Practice Step 8: Predict for New Student - 9.9 | Chapter 9: End-to-End Machine Learning Project – Predicting Student Exam Performance | Machine Learning Basics
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9.9 - Step 8: Predict for New Student

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Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What structure do we use for input when predicting a new student's outcome?

💡 Hint: Think about how we organize data in rows and columns.

Question 2

Easy

What is the output of the model's prediction?

💡 Hint: Recall the meaning of the numbers we used for passing and failing.

Practice 4 more questions and get performance evaluation

Interactive Quizzes

Engage in quick quizzes to reinforce what you've learned and check your comprehension.

Question 1

What is the output of the model.predict() function?

  • A numerical score
  • A binary outcome (pass/fail)
  • Descriptive statistics

💡 Hint: Think about how we defined passing and failing.

Question 2

True or False: You can feed non-numerical data directly into the predict function.

  • True
  • False

💡 Hint: What was the key preprocessing step in our project?

Solve 1 more question and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

You receive a new student's information indicating they studied for 3 hours, had 75% attendance but did not take a preparation course. Formulate a way to predict their outcome using the model. What will be your input, and what do you expect if their model performance is reliable?

💡 Hint: Compare their attributes against the previously successful students.

Question 2

If two students have identical attendance and preparation course status but different study hours (one studied 2 hours and another 10), how would you expect their predictions to differ? Use their study hours as a core factor in your prediction.

💡 Hint: Think back to how study hours affected past predictions.

Challenge and get performance evaluation