Building Credit Card Fraud Detection With Machine Learning

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Building Credit Card Fraud Detection With Machine Learning
Published 1/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.42 GB | Duration: 3h 5m

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

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

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