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Predict House Prices with Machine Learning using Python
Regression model trained on 1,883 homes
Property valuation is an imprecise science. Individual appraisers and valuers bring their own experience, metrics and skills to a job. Consistency is difficult, with UK and Australian-based studies suggesting valuations between two professionals can differ by up to 40%. Crikey!
Perhaps a well-trained machine could perform this task in place of a human, with greater consistency and accuracy.
Let’s prototype this idea and train some ML models using data about a house’s features, costs and neighbourhood profile to predict its value. Our target variable — property price — is numerical, hence the ML task is regression. (For a categorical target, the task becomes classification.)
Watch the YouTube version here.
We’ll use a dataset from elitedatascience.com that simulates a portfolio of 1,883 properties belonging to a real-estate investment trust (REIT). There are 26 columns. Here’s a small snippet:
