student performance

Import student performance data

Handle missing study hours or scores

Remove duplicate records

Remove extreme values

Convert 'Extracurricular Activities' from categorical (Yes/No) to numeric (0/1) for regression analysis

Train a Linear Regression Learner on the training partition to predict Performance Index

Select relevant features and the target variable (Performance Index) for the model

Partition data into a training set (70%) and a test set (30%) using random sampling

Normalize features on the training set (Min-Max) and apply the exact same transformation to the test set using Normalizer (Apply) to ensure consistency.

Apply the trained model to the test set to generate predictions

Evaluate the model's predictive accuracy on the test set by calculating $R^{2}$, Mean Absolute Error (MAE), and Mean Squared Error (MSE).