Predicting house prices in Iowa

Posted on Jan 11, 2019


This project was based on the Ames-Iowa Housing Dataset. The aim of the project was to apply different machine learning techniques to optimize house pricing predictions.


  • Imputing missing values
  • dummying ordinal and categorical variables.
  • Stepwise Forward Feature Selection usingĀ  BIC (Bayesian Information Criteria)
  • Evaluation of RMSE (Root mean squared error) using Ridge,Lasso and Random Forest algorithm.



About Author


karim El Zaatari

Data Scientist and mechanical engineering graduate with a demonstrated record of leadership & problem solving. My data science projects span over various topics including air pollution, carpooling,house pricing and machine learning in horse racing.
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