• This project coming after the exploratory students scores analysis which forms the conclusion that the math course it was difficult for all the students. I created a tool that estimate scores( MAE 4.19 grade) in order to help the students strategy before the math exams.
• I found the data set at Kaggle with over than a 1000 students scores in three different courses
• Optimized Multiple Linear, Lasso and Random Forest Regression in order to reach the best model
Initially, I transformed the categorical variables into dummy variables. In addition, I split the data into training and test split with 20% test size.
I tried tree different models and evaluated them with the Mean Absolute Error. I choose the MAE because is easy to interpret the results and it fits nice at that type of problems.
As I mentioned above I tried:
• Multiple Linear Regression
• Lasso Regression
• Random Forest
Linear Regression: MAE = 4.21
Lasso Regression: MAE = 4.19
Random Forest: MAE = 4.60