Bike Rental Sharing Demand Prediction with Machine Learning

Offered By
Coursera Project Network
In this Guided Project, you will:

Understand the theory and intuition behind Deep Neural Networks

Learn how to assess regression model performance using various KPIs such as RMSE, MSE, R2 and adjusted R2

Perform data cleaning, feature engineering and visualization

Clock2 hours
BeginnerBeginner
CloudNo download needed
VideoSplit-screen video
Comment DotsEnglish
LaptopDesktop only

In this 1-hour long project-based course, you will learn how to predict bike sharing demand with machine learning. Bike sharing services enable people to rent a bike from one location and drop it off at another location on an as-needed basis. The objective of this guided project is to predict bike sharing rental usage based on inputs such as temperature, season, humidity, wind speed. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Skills you will develop

  • Python Programming
  • Machine Learning
  • Deep Learning

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

  1. Understand the Problem statement and business case

  2. Import Libraries and Datasets

  3. Perform Data Cleaning

  4. Perform Data Visualization

  5. Divide the data into training and testing

  6. Train the model

  7. Evaluate Trained Model Performance

How Guided Projects work

Your workspace is a cloud desktop right in your browser, no download required

In a split-screen video, your instructor guides you step-by-step

Frequently asked questions

Frequently Asked Questions

More questions? Visit the Learner Help Center.