Practice Step 4: Model Building - 18.3.4 | 18. Data Science for Business and Decision- Making | Data Science Advance
K12 Students

Academics

AI-Powered learning for Grades 8–12, aligned with major Indian and international curricula.

Professionals

Professional Courses

Industry-relevant training in Business, Technology, and Design to help professionals and graduates upskill for real-world careers.

Games

Interactive Games

Fun, engaging games to boost memory, math fluency, typing speed, and English skills—perfect for learners of all ages.

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What is supervised learning?

💡 Hint: Think about how a teacher guides a student.

Question 2

Easy

Give one example of unsupervised learning.

💡 Hint: It's about discovering groups in data.

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

Which method uses labeled data to train models?

  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning

💡 Hint: Think about the definition of the learning types.

Question 2

True or False: Unsupervised learning requires labeled training data.

  • True
  • False

💡 Hint: Reflect on the meaning of 'unsupervised'.

Solve 1 more question and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Design a supervised learning model for predicting loan defaults. What data would you collect, and what features would be most relevant?

💡 Hint: Think about attributes that indicate financial reliability.

Question 2

Discuss how reinforcement learning can be implemented in a real-time bidding system for online advertising.

💡 Hint: Consider how learning from user interactions can influence decisions.

Challenge and get performance evaluation