This is a Machine Learning Model to Predict Hotel Booking Cancellation.
In this digital era, it has become easier to make hotel reservations online. However, this ease of use also has a downside, as it makes cancellations by customers easier, even when the hotel has already made preparations.
To assist the hotel in minimizing losses it is necessary to analyze and predict the factors that lead customers to cancel their orders using Data-Driven Prediction of Customer Order Cancellation.
83.293 Rows and 33 Column
1. Exploratory Data Analysis
Analysis the factors that lead customers to cancel their orders and make recommendations to minimizing failed transaction
2. Supervised Machine Learning (Classification)
Make model to predict hotel cancellations orders
Extracted valuable insights related to hotel sales and provided recommendations for four segmentation factors (ADR, hotel, customer, and market segment) to reduce the hotel's failed transaction rate by 1.65%, utilizing exploratory data analysis and data visualization.
I use 3 models of Classification with Hyper Parameter Tuning Machine Learning:
- Logistic Regression
- Random Forest Classifier
- Gradient Boosting Classsifier
After comparing 3 models hyper parameter tuning
Gradient Boosting Classifier (with 1,000 estimators, minimum samples split of 5, and a maximum depth of 10) is the best model!
You can see the full presentation of this project at:
https://drive.google.com/file/d/10-vEe8LrDyqL3rQ5xvTKSCPkE1MsJ-xp/view?usp=sharing
https://www.kaggle.com/code/aminizahra/hotel-booking-analysis https://www.kaggle.com/code/asadxio/hotel-booking-cancellation-analysis https://www.kaggle.com/code/abaliyan/hotel-booking-eda https://github.com/ReyhanR/Hotel-Cancellation-Booking-Prediction-Project/tree/master