Build an app to predict Student Grades - Certification

Full Name:

Email:

Phone Number:

How did you complete your training on the above topic?

Attended the live bootcamp

Self-learning using the kandi kit

College bootcamp

Did not complete any training

Answer the following questions to receive your Participation Certificate

Which of the following evaluation metrics was used to sort the models? 

MAD

RMSE

r squared

MSE

Which of the following regularisation method combines L1 and L2 penalties? 

Lasso

Elastic Net

Ridge

AdaBoost

Regression helps in the prediction of _____________ 

Categorical values

Continuous values

Numeric values

Non-numeric values

Regression is an example of ________________ 

Supervised learning

Semi-Supervised learning

Self-supervised learning

Unsupervised learning

What does the regularisation prevent in machine learning? 

Underfitting

Overfitting

Tuning

Learning

OPTIONAL ACHIEVEMENT CERTIFICATE - Upgrade your Participation Certificate to an Achievement Certificate by answering the below optional practical question

In this training, we discussed how Student's Grade is predicted. Download and run the Students Grade predictor kit and use the model to predict the final grades of a student based on different grade scores of the tests G1 and G2 and share with us a screenshot/photo/video of the changes that you implemented in your kit.