Building Credit Card Fraud Detection With Machine Learning
Published 1/2024
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz
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
Overview
Section 1: Introduction
Lecture 1 Introduction to the Course
Lecture 2 Table of Contents
Lecture 3 Whom This Course is Intended for?
Section 2: Tools, IDE, and Datasets
Lecture 4 Tools, IDE, and Datasets
Section 3: Introduction to Fraud Detection Model
Lecture 5 Introduction to Fraud Detection Model
Section 4: How Credit Card Fraud Detection Model Works?
Lecture 6 How Credit Card Fraud Detection Model Works?
Section 5: Most Common Credit Card Fraud Cases
Lecture 7 Most Common Credit Card Fraud Cases
Section 6: Setting Up Google Colab IDE
Lecture 8 Setting Up Google Colab IDE
Section 7: Finding & Downloading Transaction Dataset From Kaggle
Lecture 9 Finding & Downloading Transaction Dataset From Kaggle
Section 8: Project Preparation
Lecture 10 Uploading Transaction Dataset to Google Colab IDE
Lecture 11 Quick Overview of Transaction Dataset
Section 9: Cleaning Dataset by Removing Missing Values & Duplicates
Lecture 12 Cleaning Dataset by Removing Missing Values & Duplicates
Section 10: Evaluating the Security of Chip & Pin Transaction Methods
Lecture 13 Evaluating the Security of Chip & Pin Transaction Methods
Section 11: Analyzing Repeat Retailer Fraud Patterns
Lecture 14 Analyzing Repeat Retailer Fraud Patterns
Section 12: Finding Correlation Between Transaction Amount & Fraud
Lecture 15 Finding Correlation Between Transaction Amount & Fraud
Section 13: Analyzing Fraud Cases in Online Transaction
Lecture 16 Analyzing Fraud Cases in Online Transaction
Section 14: Conducting Feature Selection with Random Forest
Lecture 17 Conducting Feature Selection with Random Forest
Section 15: Building Credit Card Fraud Detection Model with Random Forest
Lecture 18 Building Credit Card Fraud Detection Model with Random Forest
Section 16: Building Credit Card Fraud Detection Model with Logistic Regression
Lecture 19 Building Credit Card Fraud Detection Model with Logistic Regression
Section 17: Building Credit Card Fraud Detection Model with Support Vector Machine
Lecture 20 Building Credit Card Fraud Detection Model with Support Vector Machine
Section 18: Evaluating Model Performance with Precision, Recall, and F1 Score
Lecture 21 Evaluating Model Performance with Precision, Recall, and F1 Score
Section 19: Conclusion & Summary
Lecture 22 Conclusion & Summary
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
