Using Data to Predict Prices of Houses in Ames, IA

Posted on Jul 30, 2022

The skills the author demonstrated here can be learned through taking Data Science with Machine Learning Bootcamp with NYC Data Science Academy.

Data Science Introduction

Using Data to Predict Prices of Houses in Ames, IA

The following website was created in Streamlit to automatically generate a web-based interactive dashboard from Python code. Modeling Home Prices and Value Added from Improvements in Ames, IA. It utilized a data set from 2020 of all the prices of houses and their unique features in order to develop a machine learning prediction model utilizing ElasticNet at more than 93% accuracy.

The following features were used to estimate the average price of a house in a particular area and neighborhood of the town:

  1. Number of Rooms
  2. Number of Bathrooms
  3. Number of Garage Car Spaces
  4. Ground Living Area (square feet)
  5. Lot Area (square feet)
  6. Lot Frontage (linear feet)
  7. Overall Quality of House (on a scale from 1 to 9)
  8. Approximate Age (years)
  9. Presence of Basement
  10. Presence of Paved Driveway
  11. Whether or Not Remodeled

The following proposed improvements were used to determine how much added value a particular house in a specific area and neighborhood would have if implemented:

  1. Remodeling Exterior Material
  2. Remodeling Kitchen
  3. Building Pool
  4. Finishing Basement
  5. Finishing Garage

A variety of machine learning regression models such as Multilinear, Logistic, Huber, Penalized, Ridge, Lasso, Random Forest, and Support Vector Machines (SVM) were attempted but ElasticNet turned out to be the most accurate and easily implemented one. In conclusion, a combination of quantitative and ordinal categorical features working together to determine the overall price of a house and its improvements made it ideal for this task.

About Author

Joydeep Chatterjee

Creative data scientist who is passionate about applying machine learning to model physical processes, logistics, and business activities. Integrating over 15 years of civil engineering experience to blend technical and leadership background with data science skills to solve...
View all posts by Joydeep Chatterjee >

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