Gradient Boosting Machines (GBMs) with XGBoost

This tutorial is a part of Machine Learning with Python: Zero to GBMs and Zero to Data Science Bootcamp by Jovian

The following topics are covered in this tutorial:

• Downloading a real-world dataset from a Kaggle competition
• Performing feature engineering and prepare the dataset for training
• Training and interpreting a gradient boosting model using XGBoost
• Training with KFold cross validation and ensembling results
• Configuring the gradient boosting model and tuning hyperparamters

Let's begin by installing the required libraries.