# 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.