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Expected Project Output

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.