Procedure

Step 1: Click on the "Upload Dataset" button, select the CSV file, and ensure it contains the columns: ID, Student Name, Study Hours, Attendance %, Previous Score, and Exam Score.

Step 2: Once the file is uploaded, a Dataset Preview will be displayed below the upload section. Click on the "NEXT" button to proceed to Data Cleaning.

Step 3: Click on the "Clean & Update Dataset" button to clean the dataset and view the Cleaned Dataset. Then, click on the "NEXT" button to proceed to Data Splitting.

Step 4: Adjust the slider to set the appropriate train-test split for the dataset. A common split is 80/20 for training and testing. After splitting, click on the "NEXT" button to apply Linear Regression to the data.

Step 5: Apply Linear Regression to the training and testing data to analyze the relationship between features and the target variable. Evaluate the R² values and, if there is a significant difference between training and testing R² scores, proceed to apply Lasso Regression for improved model performance.

Step 6: Apply Lasso Regression for improved model performance. Adjust the alpha value to find the best alpha and corresponding R² values. Compare the results to determine which model performs better. Click on "Go to Ridge Regression" to proceed and pass the data to Ridge Regression for further evaluation.

Step 7: Apply Ridge Regression and adjust the alpha value to find the best R² value. Click on "Compare All Regressions" to analyze and compare the results for selecting the most optimal model.

Step 8: Compare all regression models by analyzing their R² values and identify the best performing model. Highlight the most suitable regression based on the highest R² and performance on the dataset.