At this point in our Linear Modeling in R course, you've learned about every step in a typical linear modeling workflow, and you have a solid understanding of the concepts that underlie linear modeling. Now it's time to apply that knowledge with a bigger end-to-end project to answer a real data science question: how can we accurately predict the sale price of a home or a condo?
Throughout this course, we've been working with some data about home sales in Brooklyn, New York, but in this project you'll be challenged to work with the full data set and build models for each of NYC's five boroughs. This will help you practice building, visualizing, and assessing many linear models at once.
Like all of our guided projects, we offer some structure and hints, but you're encouraged to take this project and make it your own. This would make an excellent project for a data science portfolio, so don't hesitate to take it in whatever direction interests you!
2. Understanding the Data
3. Explore Bivariate Relationships with Scatterplots
4. Outliers and Data Integrity Issues
5. Linear Regression Model for Boroughs in New York City Combined
6. Linear Regression Models for Each Borough - Coefficient Estimates
7. Linear Regression Models for Each Borough - Regression Summary Statistics
8. Next Steps