Introduction
This module follows the previous two modules within the Predict rocket launch delays with machine learning learning path.
Tip
For best results, work through this module right after you complete the previous module in this learning path: Data collection and manipulation. Otherwise, you might encounter errors or get different results. If this occurs, you can re-run the commands in the previous module before starting on this one.
Previously, you imported 300 rows of weather data representing 60 rocket launches plus the couple of days before and after a launch. Through a simplistic version of data cleansing and manipulation, you got your data to a place where you can start using machine learning algorithms to gather knowledge about it.
In this module, you'll use a decision tree classifier to gain knowledge from raw weather and rocket launch data. This module will focus on a local analysis of your data by using scikit-learn.
Tip
This module is part of a multimodal learning experience. Follow along with a video walkthrough of the module in a new tab.
Learning objectives
In this module, you'll begin to discover:
- The importance of column choosing.
- How to split data to effectively train and test a machine learning algorithm.
- How to train, test, and score a machine learning algorithm.
- How to visualize a tree classification model.