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8. Explain any two examples of Regression Model.
Ans. Let us see some examples of the Regression Model:
• Income Prediction: Predict a person's annual income based on demographics. The model is trained with input
features like age, education, hours per week, meaning it can take value within a specific range. The output is a
continuous value, annual income of a person.
• House Price Prediction: The model predicts the selling price of a house based on the input features like size of the
house, number of rooms, location, market price of the house, etc. The model predicts the price of the house based
on features of the house.
C. Competency-based/Application-based questions. 21 st Century #Information Literacy
#Critical Thinking
Skills
1. Emma is using a ridesharing app to book a cab. She notices that the app predicts her destination based on her travel
history and provides an estimated arrival time for the cab. Which technology is most likely responsible for predicting
Emma's destination?
Ans. Machine Learning
2. A hospital uses a system that can automatically detect tumours in X-ray images with high accuracy. The system has
been trained using a large dataset of medical images. Which type of technology is being used in this scenario?
Ans. Deep Learning using Convolutional Neural Networks (CNNs)
3. Identify the type of learning (supervised, unsupervised, reinforcement learning) are the following case studies most
likely based on? [CBSE Handbook]
a. Case Study 1: A company wants to predict customer churn based on past purchasing behaviour, demographics, and
customer interactions. They have a dataset with labelled examples of customers who churned and those who did not.
Ans. Supervised Learning
b. Case Study 2: A social media platform wants to group users based on their interests and behaviour to recommend
relevant content. They have a large dataset of user interactions but no predefined categories. Which type of learning
is this case study most likely based on?
Ans. Unsupervised Learning
c. Case Study 3: An autonomous vehicle is learning to navigate through a city environment. It receives feedback in the
form of rewards for reaching its destination safely and penalties for traffic violations. Which type of learning is this
case study most likely based on?
Ans. Reinforcement Learning (RL)
d. Case Study 4: A healthcare provider wants to identify patterns in patient data to personalize treatment plans. They
have a dataset with various patient attributes but no predefined labels indicating specific treatment plans. Which
type of learning is this case study most likely based on?
Ans. Unsupervised Learning
e. Case Study 5: A manufacturing company wants to optimize its production process by detecting anomalies in sensor
data from machinery. They have a dataset with examples of normal and anomalous behaviour. Which type of
learning is this case study most likely based on?
Ans. Supervised Learning
4. Identify the type of model (classification, regression, clustering, association model) are the following case studies most
likely based on? [CBSE Handbook]
a. A bank wants to predict whether a loan applicant will "default" or "non-default" on their loan payments. They have
a dataset containing information such as income, credit score, loan amount, and employment status.
Ans. Classification
b. A real estate agency wants to predict the selling price of houses based on various features such as size, location,
number of bedrooms, and bathrooms. They have a dataset containing historical sales data.
Advanced Concepts of Modeling in AI 211

