Predicting Boston Housing Prices

In this project, I applied basic machine learning concepts on data collected for housing prices in the Boston, Massachusetts area to predict the selling price of a new home. I first explored the data to obtain important features and descriptive statistics about the dataset. Next, I properly split the data into testing and training subsets, and determine a suitable performance metric for this problem. Then I analyzed performance graphs for a learning algorithm with varying parameters and training set sizes. This enabled me to pick the optimal model that best generalizes for unseen data. Finally, I tested this optimal model on a new sample and compare the predicted selling price to my statistics.

The main techniques used:

  • Evaluating Model performance
  • Model Evaluation & Validation
  • Model Optimization

You can see the code(iPython notebook) there.

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