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https://github.com/patelabhi574/hotel_reservation_analysis

Analyzing data collected by hotel to make future prediction for the owner of what are the segments they are making most profit & also which are the patterns & trends which have been seen over the past years in the booking in different times throughout the year and price setting on the website in peak time as per availability index.
https://github.com/patelabhi574/hotel_reservation_analysis

data data-visualization datamodeling looker-studio powerbi reporting sql-query sql-server

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Analyzing data collected by hotel to make future prediction for the owner of what are the segments they are making most profit & also which are the patterns & trends which have been seen over the past years in the booking in different times throughout the year and price setting on the website in peak time as per availability index.

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# Hotel_reservation_analysis

It is important for hotels to be able to predict customer cancellations to optimize their operations and revenue management. By accurately forecasting cancellations, hotels can adjust their inventory, allocate resources efficiently, and offer vacant rooms to potential guests, reducing revenue loss and maximizing occupancy rates. Additionally, predictive cancellation models can help hotels improve customer satisfaction by proactively managing reservations and providing alternative options in case of cancellations, ensuring a seamless experience for all guests.



## Data Description 

- The dataset contains 36,275 instances and 19 attributes, of which 14 are quantitative input variables, 4 are qualitative input variables, and 1 qualitative output variable. Furthermore, the dataset contains no missing values and is in raw form.



## Insights I am looking for future prediction : Query question to SQL

- How do lead time and booking channel (e.g., online travel agency, direct booking) affect cancellation rates? How can the hotel use this information to optimize marketing and distribution strategies?

- Which factors are most predictive of cancellation, and how accurate are the predictions? How can the hotel use this information to improve its booking policies and procedures?

- Which months or seasons have the highest cancellation rates? How can the hotel use this information to optimize pricing and availability during these periods?

- How does the preferred room type vary across different market segments, and what is the distribution of bookings for each segment?

- Repeated Guest Bookings trend such as Room type preferred,weekdays or weekend bookings and cancellations.



# Findings 

## Hotel Reservation Analysis: Key Findings and Recommendations

### Booking Channels

- Online Bookings: Over 60% of total bookings.

- Offline Bookings: Just over 30% of total bookings.

- Action: Enhance online service quality and competitive pricing to attract more corporate bookings.



### Booking Cancellations

- Cancellation Rate: 36.5% for online bookings out of 23,000 total bookings.

- Action: Implement stricter cancellation policies to secure alternative bookings.



 ### Special Requests and Staffing



- Peak Period: Last six months of the year (2017-18) saw the highest number of special requests.

- Action: Increase hotel staff during these peak periods to improve service and response times.

  

### Lead Time

- Online Customers: Average lead time of 2.5 months.

- Offline Customers: Average lead time of 4 months.

- Action: Increase average online booking price by 15% (current average $112) to compensate for high cancellation rates.

  

### Seasonal Pricing

- High Demand Months: July, August, October.

- Action: Increase prices during these peak months to maximize revenue.

  

### Room Type Preferences

- Popular Rooms: Room types 1 and 3 are most preferred, especially for weekend bookings.

- Action: Consider expanding the number of these room types to meet customer demand.