Credit Card Fraud Detection using Scikit-Learn and Snap ML
IntermediateGuided Project
Wondering how Machine Learning can help with credit card fraud detection? This guided project will show you how to utilize the high-performance IBM library Snap ML to accelerate the training of your Machine Learning models for detecting fraudulent credit card transactions.
4.5 (92 Reviews)
Language
- English
Topic
- Data Science
Enrollment Count
- 576
Skills You Will Learn
- Data Science, Python, Machine Learning, SVM, Fraud Detection
Offered By
- IBM
Estimated Effort
- 30 minutes
Platform
- SkillsNetwork
Last Update
- May 16, 2024
About This Guided Project
As the world shift towards online payment methods at a faster pace, enhancing credit card fraud detection has been a priority for all financial organizations. With the help of Machine Learning, organizations can detect credit card fraud more easily and efficiently. Want to know how? This guided project provides you with a cleaned credit card dataset and allows you to use Machine Learning faster with Snap ML.
Snap ML is a library for accelerated training and inference of Machine Learning models such as linear models, decision trees, random forests and boosting machines. It's a library developed and maintained by IBM Research. The library binaries are freely available on PyPi. It supports Linux/x86, Linux/Power, MacOS, Windows, Linux/Z. GPU support is also available for Linux. If you are curious, you can find detailed documentation here and usage examples here.
We will focus on training acceleration in particular. You will consolidate your machine learning (ML) modelling skills by using two popular classification models to recognize fraudulent credit card transactions. They are the Decision Tree and Support Vector Machine. You will use a real-world dataset to train such models.
You will find out that a Scikit-learn application can be seamlessly optimized by using Snap ML. The seamless integration of the Snap ML library is possible due to its Scikit-learn Python API compatibility.
Snap ML is a library for accelerated training and inference of Machine Learning models such as linear models, decision trees, random forests and boosting machines. It's a library developed and maintained by IBM Research. The library binaries are freely available on PyPi. It supports Linux/x86, Linux/Power, MacOS, Windows, Linux/Z. GPU support is also available for Linux. If you are curious, you can find detailed documentation here and usage examples here.
We will focus on training acceleration in particular. You will consolidate your machine learning (ML) modelling skills by using two popular classification models to recognize fraudulent credit card transactions. They are the Decision Tree and Support Vector Machine. You will use a real-world dataset to train such models.
You will find out that a Scikit-learn application can be seamlessly optimized by using Snap ML. The seamless integration of the Snap ML library is possible due to its Scikit-learn Python API compatibility.
A Look at the Project Ahead
After completing this guided project you will be able to:
- Perform basic data preprocessing using Scikit-learn.
- Model a fraud detection task using the Scikit-Learn and Snap ML Python APIs.
- Train Support Vector Machine and Decision Tree models using Scikit-Learn and Snap ML.
- Run inference and assess the quality of the trained models.
What You'll Need
To complete this guided project, you will need a basic understanding of the working mechanics of the Decision Tree and Support Vector Machine models. You will also need some prior experience working with Scikit-learn APIs to be able to follow our data preprocessing steps easily.
This course mainly uses Python and JupyterLabs. Although these skills are recommended prerequisites, no prior experience is required as this Guided Project is designed for complete beginners.
Frequently Asked Questions
Do I need to install any software to participate in this project?
Everything you need to complete this project will be provided to you via the Skills Network Labs and it will all be available via a standard web browser.
What web browser should I use?
The Skills Network Labs platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer, or Safari.
To complete this guided project, you will need a basic understanding of the working mechanics of the Decision Tree and Support Vector Machine models. You will also need some prior experience working with Scikit-learn APIs to be able to follow our data preprocessing steps easily.
This course mainly uses Python and JupyterLabs. Although these skills are recommended prerequisites, no prior experience is required as this Guided Project is designed for complete beginners.
Frequently Asked Questions
Do I need to install any software to participate in this project?
Everything you need to complete this project will be provided to you via the Skills Network Labs and it will all be available via a standard web browser.
What web browser should I use?
The Skills Network Labs platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer, or Safari.
Language
- English
Topic
- Data Science
Enrollment Count
- 576
Skills You Will Learn
- Data Science, Python, Machine Learning, SVM, Fraud Detection
Offered By
- IBM
Estimated Effort
- 30 minutes
Platform
- SkillsNetwork
Last Update
- May 16, 2024
Instructors
Roxanne Li
Data Scientist at IBM
I am an aspiring Data Scientist at IBM with extensive theoretical/academic, research, and work experience in different areas of Machine Learning, including Classification, Clustering, Computer Vision, NLP, and Generative AI. I've exploited Machine Learning to build data products for the P&C insurance industry in the past. I also recently became an instructor of the Unsupervised Machine Learning course by IBM on Coursera!
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