Decision tree is a decision making tool in python for data science that uses a flowchart or is a model of decisions and all the possible outcomes, including cost and utility.

The decision tree algorithm comes under a supervised learning algorithm. It works efficiently for both continuous and categorical output variables.

The results are reflected on the branches of the node, and the nodes will have either:

- Conditions or decision nodes
- Result or end nodes.

The branches represent truth or falsity of a statement, and it makes a decision which is based on it. The below example represents a decision tree that will evaluate the smallest of three numbers.

**Decision tree regression:**

It observes the features of an object and then trains a model in the structure of a tree for predicting data in the future for producing meaningful continuous output. By continuous output we mean the result is not discrete.

**Discrete output examples: **We will make a model for the prediction of rain on a particular day.

**Continuous output example: **It is a profit prediction model which will state the probable profit that can be generated from the sale of a product.

In this, the continuous values are predicted with a decision tree regression model.

Now we will observe the Step by step implementation.

**Step 1:**Importing the required libraries.**Step 2:**Initializing and printing the data set.**Step3:**Selecting all the rows and column 1 from the data set to “X”.**Step 4:**Selecting all rows and column 2 from the data set of “y”.**Step5:**Fitting the decision tree regressor to the data set.**Step 6:**Prediction of a new value.**Step 7:**Visualization of the result.**Step8:**The tree is exported and shown n a tree structure below.

OUTPUT:

So, to learn more about it in python for data science, you can check this and this as well.