Understanding Decision Trees and Their Applications – Prof. Sanjit Kumar Ghosh | ISME: Best MBA/PGDM, MCA, BBA, BCom, BCA, PhD Colleges in Bangalore

22nd January 2026

Medium link: https://medium.com/@sanjit.kr.ghosh12/understanding-decision-trees-and-their-applications-bedba0af32a7

Course Relevance: This caselet will be useful for PGDM – Advance machine learning and MCA, BCA Artificial intelligence course.

Academic Concepts: In this caselet, students will gain an understanding of what a decision tree is, its basic structure, various use cases, and how to implement a simple decision tree problem using Python.

Teaching Note:

The caselet introduces decision trees and it’s step by step implementation with python coding. It explains their flowchart-like structure, interpretability, and business relevance. A Python program was discussed demonstrating a simple loan approval prediction model. The caselet also highlights applications in customer segmentation, churn prediction, credit risk assessment, sales forecasting, operational efficiency, and fraud detection. It also discussed limitation of decision tree.

Learning Objectives:

By the end of this caselet, students should be able to:

Understand the basic concept and structure of a decision tree.
Steps involved in decision trees implementation
Interpret decision tree outputs and trace decision paths.
Implement a basic decision tree model in Python using scikit-learn.
Appreciate the role of decision trees in business analytics across domains.
Recognize limitations of decision trees and how ensemble methods overcome them.

Introduction to Decision Tree:

Decision tree is a supervised learning-based algorithm which can be used for both classification and regression-based problem. It is a kind of flowchart-like structure used to make decisions based on data. It starts with a root node (which is nothing but a question or condition), then it branches into possible outcomes, and finally ends in leaf nodes that represent final decisions or predictions. Each split is based on a feature or attribute in the given dataset, making the model easy to visualize.

In analytics, decision trees are used for both classification (e.g., to find out possible customer churn?) and regression (e.g., projection of next year sales?). Their simplicity and transparency make them especially valuable for stakeholders who need to understand and trust the logic behind data-driven decisions.

Why Decision Trees Matter in Data Analytics

Decision trees offer several advantages that make them ideal for business applications:

Easy to Understand: Decision tree resembles human way of thinking so it is easy to understand.
Data normalization not needed: They handle categorical and numerical data without extensive preprocessing.
Feature importance: Trees highlight which variables are most influential in decision-making.
Scalability: They can be applied to small datasets or scaled up using ensemble methods like Random Forests, it is easy to scalable.

These features make decision trees a useful tool for analysts, decision maker, marketers, financial planners.

Interpreting Decision Tree Results

One of the strengths of decision trees is it is easy to understand. Analysts can trace the path from root to leaf to understand how each decision is made. Let us consider a case for simple loan approval process, where a tree might show:

If income > ₹60,000 → check credit score
If credit score > 750 → approve loan
Else → reject loan

This transparency builds trust among stakeholders and facilitates compliance in regulated industries like finance and healthcare.

Python program for A simple decision tree program for predicting chances of Loan approval:

Objective:
To develop a machine learning model using a Decision Tree Classifier that predicts whether a loan application should be approved based on applicant-specific financial and employment details.

Inputs:

Credit Score, Annual Income, Loan Amount Requested,Employment Status (Employed/Unemployed)

Output:

Loan Approval Decision: Approved (1) or Not Approved (0)

Goal of this program,:
Enable real-time prediction of loan approval using user-provided inputs, trained on a sample dataset, to simulate decision-making in a simplified financial screening process.

Coding :

from sklearn.tree import DecisionTreeClassifier

# Format of Sample training data for decision tree: [Credit Score, Income, Loan Amount, Employment Status]

# Employment Status: 1 = Employed, 0 = Unemployed

X = [

[750, 50000, 10000, 1],

[600, 40000, 12000, 1],

[650, 30000, 8000, 0],

[720, 52000, 15000, 1],

[580, 26000, 5000, 0],

[690, 49000, 11000, 1],

[610, 35000, 9000, 0]

]

y = [1, 1, 0, 1, 0, 1, 0] # Different Loan approval labels

# Training the model with DecisionTreeClassifier

model = DecisionTreeClassifier()

model.fit(X, y)

# Code for Taking user input to predict status

print(“Enter Your details:”)

credit_score = int(input(“Credit Score (e.g., 700): “))

income = int(input(“Annual Income (e.g., 50000): “))

loan_amount = int(input(“Loan Amount Requested (e.g., 10000): “))

employment_status = int(input(“Employment Status (1 = Employed, 0 = Unemployed): “))

# Code for Predicting loan approval

user_data = [[credit_score, income, loan_amount, employment_status]]

prediction = model.predict(user_data)

# Code for result display

if prediction[0] == 1:

print(“Your Loan Likely to be Approved”)

else:

print(“Your Loan Likely to be rejected”)

Output:

Enter Your details:

Credit Score (e.g., 700): 500

Annual Income (e.g., 50000): 20000

Loan Amount Requested (e.g., 10000): 2000000

Employment Status (1 = Employed, 0 = Unemployed): 1

Your Loan Likely to be rejected

Key Applications of Decision Trees in Business

Let us discuss on how decision trees are useful across various business domains:

1. Usage of Decision tree in Customer Segmentation:

We can use decision trees to classify customers based on demographics, behavior, and purchase history. For example, a retail company might use a tree to identify high-value customers likely to respond to a loyalty program. The model could split customers by age, income, and frequency of purchase, helping managers to take corrective action accordingly.

2. Customer Churn Prediction

In different sector of business like in telecom, retaining customers is challenging. Decision trees can predict churn by analysing usage patterns, support interactions, and contract details of customer. A tree might reveal that certain customers with low engagement and frequent complaints are more likely to leave, this finding can promote proactive retention strategies.

3. Credit Risk Assessment

Banks and financial institutions are using decision trees to evaluate loan applications. By analyzing factors of customer like income, credit score, employment status, and debt-to-income ratio, decision trees can classify applicants into risk categories. This helps reduce default rates while maintaining customer inclusivity.

4. Sales Forecasting

Decision trees can model sales outcomes based on variables like seasonality, promotions, competitor pricing, and different economic indicators. For instance, a tree might show that sales spike during festival session when discounts exceed 20%, it can guide adopt promotional strategies.

5. Operational Efficiency

In supply chain management, decision trees help optimize inventory levels, delivery routes, and vendor selection. A logistics company might use a tree to decide whether to ship via air or ground based on distance, cost, and urgency.

6. Financial Fraud Detection

Today different E-commerce and financial platforms are using decision trees to flag suspicious transactions. By analyzing transaction amount, location, time, and user behavior, trees can identify inherent patterns which can give indication of fraud and thus enabling real-time alerts and interventions.

Building a Decision Tree: A Step-by-Step Overview

Creating a decision tree involves several steps:

Define the objective: What decision or prediction are you trying to make?
Collect and clean data: Ensure relevant features are available and accurate.
Choose a splitting criterion: There are two popular techniques for Attribute selection method (ASM), include Gini impurity and information gain.
Grow the tree: Split nodes recursively until stopping criteria are met (e.g., max depth, minimum samples).
Prune the tree: Remove branches that add little value. This process will useful to avoid overfitting.
Validate the model: Use cross-validation or test data to assess performance.

Tools like Python’s scikit-learn, R’s rpart, and platforms like RapidMiner or SAS make this process accessible to analysts with varying technical backgrounds.

Limitations and How to Overcome Them

Decision trees have limitations:

Overfitting: Trees can become too complex, capturing noise instead of signal.
Instability: Small changes in dataset can lead to different tree structures all together.
Bias toward dominant features: Decision Trees may favor features with more levels or variance.

To address these, we can use ensemble methods like Random Forests or Gradient Boosted Trees, which combine multiple trees to improve accuracy and robustness.

How Decision Tree can help other tools/models:

Decision trees can be integrated with broader analytics ecosystem. It can be combined with:

Clustering algorithms for customer segmentation
Time series models for forecasting
Natural language processing for analyzing text data
Dashboards and BI tools for visualization and reporting

This integration enhances the strategic value of decision trees

In near future automated machine learning (AutoML) and explainable AI (XAI) will make tree-based models more accessible and transparent

Conclusion: Decision trees play important role in business analytics. It is helping organizations to keep informed, transparent, and data-backed decisions. It can be used in different sector—from marketing to finance to operations. it makes them an important tool for any analyst or decision-maker.

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