Prepare a Classification model using decision tree Classifier.

A decision tree is a flowchart-like tree structure where an internal node represents a feature(or attribute), the branch represents a decision rule, and each leaf node represents the outcome.

The topmost node in a decision tree is known as the root node. It learns to partition on the basis of the attribute value. It partitions the tree in a recursive manner called recursive partitioning. This flowchart-like structure helps you in decision-making. It's visualization like a flowchart diagram which easily mimics the human level thinking. That is why decision trees are easy to understand and interpret.

 

 Step-I; Import the libraries 

Step-II: Read and Display the data set and display it

Step-III: Change string values to numerical 

To make a decision tree, all data has to be numerical.

We have to convert the non numerical columns 'Nationality' and 'Go' into numerical values.

Pandas has a map() method that takes a dictionary with information on how to convert the values.

{'UK': 0, 'USA': 1, 'N': 2}

Means convert the values 'UK' to 0, 'USA' to 1, and 'N' to 2.

 Step-IV:

Then we have to separate the feature columns from the target column.

The feature columns are the columns that we try to predict from, and the target column is the column with the values we try to predict.

Step-V:  Now we can create the actual decision tree, fit it with our details.

 The output is