Why Split Data?

To check how well the model works on unseen data (test set).

This ensures the model doesn't just "memorize" the data but can generalize to new situations.

How to Split Data

Usually, data is divided into two parts:

·?????? Training Set: 70–80% of the data (for learning).

·?????? Test Set: 20–30% of the data (for testing).

Example: Predicting Exam Scores

Suppose we have data for 10 students, showing:

·?????? Study hours (input/independent variable).

·?????? Exam scores (output/dependent variable).

Test Set (2 students): Use this to evaluate the model.

?

Splitting the Data

If we split this data into 80% training and 20% testing:

• Training Set (8 students): Use this to train the model

Output

• Training Data: Used by the model to learn patterns.
• Test Data: Used to check how well the model predicts scores.

Exercise:

1.??? Purpose of Splitting: Why is it necessary to split data into training and test sets in machine learning? Use an example to support your answer.

2.??? Avoiding Overfitting: How does splitting data into training and test sets help prevent overfitting, and why is this important for building a reliable model?

3.??? Testing Generalization: How does the test set help us evaluate whether the model can generalize to new, unseen data?

Example: split the data

How to split data in python

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