Hello there! Today, we're going to explore Gradient Boosting, a powerful technique that improves the accuracy of machine learning models. Our goal is to understand what Gradient Boosting is, how it works, and how to use it with a real example in Python. By the end, you'll know how to implement Gradient Boosting and apply it to a dataset.

Gradient Boosting is an ensemble technique that combines multiple weak learners, usually decision trees, to form a stronger, more accurate model. Unlike Bagging and Random Forests, which create models independently, Gradient Boosting builds models sequentially. Each new model aims to correct errors made by the previous ones.

Imagine baking a cake. The first cake might not be perfect — maybe too dry or not sweet enough. The next time, you make changes to improve it based on previous errors. Over time, you get closer to perfection. This is how Gradient Boosting works.

Here's a step-by-step explanation of Gradient Boosting:

1. Start with an initial model: This can be a simple model like a single decision tree.
2. Calculate errors: Find out where the initial model makes mistakes.
3. Build the next model: Create a new model that focuses on correcting the errors from the initial model using gradients.