Objective: Develop a Multiple Regression model to predict Student Performance Index based on several key factors.
Features: Hours Studied, Previous Scores, Sleep Hours, Number of practice papers practiced.
Model Development: Employ Multiple Variable Linear Regression techniques to establish a predictive model. The model will learn the relationship between these input features and the student's performance metric.
Conclusion: Achieved training cost of 220.20, CV cost of 230.12, and test cost of 226.96, beating the 368.51 benchmark.