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When a customer places an order, the order may or may not be canceled later. To assist the hotel in minimizing losses it is necessary to analyze and predict the factors that lead customers to cancel their orders using machine learning model.

Home Page: https://drive.google.com/file/d/10-vEe8LrDyqL3rQ5xvTKSCPkE1MsJ-xp/view?usp=sharing

Jupyter Notebook 100.00%
classification datascience gradientboosting hotelbooking logisticregression machinelearning randomforest

hotel_cancelation_order's Introduction

Don't Cancel!

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.

Dataset

83.293 Rows and 33 Column

Objective

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

Recommendations Action

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.

Machine Learning

I use 3 models of Classification with Hyper Parameter Tuning Machine Learning:

  1. Logistic Regression
  2. Random Forest Classifier
  3. Gradient Boosting Classsifier

App Screenshot

After comparing 3 models hyper parameter tuning

App Screenshot

Gradient Boosting Classifier (with 1,000 estimators, minimum samples split of 5, and a maximum depth of 10) is the best model!

Full Presentation

You can see the full presentation of this project at:

https://drive.google.com/file/d/10-vEe8LrDyqL3rQ5xvTKSCPkE1MsJ-xp/view?usp=sharing

Reference

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

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