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This project utilizes Python and various libraries like pandas, matplotlib, and seaborn to examine hotel booking cancellations and other unrelated factors. The aim is to boost revenue generation efficiency and provide valuable business recommendations.

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ruchi020897/Hotel_Booking_Data_Analysis_Using_Python

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Download the complete report from here: Hotel Booking Report

Overview

The primary goal of this project is to analyze the hotel booking cancellations as well as other factors that have no bearing on their business and yearly revenue generation to increase their efficiency in generating revenue, and for us to offer thorough business advice to address this problem.

You can download the dataset from here: Hotel Booking Dataset

Business Problem

In recent years, City Hotel and Resort Hotel have seen high cancellation rates. Each hotel is now dealing with several issues as a result, including fewer revenues and less than ideal hotel room use. Consequently, lowering cancellation rates is both hotels' primary goal to increase their efficiency in generating revenue, and for us to offer thorough business advice to address this problem. The analysis of hotel booking cancellations as well as other factors that have no bearing on their business and yearly revenue generation are the main topics of this report.

Assumptions

  • The information is still current and can be used to analyze a hotel's possible plans in an efficient manner.
  • There are no unanticipated negatives to the hotel employing any advised technique.
  • The hotels are not currently using any of the suggested solutions.
  • The biggest factor affecting the effectiveness of earning income is booking cancellations.
  • Cancellations result in vacant rooms for the booked length of time.
  • Clients make hotel reservations the same year they make cancellations.

Research Questions

  • What are the variables that affect hotel reservation cancellations?
  • How can we make hotel reservations cancellations better?
  • How will hotels be assisted in making pricing and promotional decisions?

Hypothesis

  • More cancellations occur when prices are higher.
  • When there is a longer waiting list, customers tend to cancel more frequently.
  • The majority of clients are coming from offline travel agents to make their reservations.

Insights Gained through Analysis

  • There are a significant number of reservations that have not been cancelled. There are still 37% of clients who cancelled their reservation, which has a significant impact on the hotels' earnings.
  • In comparison to resort hotels, city hotels have more bookings. It's possible that resort hotels are more expensive than those in cities.
  • On certain days, the average daily rate for a city hotel is less than that of a resort hotel, and on other days, it is even less. It goes without saying that weekends and holidays may see a rise in resort hotel rates.
  • Both the number of confirmed reservations and the number of cancelled reservations is largest in the month of August, whereas January is the month with the most cancelled reservations.
  • Cancellations are most common when prices are greatest and are least common when they are lowest. Therefore, the cost of the accommodation is solely responsible for the cancellation.
  • The top country with the highest number of reservation cancellations is Portugal.
  • Around 46% of the clients come from online travel agencies, whereas 27% come from groups. Only 4% of clients book hotels directly by visiting them and making reservations.

Suggestions

  • Cancellation rates rise as the price does. To prevent cancellations of reservations, hotels could work on their pricing strategies and try to lower the rates for specific hotels based on locations. They can also provide some discounts to the consumers.
  • As the ratio of the cancellation and not cancellation of the resort hotel is higher in the resort hotel than the city hotels. So, the hotels should provide a reasonable discount on the room prices on weekends or on holidays.
  • In the month of January, hotels can start campaigns or marketing with a reasonable amount to increase their revenue as the cancellation is the highest in this month.
  • They can also increase the quality of their hotels and their services mainly in Portugal to reduce-the-cancellation-rate.

About

This project utilizes Python and various libraries like pandas, matplotlib, and seaborn to examine hotel booking cancellations and other unrelated factors. The aim is to boost revenue generation efficiency and provide valuable business recommendations.

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