Predicting House Prices Using Linear regression

Main Problem and Objectives

• Build a prediction model for house pricing in Ames, IA
• Where are most sales taking place?
• Where are the most expensive houses located?
• Discuss Possible Improvements

Describing the Data and Limitations

• Target Prediction Feature: Sale Price
• Number of Instances: 1460
• Number of Attributes Allowed: 18
• Years of data collected: 2006 - 2010
• Missing

Limitations

• The attributes provided are not necessarily the best indicators of the house pricing
• The data collected is mostly around a particular unstable time in the market

Understanding the Data

Looking at Correlations Quality & Price Correlation

Looking at Sale Prices Across Neighborhoods

Where are the most sales happening?

• Most Sales Happening in :
  • North Ames
• How many happened?
  • 225 (About 15.41 % of total sales)

Where are the most expensive homes?

Creating a Regression model

• Type of Regression: Linear
• Attributes Dropped: Utilities
• Dummy Variable Selection: All except Lot Area and GrLivArea
  • Accuracy Testing:
  • R Squared = 0.899
  • Mean Absolute Error: 16261.24
  • Mean Squared Error: 637850318.41
  • Root Mean Squared Error: 25255.70
  • Cross Validation Score: 0.738
• Limitations of the Model

There were a lot of outliers in the data that have caused the RMSE and MSE to be quite high The cross validation score was lower than the initial model made, although R Square was improved this could be a sign of some over fitting on this model.

Possible Improvements

• More location based metrics such as surrounding business’, schools, police stations, etc

• More insight into the overall condition and quality metric

• More data points for expensive homes, to improve predictions on the expensive homes.