Advanced Regression to Predict Housing Prices (Kaggle Competition)

Posted on Mar 12, 2018

Project Overview:

This project was a team effort along with Kweku Ulzen, Michael Chin, and Matthew Sun to enter into the Housing Prices in Ames, Iowa Kaggle Competition.

The goals included:

  • Maximize the predictive power of our machine learning models to predict the sale prices of houses based on a given set of features.
  • Code a feedback system that would allow the team to easily experiment with subtle changes to the vast variety of model tuning.

Approach:

The team set up a shared repository on Github, and set about initial dataset exploratory analysis independently, and sharing results frequently.

We came to a final model by following a cycle of data exploration, data engineering, feature selection, model selection and tuning, testing, and analyzing results.

Read our presentation here.

 

 

About Author

Dean Goldman

Dean Goldman is based in New York City. He is a creative thinker with experience in web programming, data science, and design. Seeking to apply skills in problem solving, coding, and data analytics.
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