LIBA - Machine Learning Introduction

Which of the following is an example of diagnostic analytics?

• Monthly sales report

• Root cause analysis of declining sales

• Forecasting next month's sales

• Suggesting discounts to improve sales

Prescriptive analytics goes beyond predictive analytics by:

• Providing data summaries

• Identifying why something happened

• Suggesting actions and likely outcomes

• Eliminating irrelevant data

A retail chain analyzes 'What happened in the last quarter?' This is:

• Descriptive analytics

• Diagnostic analytics

• Predictive analytics

• Prescriptive analytics

In which scenario would unsupervised learning be most suitable?

• Predicting loan default using labeled repayment history

• Detecting unusual credit card transactions

• Classifying emails into spam and non-spam

• Predicting housing prices based on features

K-Means clustering is an example of:

• Supervised learning

• Unsupervised learning

• Reinforcement learning

• Semi-supervised learning

A bank wants to segment its customers into groups based on behavior. Which ML type should be used?

• Supervised

• Unsupervised

• Prescriptive

• Reinforcement

Which of the following is a categorical variable?

• Temperature in Celsius

• Number of transactions per day

• Blood group of patients

• Age in years

Which is the best description of numerical continuous data?

• Customer gender (Male/Female)

• Rating on a 5-point Likert scale

• Height of students in cm

• Number of employees in a company

The main purpose of normalization in data preprocessing is:

• Reduce data redundancy

• Scale features to a comparable range

• Remove irrelevant variables

• Fill missing values

A dataset has missing values in 20% of rows for a key variable. Which preprocessing step is most suitable before applying ML?

• Ignore the missing rows blindly

• Always fill missing values with 0

• Use imputation techniques such as mean/median or model-based imputation

• Remove all columns with missing data