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Predicting Credit Card Fraud with R
John Garcia
John Garcia
Welcome to Predicting Credit Card Fraud with R. In this project-based course, you will learn how to use R to identify fraudulent credit card transactions with a variety of classification methods and use R to generate synthetic samples to address the common problem of classification bias for highly imbalanced datasets—the class of interest (fraud) represents less than 1% of the observations.
Read more
Welcome to Predicting Credit Card Fraud with R. In this project-based course, you will learn how to use R to identify fraudulent credit card transactions with a variety of classification methods and use R to generate synthetic samples to address the common problem of classification bias for highly imbalanced datasets—the class of interest (fraud) represents less than 1% of the observations.
Read more
Welcome to Predicting Credit Card Fraud with R. In this project-based course, you will learn how to use R to identify fraudulent credit card transactions with a variety of classification methods and use R to generate synthetic samples to address the common problem of classification bias for highly imbalanced datasets—the class of interest (fraud) represents less than 1% of the observations.
Class imbalance can make it difficult to detect the effect independent variables have on fraud, ultimately leading to higher misclassification rates. Fixing the imbalance allows the minority class (fraud) to be better learned by the classifier algorithms.
After completing the project, you will be able to apply the methods introduced in the project to a wide range of classification problems that typically confront class imbalance, including predicting loan default, customer churn, cancer diagnosis, early high school dropout risk, and malware detection.
Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Register for this course and see more details by visiting:
OpenCourser.com/course/a0b4rx/predicting
What's inside
Syllabus
Project Overview
Here you will describe what the project is about. It should give an overview of what the learner will achieve by completing this project.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Uses a real-world example of credit card fraud for a more practical learning experience
Covers techniques for addressing class imbalance, which is a common challenge in fraud detection
Applicable to a wide range of classification problems, making it a valuable skill for data scientists and analysts
Taught by instructors with experience in fraud detection, providing practical insights
May require familiarity with statistical modeling and machine learning concepts
Best suited for learners interested in careers in data science, fraud analytics, or risk management
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Reviews summary
In-depth fraud detection
Learners say this in-depth course is great for learning data imbalance techniques and ML algorithms in R. Students appreciate the knowledgeable instructor and engaging assignments. However, it may be too advanced for complete beginners.
Engaging assignments help students learn.
"Great, very helpful. Made a difficult project seem easy."
Knowledgeable instructor provides valuable information.
"Very knowledgeable instructor. Excellent information."
In-depth coverage of data imbalance techniques.
"Very thorough and enlightening demonstration of the classification on unbalanced data sets."
Course may be too advanced for beginners.
"It is a course that goes over a code project. It does not explain what it is doing and why, nor does it teach how to employ the knowledge in different contexts or with different data."
Activities
Coming soon
We're preparing activities for
Predicting Credit Card Fraud with R.
These are activities you can do either before, during, or after a course.
Career center
Learners who complete Predicting Credit Card Fraud with R will develop knowledge and skills
that may be useful to these careers:
Fraud Analyst
Fraud Analyst
A Fraud Analyst investigates suspicious transactions and identifies patterns and trends to prevent fraud. Understanding how to use R to identify fraudulent credit card transactions, as covered in Predicting Credit Card Fraud with R, directly applies to the responsibilities of a Fraud Analyst. You will become familiar with classification techniques and how to handle class imbalance, common challenges that you may encounter in this role.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer develops, deploys, and maintains machine learning models used in various applications. The emphasis on using R for building and evaluating machine learning models in Predicting Credit Card Fraud with R gives you a solid foundation for your role as a Machine Learning Engineer. You will develop skills in model development, evaluation, and deployment that are essential for success in this field.
Data Scientist
Data Scientist
A Data Scientist orchestrates the development of machine learning based tools and products that address critical business questions. The model building techniques that you will learn in the course, Predicting Credit Card Fraud with R, such as classification methods and working with synthetic samples, can help you refine and optimize machine learning models in your role as a Data Scientist. Completing this course may increase your confidence in identifying patterns and trends in data and developing accurate predictive models.
Risk Analyst
Risk Analyst
A Risk Analyst identifies, assesses, and mitigates risks in various industries. The skills you gain in Predicting Credit Card Fraud with R, such as using R for fraud detection and handling class imbalance, can be directly applied in the role of a Risk Analyst. You will become familiar with industry best practices for risk management and gain confidence in developing risk mitigation strategies.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst uses mathematical and statistical techniques to solve complex financial problems. Understanding how to identify fraudulent credit card transactions with R, as covered in Predicting Credit Card Fraud with R, can enhance your abilities as a Quantitative Analyst. You will gain experience in handling large datasets and using statistical techniques to analyze and identify patterns.
Credit Analyst
Credit Analyst
A Credit Analyst assesses the creditworthiness of individuals and businesses. The course, Predicting Credit Card Fraud with R, aligns well with the responsibilities of a Credit Analyst. By understanding how to use R to identify fraudulent credit card transactions and the techniques for handling class imbalance, you can enhance your analytical skills and make more informed decisions regarding credit risk.
Financial Analyst
Financial Analyst
A Financial Analyst provides insights into financial data to help make informed decisions and mitigate risk. The course, Predicting Credit Card Fraud with R, offers valuable knowledge and skills for a Financial Analyst. By understanding how to identify fraudulent credit card transactions, you can contribute to the development of robust fraud detection systems and help protect financial institutions from financial losses.
Statistician
Statistician
A Statistician collects, analyzes, interprets, and presents data. The course, Predicting Credit Card Fraud with R, aligns with the responsibilities of a Statistician. By learning how to use R for fraud detection and the techniques for handling class imbalance, you can enhance your statistical modeling skills and make more informed decisions.
Actuary
Actuary
An Actuary assesses financial risks and develops strategies to mitigate them. The course, Predicting Credit Card Fraud with R, provides a strong foundation for an Actuary. You will learn how to use R to identify fraudulent credit card transactions and gain valuable experience in statistical modeling and data analysis.
Data Analyst
Data Analyst
A Data Analyst extracts, transforms, and analyzes data to provide insights and support decision-making. The course, Predicting Credit Card Fraud with R, can be a valuable asset for a Data Analyst. By learning how to use R for fraud detection, you can apply these skills to other data analysis tasks and contribute to informed decision-making within an organization.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst uses mathematical and analytical techniques to solve complex problems and improve decision-making. The skills you gain in Predicting Credit Card Fraud with R, such as using R for data analysis and handling class imbalance, can be applied in your role as an Operations Research Analyst. You will be able to analyze data, identify trends, and develop solutions to optimize business operations.
Business Analyst
Business Analyst
A Business Analyst analyzes business processes and develops solutions to improve efficiency and effectiveness. The skills you gain in Predicting Credit Card Fraud with R, such as using R for data analysis and handling class imbalance, can be applied in your role as a Business Analyst. You will be able to analyze data, identify trends, and make recommendations to improve business outcomes.
Security Analyst
Security Analyst
A Security Analyst monitors and analyzes security systems to identify and mitigate vulnerabilities. The course, Predicting Credit Card Fraud with R, may be useful for a Security Analyst. By understanding how to use R for fraud detection and handling class imbalance, you can apply these skills to enhance security systems and protect against cyber threats.
Quantitative Researcher
Quantitative Researcher
A Quantitative Researcher develops and applies mathematical and statistical models to analyze data and make predictions. While the course, Predicting Credit Card Fraud with R, is not directly related to quantitative research, the skills you gain in data analysis, statistical modeling, and handling class imbalance can be valuable in certain areas of quantitative research, such as risk management and fraud detection.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software systems. While the course, Predicting Credit Card Fraud with R, is not directly related to software engineering, the skills you gain in data analysis, statistical modeling, and handling class imbalance can be valuable in certain areas of software development, such as fraud detection and risk management.
For more career information including salaries, visit:
OpenCourser.com/course/a0b4rx/predicting
Reading list
We've selected 12 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Predicting Credit Card Fraud with R.
Save
In-depth understanding of classification methods. Serves as a useful reference text.
Provides a foundational understanding of statistical learning, covering a wide range of topics, including classification methods and data analysis techniques. It offers a rigorous treatment of the subject and serves as a valuable reference for those seeking a deeper understanding of the statistical underpinnings of fraud detection.
Save
Focuses on practical applications of predictive modeling, including techniques for handling imbalanced datasets. It provides real-world examples and case studies that illustrate the use of classification methods in various domains.
Save
Introduces R programming and its essential libraries for machine learning. It covers data preprocessing, model building, and evaluation techniques, providing a practical guide for implementing the methods discussed in the course.
Specializes in imbalanced learning, providing a comprehensive overview of techniques and algorithms for handling datasets with skewed class distributions. It offers practical guidance for applying these methods in fraud detection and other real-world applications.
Save
Data mining techniques for fraud detection. Serves as a helpful reference text.
Machine learning algorithms to detect anomalous credit card activity. valuable reference.
Examines ensemble learning techniques, which combine multiple models to improve predictive performance. It discusses methods such as bagging, boosting, and random forests, which are commonly used in fraud detection systems.
Offers a unique perspective on machine learning, exploring the concepts and algorithms through practical examples and interactive exercises. It provides a high-level understanding of classification methods and their applications, making it a valuable resource for beginners.
Overall understanding of machine learning concepts. provides helpful background knowledge.
CART classification method to detect fraudulent transactions. Provides helpful background knowledge.
Although this book focuses on deep learning techniques, it offers valuable insights into neural networks, which are gaining popularity in fraud detection. It provides a practical guide to implementing and training neural network models for classification tasks.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/a0b4rx/predicting
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Effort
1.5 hours
Level
Intermediate
Via
Coursera
Institution
University of North Texas
Instructor
John Garcia
Language
English
Good to know
Uses a real-world example of credit card fraud for a more practical learning experience
Covers techniques for addressing class imbalance, which is a common challenge in fraud detection
Applicable to a wide range of classification problems, making it a valuable skill for data scientists and analysts
Taught by instructors with experience in fraud detection, providing practical insights
May require familiarity with statistical modeling and machine learning concepts
Best suited for learners interested in careers in data science, fraud analytics, or risk management
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