Free Download Building Credit Card Fraud Detection with Machine Learning
Published 1/2024
Created by Christ Raharja
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 22 Lectures ( 3h 5m ) | Size: 1.2 GB
Learn how to build credit card fraud detection model using Random Forest, Logistic Regression and Support Vector Machine
What you'll learn:
Learn how to build credit card fraud detection model using Random Forest, Logistic Regression, and Support Vector Machine
Learn how to conduct feature selection using Random Forest
Learn how to analyze and identify repeat retailer fraud patterns
Learn how to analyze fraud cases in online transaction
Learn how to evaluate the security of chip and pin transaction methods
Learn how to find correlation between transaction amount and fraud
Learn how credit card fraud detection models work. This section will cover data collection, feature selection, model training, and real time processing
Learn how to evaluate fraud detection model's accuracy and performance using precision, recall, and F1 score
Learn about most common credit card fraud cases like stolen card, card skimming, phishing attack, identity theft, data breach, and insider fraud
Learn the basic fundamentals of fraud detection model
Learn how to find and download datasets from Kaggle
Learn how to clean dataset by removing missing rows and duplicate values
Requirements:
No previous experience in machine learning is required
Basic knowledge in statistics and Python
Description:
Welcome to Building Credit Card Fraud Detection Model with Machine Learning course. This is a comprehensive project based course where you will learn step by step on how to build a credit card fraud detection model using logistic regression, support vector machine, and random forest. This course is a perfect combination between machine learning and fraud detection, making it an ideal opportunity to enhance your data science skills. The course will be mainly concentrating on three major aspects, the first one is data analysis where you will explore the credit card dataset from various angles, the second one is predictive modeling where you will learn how to build fraud detection model using big data, and the third one is to evaluate the fraud detection model's accuracy and performance. In the introduction session, you will learn the basic fundamentals of fraud detection models, such as getting to know its common challenges and practical applications. Then, in the next session, we are going to learn about the full step by step process on how the credit card fraud detection model works. This section will cover data collection, feature extraction, model training, real time processing, and post alert action. Afterwards, you will also learn about most common credit card fraud cases, for examples like card skimming, phishing attacks, identity theft, stolen card, data breaches, and insider fraud. Once you have learnt all necessary knowledge about the credit card fraud detection model, we will start the project. Firstly you will be guided step by step on how to set up Google Colab IDE. In addition to that, you will also learn how to find and download credit card dataset from Kaggle, Once, everything is ready, we will enter the main section of the course which is the project section The project will be consisted of three main parts, the first part is the data analysis and visualization where you will explore the dataset from multiple angles, in the second part, you will learn step by step on how to build credit card fraud detection model using logistic regression, support vector machine, and random forest, meanwhile, in the third part, you will learn how to evaluate the model's performance. Lastly, at the end of the course, you will conduct testing on the fraud detection model to make sure it produces accurate results and functions as it should.First of all, before getting into the course, we need to ask ourselves this question: why should we build a credit card fraud detection model? Well, here is my answer. In the past couple of years, we have witnessed a significant increase in the number of people conducting online transactions and, consequently, the risk of credit card fraud has surged. As technology advances, so do the techniques employed by fraudsters. Building a credit card fraud detection model becomes imperative to safeguard financial transactions, protect users from unauthorized activities, and maintain the integrity of online payment systems. By leveraging machine learning algorithms and data-driven insights, we can proactively identify and prevent fraudulent transactions. Last but not least, knowing how to build a complex fraud detection model can potentially open a lot of opportunities in the future.Below are things that you can expect to learn from this course:Learn the basic fundamentals of fraud detection modelLearn how credit card fraud detection models work. This section will cover data collection, feature selection, model training, real time processing, and post alert actionLearn about most common credit card fraud cases like stolen card, card skimming, phishing attack, identity theft, data breach, and insider fraudLearn how to find and download datasets from KaggleLearn how to clean dataset by removing missing rows and duplicate valuesLearn how to evaluate the security of chip and pin transaction methodsLearn how to analyze and identify repeat retailer fraud patternsLearn how to find correlation between transaction amount and fraudLearn how to analyze fraud cases in online transactionLearn how to conduct feature selection using Random ForestLearn how to build credit card fraud detection model using Random ForestLearn how to build credit card fraud detection model using Logistic RegressionLearn how to build credit card fraud detection model using Support Vector MachineLearn how to evaluate fraud detection model's accuracy and performance using precision, recall, and F1 score
Who this course is for:
People who are interested in building credit card fraud detection model using machine learning
People who are interested in conducting feature selection using Random Forest
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