Case-Based Questions: Advanced Concept of Modeling in AI | Artificial Intelligence for Class 10 PDF Download

Q. Identify the type of learning (supervised, unsupervised, reinforcement learning) are the following case studies most likely based on?

a) Case Study 1: A company wants to predict customer churn based on past purchasing behavior, demographics, and customer interactions. They have a dataset with labeled 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 behavior 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

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 behavior. Which type of learning is this case study most likely based on?
Ans: Supervised Learning

Q. Identify the type of model (classification, regression, clustering, association model) are the following case studies most likely based on?
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.
Ans: Regression

c) A marketing company wants to segment its customer base into distinct groups based on purchasing behavior for targeted marketing campaigns. They have a dataset containing information such as purchase history, frequency of purchases, and amount spent.
Ans: Clustering

d) A grocery store wants to identify associations between different products purchased by customers to understand which products are commonly bought together. They have a transaction dataset containing records of items purchased together during each transaction.
Ans: Association Model

Q. A healthcare provider wants to improve patient care by predicting the length of hospital stays for different medical conditions. They have a dataset containing patient demographics, medical history, and treatment details. The task involves:
Identify the type of model (classification, regression, clustering, and association model) in the tasks.

a) To predict whether a patient will have a short or long hospital stay.
Ans: Classification

b) To predict the number of days a patient will stay in the hospital.
Ans: Regression

c) To segment patients into groups with similar characteristics for personalized treatment plans.
Ans: Clustering

d) To identify patterns in patient treatments and outcomes.
Ans: Association Model

Q. Convert the following scenarios to perceptron:
​a) Context: A manager is deciding whether to approve a work-from-home request from an employee.
Factors: – Does the employee perform well when working remotely? – Are there any upcoming team meetings or collaborative projects? – Does the company’s policy support remote work? – Is it beneficial for both the employee and the company?
Ans:

• Inputs Factors: Employee remote performance, upcoming team meetings/projects, company policy on remote work, benefits for employee and company.
• Weights: Importance of each factor.
• Threshold: Decision boundary for approval.
• Output: Approve (1) or Deny (0).

b) Context: A homeowner is deciding whether to invest in solar panels for their house.
Factors: – Do I have a sufficient average amount of sunlight in my area? – Are there any available incentives or rebates for installing solar panels? – Does installing solar panels impact the value of my home? – Does solar energy lead to environmental benefits?
Ans:

• Inputs (Factors): Average sunlight, available incentives/rebates, impact on home value, environmental benefits.
• Weights: Importance of each factor.
• Threshold: Decision boundary for investment.
• Output: Invest (1) or Not Invest (0).