Decision Tree in Data Science: A Step-by-Step Tutorial

Data Science consists of different types of algorithms for various use cases. The most commonly used supervised learning algorithm is the Decision tree. This blog is a perfect guide to get started with decision trees in data science.

In this blog, we will see the detailed description of the decision tree in data science, its application and uses, how it works, and implementation using Python. Let’s get started!

What is a Decision Tree in Data Science?

Decision Tree is a supervised learning algorithm in Data science. It follows a flowchart or tree branch-like pattern that helps in taking a series of decisions and their potential outcomes. Decision trees help solve classification and regression tasks. 

Key Applications of Decision Trees in Data Science

Decision trees in data science are mainly useful in handling classification problems like classifying objects into distinct categories based on their features, and regression problems like predicting a continuous output based on one or more input variables. Some of the key applications of decision trees are:

• Fraud Detection: It helps in detecting fraudulent transactions by analyzing patterns and identifying anomalies.
• Credit Scoring: It calculates the credit scores and assesses the creditworthiness of loan applicants.
• Product Recommendations: It is used to suggest products to customers based on their previous purchase history.
• Diagnosis: It can assist doctors in diagnosing any disease and provide the result based on analyzing the patient data and medical history.
• Inventory Management: It helps in tracking and managing the inventory by predicting sales trends and optimizing stock levels.

How Decision Trees Work

This section covers the structure and key components of the decision tree and its working step by step. The structure of the decision tree is visualized in the form of a tree with its nodes, branches, and leaves. Some of the key components of the decision tree are:

• Nodes: These are the building blocks or the data.
  • Root Node: It represents the entire dataset and is present at the top of the tree.
  • Internal Nodes: They represent the features that help make decisions.
• Branches: These are the lines that connect one or more nodes, indicating the flow of decisions.
• Leaves (Terminal Nodes): These are the outcome of the series of decisions.

Working of Decision Tree

To understand the working of decision trees, let us consider the following example for a classification problem. The diagram below shows a decision tree to find whether a person is fit or not by taking a series of decisions. As a first step, let’s look at the given conditions. 

• The first condition for a fit person is, they should be below 30 years old.
• The second condition is to check whether the person eats a lot of pizzas or exercises in the morning.
• Based on the two conditions, the decision tree will predict whether the person is fit or unfit.

Example 1: Let us consider a person who is 35 years old and does exercises in the morning. It follows the path below and predicts the person is Fit.

Example 2: Let us consider a person who is 28 years old and eats lots of pizzas. It follows the path below and predicts that the person is unfit.

Implementing Decision Trees in Data Science: Step-by-Step

In this section, we will see the implementation of a decision tree in data science using Python. 

Step 1: Import Libraries

For implementing a decision tree in Python, you need to import the decision tree model from scikit-learn, pandas for manipulating the data, and matplotlib for visualizing the data.

Step 2: Create / Import Dataset

The next step is to import the dataset into the code. For the above classification example to find whether the person is fit or not, we will create our sample dataset.

Step 3: Define Features and Label

The third step is to define the necessary input features that help predict the outcome. The label represents the output column.

Step 4: Train the Model

The fourth step is to train the decision tree model for the specific use cases. Here, we will train the decision tree for classification examples.

Step 5: Visualize the data

The next step is visualization. This is an optional step, but it will help explain your code or thought processes to others and stakeholders. Here, it visualizes the decision tree to predict if a person is fit.

Step 6: Test the Example

The last step is to test the model to find whether it is providing the expected results or not. For the given problem, we will test the above examples 1 & 2 and verify the predicted result. Here, the model will predict either 1 or 0. ‘1’ represents the person is “Fit” and ‘0’ represents the person is “Unfit”.

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Conclusion

In this blog, we explored the structure, components, key applications, working, and step-by-step implementation of decision trees in data science using Python. By translating real-world logic into a model, we demonstrated how decision trees make predictions through clear, rule-based paths using a simple classification problem to predict whether a person is fit or unfit. Happy learning!

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