing strategy, improve customer support, offer popular amenities and services as add-ones during booking, build relationships with online travel agents, encourage deposits during booking also direct marketing towards online and social media.
By implementing these strategies, the hotel management will be able to achieve their business objectives.
Scope
Further analysis needs to be carried out to improve customer satisfaction by incorporating customer feedback in the dataset With the available data a machine learning model can be created to predict room cancellations or booking trends