This project implements a multiple linear regression model with polynomial features to predict students' average scores based on their demographic and educational attributes. The model is trained on a dataset containing student performance data and evaluated using common regression metrics.
- Reads student data from a CSV file
- Calculates the average of math, reading, and writing scores
- Converts categorical variables using one-hot encoding
- Expands features using polynomial transformation
- Trains a multiple linear regression model
- Makes predictions on test data
- Evaluates the model using R² score and Mean Squared Error (MSE)
- Draws a graph to compare actual vs predicted average scores
- Python
- Pandas
- Scikit-learn
- Matplotlib
- Seaborn