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