ID3 is mainly used in situations where the problem is a classification task and the data is
categorical in nature. Because ID3 produces easy-to-understand decision trees, it is widely
used in educational and real-life decision-making systems.
1️⃣ Education Domain
 Predicting student performance (Pass / Fail)
 Deciding eligibility for scholarships
 Analyzing attendance vs result
Easy rules like IF attendance is high AND marks are good → Pass make ID3 suitable here.

2️⃣ Medical Diagnosis
 Disease diagnosis based on symptoms
(Yes / No type decisions)
 Identifying risk levels (High / Medium / Low)
Doctors prefer interpretable models, and ID3 gives clear decision rules.

3️⃣ Weather-Based Decisions
 Predicting whether an outdoor event should be conducted
 Classic example: Play Tennis problem
Weather attributes are categorical (Sunny, Rainy, Windy), perfect for ID3.

4️⃣ Business Decision Making
 Customer segmentation
 Deciding loan approval (Approve / Reject)
 Credit risk analysis
ID3 helps convert data into simple if–else rules for managers.

5️⃣ Marketing & Customer Analysis
 Predicting whether a customer will buy a product
 Identifying target customers based on preferences
Categorical customer data works well with ID3.