Скачать или смотреть Exploring Credit Card Fraud Data | Fraud Detection Tutorial #2

In this video, we dive deep into credit card fraud data from Kaggle’s Credit Card Fraud Detection Dataset (284,807 transactions, with only 492 frauds – that’s just 0.172%!).

🔍 What you’ll learn in this session:

The extreme class imbalance problem (578:1 ratio of normal to fraud) and why accuracy is misleading

How fraudsters behave differently in transaction amounts and time patterns

The most powerful PCA features for spotting fraud

Why outlier detection using Isolation Forest reveals strong fraud signals

Best practices for preparing highly imbalanced data before modeling

📊 We’ll explore the dataset cell by cell in a Jupyter Notebook, using pandas, numpy, matplotlib, seaborn, and sklearn. You’ll see visualizations, statistical insights, and feature analyses that will shape our next step: building fraud detection models.

📝 Resources

Kaggle Dataset: Credit Card Fraud Detection https://www.kaggle.com/datasets/mlg-ulb/cr...

GitHub Repository with Notebooks (https://github.com/ysimokat/Bank-Fraud-Det...)

📌 Next Video Preview

We’ll build our first machine learning models for fraud detection:

Logistic Regression (baseline)

Random Forest (tree-based learning)

Neural Network (deep learning)

Isolation Forest (anomaly detection)
…plus techniques like SMOTE and class weights to handle imbalance.

💬 Discussion Starter:
What surprised you most – the extreme imbalance, the PCA features, or the outlier patterns?
Have you worked with imbalanced datasets before? Share your strategies in the comments!

👍 Like & Subscribe to follow the full fraud detection project step by step.

#machinelearning #datascience #deeplearning