- Implement a decision tree to predict housing prices for the given dataset using the available features.
- The various attributes of the data are explained in the file data description.txt. Note that some attributes are categorical while others are continuos.
- Feel Free to use Python libraries such as binarytree or any other library in Python to implement the binary tree. However, you cannot use libraries like scikit-learn which automatically create the decision tree for you.
- Experiment with different measures for choosing how to split a node(Gini impurity, information gain, variance reduction) . You could also try different approaches to decide when to terminate the tree.
- Report metrics such as Mean Squared Error(MSE) and Mean Absolute Error(MAE) along with any other metrics that you feel may be useful.
Decision_Tree
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Decision_Tree
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