Credit Card Fraud Detection with Machine Learning

Fraudulent transactions cost billions annually. This project aims to accurately detect credit card fraud using machine learning techniques on a highly imbalanced dataset. With precision-driven modeling, robust evaluation, and insightful interpretation, this solution contributes toward safer digital transactions in the fintech world.

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🔍 Overview

• Dataset: Kaggle - Credit Card Fraud Detection
• Goal: Identify fraudulent credit card transactions using supervised learning.
• Total Records: 284,807 transactions, including 492 frauds (0.172%)
• Core Focus: Tackling class imbalance, training robust models, and evaluating fraud detection effectiveness.

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📌 Key Features

• Thorough Exploratory Data Analysis (EDA)
• Class imbalance handling using undersampling
• Preprocessing pipeline for scalable feature transformation
• Model building: Logistic Regression, Random Forest, and SVM
• In-depth performance evaluation: Confusion Matrix, ROC Curve, AUC, Precision, Recall, F1-Score
• Data visualization for insights and model interpretability

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🛠️ Tech Stack

Category	Tools & Libraries
Language	Python 3.x
Data Processing	pandas, numpy
Visualization	matplotlib, seaborn
ML Algorithms	scikit-learn
Evaluation Tools	classification_report, roc_auc_score

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⚙️ Workflow

1. Load and inspect dataset
2. Handle class imbalance (undersampling)
3. Preprocess data (scaling, feature analysis)
4. Train ML models (Logistic Regression, Random Forest, SVM)
5. Evaluate models using appropriate metrics
6. Visualize predictions and interpret model outputs

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📈 Performance Metrics

Metric	Why It Matters
Accuracy	Measures overall correctness
Precision	Ensures fraud predictions are reliable
Recall	Captures most actual frauds (critical metric)
F1-Score	Balances precision and recall
ROC-AUC	Measures discrimination capability at all thresholds

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🚀 Results Summary

• High Recall achieved, critical for fraud detection
• Applied undersampling to address class imbalance
• Random Forest performed best in precision-recall balance
• Clear, interpretable model evaluation with visual plots

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🔮 Future Scope

• Explore SMOTE/ADASYN for synthetic oversampling
• Try advanced algorithms: XGBoost, LightGBM, CatBoost
• Build and deploy a real-time detection system (API/Streamlit)
• Design a live fraud alert dashboard for financial applications

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📂 Dataset Reference

📎 Credit Card Fraud Detection Dataset on Kaggle

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💬 “In fraud detection, recall is king — because missing a fraud costs more than a false alarm.”