This course is the seventh of eight. In this project, we will tackle a prediction problem: forecasting the number of bicycles that will be rented on a given day. Using historical data, we will consider factors such as weather conditions, the day of the week, and other relevant variables to accurately predict daily bicycle rentals. This will help ensure that our bicycle rental service is prepared with the appropriate number of bicycles each day. We will learn specifically about data acquisition and correlation.
Overview
Syllabus
- Week 1: First Steps and Correlation
- Welcome to an exhilarating week in our Data Science journey, where we transition from theory to practice with the commencement of our capstone project. We will immerse ourselves in the intricate world of predictive modeling using linear regression, aiming to forecast bicycle rentals. You will learn specifically about data acquisition and correlation.
- Week 2: Finalising the Data Science Project
- This week, we will complete the Data Science Project. You will learn about single and multiple linear regression and apply these models to solve the bicycle rental problem.
Taught by
Robert Zimmer
