In this decision trees course, we started by building intuition for decision trees and random forests and how they're utilized to make predictions by following "decisions" in the data.

In this guided project, you'll try to predict the total number of bikes people rented in a given hour. You'll also predict the total number of bike rentals. To accomplish this, you'll create a few different machine learning models and evaluate their performance.

While practicing what you learned in this course, we'll be working with a dataset that describes detailed data on the number of bicycles people rent by the hour and day from a communal bike sharing station in Washington D.C.

Working on guided projects like this will give you hands-on experience with real world examples, so we encourage you to not only complete them, but to take the time to really understand the concepts.

These projects are meant to be challenging to better prepare you for the real world, so don't be discouraged if you have to refer back to previous lessons. If you haven't worked with Jupyter Notebook before or need a refresher, we recommend completing our Jupyter Notebook Guided Project before continuing.

As with all guided projects, we encourage you to experiment and extend your project, taking it in unique directions to make it a more compelling addition to your portfolio!

Objectives

  • Learn to create new features.
  • Learn to apply different machine learning models.

Lesson Outline

1. Introduction to the Data Set
2. Calculating Features
3. Splitting the Data Into Train and Test Sets
4. Applying Linear Regression
5. Applying Decision Trees
6. Applying Random Forests
7. Next Steps