Project overview
In this project, you’ll take on the role of a data analyst tasked with predicting condominium sale prices in New York City. Using property sales data, you’ll apply linear regression modeling techniques in R to determine how well the size of a condominium explains its sale price, both across the city as a whole and within individual boroughs.
Throughout the project, you’ll develop skills in data cleaning, exploratory analysis, and statistical modeling. You’ll identify and handle outliers, use data visualization to understand trends, and build and interpret linear regression models. The project culminates in a comparison of model performance between boroughs.
Objective: Use linear regression modeling in R to predict New York City condominium sale prices based on size, and compare model accuracy between different boroughs.
Key skill required
To complete this project, it's recommended to build these foundational skills in R
- Understanding the core concepts of predictive modeling
- Assessing variable relationships using scatterplots
- Fitting and interpreting linear regression models
- Evaluating the accuracy of linear regression models
Projects steps
Step 1: How Well does the Size of a Condominium in New York City Explain Sale Price?
Step 2: Understanding the Data
Step 3: Explore Bivariate Relationships with Scatterplots
Step 4: Outliers and Data Integrity Issues
Step 5: Linear Regression Model for Boroughs in New York City Combined
Step 6: Linear Regression Models for each Borough - Coefficient Estimates
Step 7: Linear Regression Models for each Borough - Regression Summary Statistics
Step 8: Next steps
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