In this Project I have used Boston housing data found in UCI Machine Learning Repository to build a model that can predict house prices given some information on the house and neighborhood. The Boston housing data was collected in 1978 and each of the 506 entries represent aggregated data about 14 features for homes from various suburbs in Boston, Massachusetts.
I trained a model using the decision tree algorithm and optimize the 'max_depth' parameter for the decision tree using grid search. The 'max_depth' parameter can be thought of as how many questions the decision tree algorithm is allowed to ask about the data before making a prediction.
Model performance is evaluated using R^2 value.
- Adimali Piyadasa