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HousePricePrediction

In this project, I employed the Scikit-learn library to develop a supervised learning-based model that can predict the price of a house given its features including location, square footage of the house, number of bedrooms, etc.

At first, I split the dataset into training and test sets, and fit “random forest regression” and “decision tree regressor” models to the training set.

The performance comparison of the models indicated that the random forest regression algorithm can predict the prices more accurately in terms of mean absolute error.