Contents
Expected Project Output
After completing this project, learners will be able to generate several useful outputs related to fraud detection.
Transaction Distribution Analysis
Visualize the distribution of genuine and fraudulent transactions.
Exploratory Data Analysis
Analyze transaction amounts, feature distributions, and class imbalance.
Fraud Detection Model
Train a Logistic Regression model to classify transactions.
Model Evaluation
Evaluate the classification model using:
- Accuracy
- Precision
- Recall
- F1 Score
- Confusion Matrix
- ROC-AUC Score
Performance Visualizations
Generate charts such as:
- Transaction Class Distribution
- Correlation Heatmap
- Confusion Matrix
- ROC Curve
- Precision–Recall Curve
These outputs help assess the effectiveness of the model in identifying fraudulent transactions while maintaining reliable performance on legitimate transactions.
Credit Card Fraud Detection for Beginners using Data Science
VA










