Decision Tree Project
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.
What have we learnt so far
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
What are we going to do in this project ?
- 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.
Why solve this assignment?
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.
About the Dataset
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.