Hotel_Booking-Analysis

About Dataset:

This dataset contains 119390 observations for a City Hotel and a Resort Hotel. Each observation represents a hotel booking between the 1st of July 2015 and 31st of August 2017, including booking that effectively arrived and booking that were canceled.

Business Problem:

In recent years, City Hotel and resort Hotel have seen high cancellation rates. Each hotel is now dealing with a number of issues as a result, including fewer revenues and less than ideal hotel room use. Consequently, lowering cancellation rates is both hotels primary goal in order to increase their efficiency in generating revenue, and for us to offer thorough 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.

Analysis Project Steps:

1. Create a Problem Statement
2. Identify the data you want to analyze.
3. Explore and clean the data
4. Analyze the data to get useful insights.
5. Present the data in terms of reports or dashboards using visualization.

Assumptions:

1. No unusal occurrences between 2015 and 2017 will have a substantial impact the data used.
2. The information is still current and can be used to analyze a hotel's possible plans in an efficient manner.
3. There are no uaanticipated negatives to the hotel employing any advised technique.
4. The hotels are not currently using any of the suggested solutions.
5. The biggest factor affecting the effectiveness of earning income is booking cancellatons.
6. Cancellations resultin vacant rooms for the booked length of time.
7. Clients make hotel reservations the same year they make cancellations.

Research Questions:

1. What are the variables that affect hotel reservation cancellations?
2. How can we make hotel reservations cancellations better?
3. How will hotelsbe assisted in making pricing and promotinal decisions?

Hypothesis:

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