💳 Credit Card Fraud Detection Dashboard

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📌 Overview

Interactive dashboard built with Streamlit, Plotly, and Scikit-learn for real-time fraud detection analysis.
It demonstrates a business-aware ML pipeline on the classic Credit Card Fraud Dataset (284,807 transactions, only 492 frauds ≈ 0.17%).

• 🔍 Upload your own transaction CSV or use the built-in dataset
• ⚖️ Custom decision thresholds with cost-sensitive analysis
• 📊 Confusion matrix, ROC/PR curves, and cost–threshold visualization
• 💡 Permutation feature importance for interpretability
• 🧾 Segmented performance profiling (by amount, time of day, etc.)

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📊 Dashboard Preview

Data Overview

Prediction Engine

Model Metrics

Model Insights

Data Quality & Segments

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🔑 Features

• Models: RandomForest & XGBoost (calibrated)
• Presets: Strict / Balanced / Lenient thresholds
• Threshold Finder: auto-select by target Precision/Recall
• Cost Analysis: business-aligned FP vs FN costs
• Visuals: Confusion matrix, ROC, PR, cost vs threshold curves
• Insights: Permutation importance, segmented KPIs
• Data Handling: automatic schema validation + engineered features (log(Amount), business hours, night proxy)

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🚀 Run Locally

Clone the repo and install requirements:

git clone https://github.com/tarekmasryo/fraud-detection-dashboard.git
cd fraud-detection-dashboard
pip install -r requirements.txt

Run the app:

streamlit run app.py

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Related Repositories

• 🔍 Fraud Detection EDA + Baseline Models

If you use this dashboard, please credit as:

Fraud Detection Dashboard by Tarek Masryo.
Code licensed under Apache 2.0