Detecting Online Payment Fraud with Machine Learning Techniques in Python
Learn how to detect online payment fraud using machine learning in Python. This step-by-step tutorial covers loading and preparing real-world credit card transaction data, exploring patterns, and building a predictive model using tools like pandas and scikit-learn. No prior experience is required, and all concepts are explained in a clear, beginner-friendly way.
You will discover how to handle imbalanced data, visualize key features, and evaluate your model with precision and recall. By the end, you will be able to make your own predictions and understand the challenges of real-world fraud detection. Continue learning with recommended resources and practical exercises.
00:00 Introduction to fraud detection
01:01 What you will learn
02:23 Data setup and loading
03:09 Understanding the data set
04:45 Checking for missing values
05:42 Exploring class distribution
06:49 Visualizing class imbalance
08:04 Analyzing transaction amounts
09:00 Feature selection and cleaning
10:16 Standardizing features
11:27 Visualizing feature distributions
12:53 Splitting data for training and testing
13:35 Choosing a machine learning model
15:03 Training the random forest
16:16 Making predictions
17:27 Evaluating model performance
19:12 Visualizing confusion matrix
20:40 Feature importance analysis
22:14 Testing with real and custom inputs
24:40 Real-world challenges and limitations
25:11 Next steps and further learning
25:54 Lesson recap and conclusion
#MachineLearning #Python #FraudDetection