## Hotel Booking Cancellations Prediction

Booking cancellations can have a significant impact on a hotel's revenue and bottom line. When a reservation is cancelled, the hotel may not be able to book that room again, resulting in lost revenue. To mitigate this risk, hotels often require a booking deposit, which is usually a percentage of the reservation's full price. However, requiring a deposit can also have negative consequences, such as *dissuading some customers from booking at the hotel altogether*.

To address this challenge, our Kaggle competition aims to **build a predictive model that can accurately determine whether a hotel booking will be cancelled or not**. By predicting cancellations in advance, hotels can implement risk-aware strategies for calculating deposit sizes and making other operational decisions that help them **maximize occupancy rates and revenue**.

### The Challenge

In this competition, you will work with **real-world anonymized data from two different hotels** to develop a model that predicts whether a booking will be cancelled or not. The data set includes a range of features, such as:

- The booking date
- The length of the stay
- The number of guests
- And other relevant information that can help inform the model's predictions

Your challenge is to create a **classifier that accurately predicts whether a given booking will be cancelled or not**.

### The Goal

By participating in this Kaggle competition, you will have the opportunity to:

- Apply your machine learning skills to a **real-world business problem**
- Develop **innovative solutions** that can help hotels improve their revenue and bottom line
- **Showcase your skills** and learn from other participants

Whether you're an experienced data scientist or a newcomer to the field, this competition offers a challenging and rewarding opportunity to showcase your skills and learn from other participants. 

So, **join us today and start building your predictive model for hotel booking cancellations!**

# Data 

[Data in Brief](https://www.sciencedirect.com/science/article/pii/S2352340918315191)