Predicting Credit Card Fraud with R from Coursera

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.

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

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

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

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Predicting Credit Card Fraud with R with these activities:

Practice identifying fraudulent credit card transactions

Show steps

By practicing identifying fraudulent credit card transactions, you will develop the skills necessary to effectively complete the project in this course.

Browse courses on Credit Card Fraud Detection

Show steps

Download the dataset of credit card transactions
Use R to explore the dataset and identify potential fraudulent transactions

Develop a model to predict fraudulent credit card transactions

Show steps

Developing a model to predict fraudulent credit card transactions will allow you to apply the methods introduced in the course to a real-world problem.

Browse courses on Classification Algorithms

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Choose a classification algorithm
Train the model on the dataset
Evaluate the model's performance

Show all two activities

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

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.

See salaries and explore the career path for Fraud Analyst

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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

See salaries and explore the career path for Software Engineer