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.
Data Science Project Capstone: Predicting Bicycle Rental
Data Science Project Capstone: Predicting Bicycle Rental
This course is part of Data Science Foundations Specialization
Instructor: Robert Zimmer
Access provided by ExxonMobil
1,892 already enrolled
Gain insight into a topic and learn the fundamentals.
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Beginner level
No prior experience required
7 hours to complete
Flexible schedule
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What you'll learn
In this course you will tackle a prediction problem: forecasting the number of bicycles that will be rented on a given day.
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Assessments
10 assignments
Taught in English
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This course is part of the Data Science Foundations Specialization
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