AIM411 Data Mining and Analytics Quiz

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Questions and Answers

What is the primary reason for the enormous data growth mentioned?

A decline in data storage capabilities

Increased competition among companies

Decreased data collection technologies

Advances in data generation and collection technologies (correct)

Which of the following best describes the new mantra regarding data collection?

Avoiding data collection to minimize risks

Collecting only essential data for immediate purposes

Gathering data selectively based on its known utility

Gather whatever data you can whenever and wherever possible (correct)

What competitive advantage is highlighted in the text that data mining can provide?

Reduced data storage needs

Better, customized services for an edge (correct)

Lower operational costs

Enhanced data privacy and security

Which type of data does Google extensively handle as mentioned?

Peta Bytes of web data Signup and view all the answers

Which of the following sources is NOT mentioned as a source of data growth?

User reviews on products Signup and view all the answers

What is the total weight of the final exam in the course assessment?

40 marks Signup and view all the answers

How many quizzes are included in the class work assessment?

2 Quizzes Signup and view all the answers

Which of the following is NOT a research interest of Dr. Ahmed Abdelhafeez?

Operating Systems Signup and view all the answers

What is the significance of the h-index mentioned for Dr. Ahmed Abdelhafeez?

It indicates research impact and productivity Signup and view all the answers

What is the date for Quiz 1?

October 21, 2024 Signup and view all the answers

Which method is NOT listed under Data Mining techniques?

Statistical Analysis Signup and view all the answers

What is the total degree for Quiz 2?

5 degrees Signup and view all the answers

Which certification is NOT mentioned as being held by Dr. Ahmed Abdelhafeez?

MCSE Signup and view all the answers

What is the primary goal of churn prediction for telephone customers?

To predict whether a customer is likely to leave. Signup and view all the answers

Which of the following attributes is NOT considered in churn prediction?

Color preferences Signup and view all the answers

In the context of sky survey cataloging, what is the first step taken in processing the images?

Segment the image Signup and view all the answers

How many pixels are there in each image from the Palomar Observatory survey?

23,040 x 23,040 Signup and view all the answers

What statistical method is commonly used to predict the value of a continuous variable based on other variables?

Regression Signup and view all the answers

Which of the following is NOT an example of a prediction that uses regression?

Customer preference for products Signup and view all the answers

What is the size of the object catalog in the galaxy classification project?

9 GB Signup and view all the answers

What has been identified as a success story in the sky survey cataloging project?

Discovery of new high red-shift quasars Signup and view all the answers

What is the primary goal of fraud detection in credit card transactions?

To classify transactions as either legitimate or fraudulent Signup and view all the answers

Which of the following best describes the approach to identifying fraudulent credit card transactions?

Using past transaction data as a basis for classification Signup and view all the answers

What are the attributes used to classify transactions in fraud detection?

Transaction timing and purchasing frequency Signup and view all the answers

What type of model is learned from labeled transactions in the context of fraud detection?

A classification model for transaction validity Signup and view all the answers

Which example best illustrates a classification task within the provided context?

Classifying news articles by topic Signup and view all the answers

What is a key factor in labeling past transactions as either fraud or fair?

Considering the historical behavior of the account-holder Signup and view all the answers

Which of the following examples does NOT relate to classification tasks?

Predicting weather conditions Signup and view all the answers

What might be a consequence of not using customer attributes in classification for fraud detection?

Increased false positives in fraud alerts Signup and view all the answers

What classification task is illustrated by the model predicting credit worthiness?

Predictive modeling Signup and view all the answers

Which marital status has the highest representation of 'Cheat' in the data provided?

Divorced Signup and view all the answers

Based on the data, which level of education appears most commonly among employed individuals?

Graduate Signup and view all the answers

Which attribute is likely a predictor for whether someone is credit worthy according to the classification example?

Years at present address Signup and view all the answers

In the context of the data, what does a 'Yes' in the 'Refund' column indicate?

The individual has received a tax refund Signup and view all the answers

What classification outcome is being modeled when predicting whether 'Tid 1' is credit worthy?

Low risk Signup and view all the answers

Based on the data, which demographic has the least amounts of 'No' responses in the 'Cheat' column?

Married Signup and view all the answers

Which of the following attributes does not appear to have a direct impact on predicting credit worthiness?

Marital status Signup and view all the answers

What does 'Tid' refer to in the provided data?

Transaction ID Signup and view all the answers

What can be inferred if a person has been employed for less than 3 years?

They may have a higher risk for credit unworthiness Signup and view all the answers

Study Notes

Course Information

AIM411: Data Mining and Analytics
Lecturer: Dr. Ahmed Abdelhafeez
Lab Instructor: Eng. Shady Ahmed Bedeir
Google Classroom Code: 4t46lsf
Midterm Exam: 25% of total marks
Practical Exam: 20% of total marks
Final Exam: 40% of total marks
Class Work: 20% of total marks, including two quizzes and a project

Course Staff: Instructor

Dr. Ahmed Abdelhafeez Ibrahim
Holds a PhD from the Faculty of Engineering, Ain Shams University
Research Interests: AI & Machine Learning Techniques, Deep Learning, Ensemble Learning, Image Processing, Pattern Recognition, Data Science, and Neutrosophic Techniques
Assistant Professor Researcher at the Department of Artificial Intelligence, October 6th University
H-index of 10 on Google Scholar
Managing editor for SciNexus Journal
Published 60 research papers, and reviewed over 30 for five ranked journals
Author for Nehdet Misr Publishing Group
Lecturer In Elforqan training in Qatar
Part-time lecturer at the Faculty of Computer Science, Arab Academy
Holds several certifications, including ICDL, IC3, Master of Microsoft Office, CCNA, ISO, Huawei HCIA, IBM certified in Big Data and AI

Why Data Mining?

Increasing data generation and collection technologies have led to an explosion of data in businesses and scientific databases
New Mantra: Gather as much data as possible, whenever and wherever it’s available
Expectations: Gathered data will have value, either for the original purpose or for unforeseen purposes
Businesses have large amounts of data:
- Google has Peta Bytes of web data
- Facebook has billions of active users
- Amazon handles millions of visits daily
- Bank and credit card transactions are constantly recorded
Increased computer power and affordability
Competitive pressure for better, customized services (e.g., customer relationship management)

Data Mining Tasks

Predictive Modelling:
- Classification: Finding models for class attributes as a function of other attribute values
- Regression: Predicting the value of a continuous variable based on other variables, using a linear or non-linear model

Classification: Application 1 - Fraud Detection

Goal: Predict fraudulent transactions in credit card data
Approach:
- Use credit card transactions and customer information (buying patterns, payment history, etc.) as attributes
- Label past transactions as fraud or fair
- Learn a model for classifying transactions
- Use the model to detect fraud in real-time

Classification: Application 2 - Churn Prediction

Goal: Predict whether a customer is likely to switch to a competitor
Approach:
- Analyze detailed customer transaction records (frequency of calls, time of day, financial status, etc.)
- Label customers as loyal or disloyal
- Create a model to predict customer loyalty

Classification: Application 3 - Sky Survey Cataloging

Goal: Predict the class of sky objects (star or galaxy) based on telescope images
Approach:
- Segment the image
- Measure image features (40 per object)
- Model the class based on these features
- Successfully identified 16 new high red-shift quasars, some of the farthest objects difficult to find

Regression

Predicting the value of a continuous variable based on the values of other variables, assuming a linear or non-linear model of dependency
Examples:
- Predicting sales amounts based on advertising expenditure
- Predicting wind velocities based on temperature, humidity, and pressure
- Predicting stock market indices

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Description

Prepare for your AIM411 quiz on Data Mining and Analytics, guided by Dr. Ahmed Abdelhafeez. This quiz will cover key concepts, techniques, and applications in the field of data science as discussed in class. Test your understanding and readiness for your upcoming midterm and practical exams!