In this project we are going to implement decision tree methods. It is a predictive model based on a branching series of Boolean tests. It breaks down a Dataset into smaller and smaller subsets while at the same time an associated decision tree is incrementally developed. The final result is a tree with decision nodes and leaf nodes. Let's try to solve both a regression problem and a classification problem using decision trees.

We have seen how to clean the data and how to select features and learnt how to apply the following:

• Feature Engineering
• Feature Selection
• Linear Regression
• Logistic Regression

• We are going to implement decision tree methods as for both regression and classification problem.
• We will observe how the model learns and performs with the data set given.

By the completing this Assignment :-

• You will get hands-on practice on how decision tree is performing for both classification and Regression and how it is different from the Linear regression and Logistic Regression
• Implementation of Grid search CV and Randomized search CV.
• You will get to learn how hyper parameter tuning helps in model performance.

For Decision tree Regressor

• We are using the same Dataset of House prices, we had used for Linear Regression.

For Decision tree Classifier

• We are using the same Dataset of Loan Prediction, we had used it earlier in Logistic Regression.