Why Train Test Split Is Important in ML | Day 20 of Understanding ML #viral #shortvideo

We already know what training data and testing data are.

But why do we actually split data in Machine Learning?
And how does Train–Test Split make an ML model reliable?

In this video, I explain the real purpose of Train Test Split in a simple Hinglish way — without repeating basics.

You’ll learn:
• Why evaluating on training data gives fake results
• How random splitting works
• Why 70–30 or 80–20 split is used
• How Train Test Split prevents overfitting
• How we check model performance on unseen data

This is Day 20 of my “Understanding Machine Learning” series, where I break down ML concepts step by step using real logic instead of heavy theory.

📌 Topic: Model Evaluation
📌 Language: Hindi / Hinglish
📌 Level: Beginner Friendly
📌 Series: Understanding Machine Learning
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