Predicting Credit Card Fraud with R
Use R to identify fraudulent credit card transactions with a variety of classification methods.
Create, train, and evaluate decision tree, naïve Bayes, and Linear discriminant analysis classification models using R
Generate synthetic samples to improve the performance of your models.
Demuestra esta experiencia práctica en una entrevista
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.
Familiarity with data analysis and using R.
Habilidades que desarrollarás
Aprende paso a paso
En un video que se reproduce en una pantalla dividida con tu área de trabajo, tu instructor te guiará en cada paso:
Task 1: Explore why imbalanced datasets are problematic for classification algorithms.
Task 2: Use R to explore a dataset.
Task 3: Create random testing and training datasets using the caret package in R.
Task 4: Use R to synthetically balance your training dataset using three techniques from the smotefamily package.
Task 5: Train three classification algorithms (decision tree, naïve Bayes, and linear discriminant analysis) using the natively imbalanced dataset, and generate the predictions for the test dataset.
Task 6: Use R to visually compare your models using the recall, precision, and F measure classification accuracy metrics.
Cómo funcionan los proyectos guiados
Tu espacio de trabajo es un escritorio virtual directamente en tu navegador, no requiere descarga.
En un video de pantalla dividida, tu instructor te guía paso a paso
Reseñas
Principales reseñas sobre PREDICTING CREDIT CARD FRAUD WITH R
Very intriguing course and example application. Very informative and practical approaches to addressing imbalances in data. Excellent instructor and great course.
It is best guided project which helps to learn caret library and this helped me to increase my r programming skills
Very thorough and enlightening demonstration of the classification on unbalanced data sets.
Very knowledgeable instructor. Excellent information.
Preguntas Frecuentes
Preguntas Frecuentes
¿Hay Proyectos guiados disponibles para computadoras de escritorio y dispositivos móviles?
¿Quiénes son los instructores de Proyectos guiados?
¿Puedo descargar el trabajo de mi Proyecto guiado después de completarlo?
¿Cuánta experiencia preciso tener para este Proyecto guiado?
¿Puedo completar este Proyecto guiado directamente utilizando mi navegador web, en lugar de instalar un software especial?
¿Cómo es la experiencia de aprendizaje con los Proyectos guiados?
¿Tienes más preguntas? Visita el Centro de Ayuda al Alumno.
Coursera Footer
