Objective

• Build Machine Learning models using hotel reservation dataset and demonstrate the use case on hotel booking demand analysis & cancelation prediction.
• To demonstrate the usage of AutoML library to develop the "first-glimpse" model using PyCaret.

Dataset

• This data set contains booking information for a city hotel and a resort hotel and includes information such as when the booking was made, length of stay, the number of adults, children, and/or babies, and the number of available parking spaces, among other things.
• Data source: The original data source can be downloaded from Antonio et al. (2019): Hotel booking demand datasets
• Raw Dataset: hotel_bookings.csv
• Processed Dataset: hotel_bookings_v1

Tools

• Jupyter Notebook
• PyCaret: Python package for AutoML, install using pip install. Read about PyCaret here
• ML model: K-means clustering, LightGBM classifier, Random Forest regressor

Notebook

• 01 - Data Processing.ipynb
• 02 - Exploratory Data Analysis (EDA).ipynb
• 03 - Unsupervised Learning - Clustering - K-means.ipynb
• 04 - Supervised Learning - Classifier - LightGBM.ipynb
• 05 - Supervised Learning - Regressor - Random Forest.ipynb
• Notebook can be downloaded from here