Classification in Data Mining 📁🎯

In data mining, Classification is like being a Librarian. You take a new book (data point) and you have to decide which shelf (category) it belongs on based on its features.

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1. Meaning of Classification

Classification is a form of data analysis that extracts models describing important data classes. These models (called classifiers) are used to predict categorical labels (e.g., "Safe/Risky," "Spam/Ham," "Win/Loss").

• It is Supervised Learning because the computer is "supervised" by an existing dataset where the categories are already known.

2. The Classification Process

Classification is not a one-step event; it is a cycle that consists of two main phases:

1. Learning Phase (Training):
   • The algorithm analyzes a Training Set (data that already has labels like "Spam" or "Not Spam").
   • It creates a "Knowledge Set" or a mathematical model that describes the rules of the data.
2. Testing Phase (Validation):
   • The model is applied to a Test Set (new data the computer hasn't seen before).
   • If the model correctly predicts the category for 90% of the test data, it is considered "Accurate" and ready for real-world use.

Where do we see this in action?

• Banking: Predicting if a loan applicant is likely to default (No) or repay (Yes).
• Healthcare: Classifying medical images as showing a disease (Malignant) or being healthy (Benign).
• E-commerce: Classifying customers into "Likely to buy" or "Just browsing."
• Security: Identifying if a login attempts is from a "Legitimate user" or a "Hacker."

Summary

• Classification assigns data to specific, pre-defined categories.
• It requires Training Data to learn the rules.
• It is a multi-step process: Training -> Testing -> Using.
• It is the foundation of most modern Decision Support Systems.

Quiz Time! 🎯

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