DATA_SCIENCE_PROJECT
Python
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Credit Card Fraud Detection
Credit Card Fraud Detection
Machine learning project that identifies fraudulent credit card transactions to prevent financial loss.
Key Features
- Anomaly Detection: Identifies outliers in transaction patterns.
- Imbalanced Data Handling: Uses SMOTE/ADASYN techniques.
- Model Comparison: Evaluates Random Forest, Logistic Regression, and SVM.
- Metrics: Focuses on Precision-Recall AUC rather than just accuracy.
Technology Stack
- Language: Python
- Libraries: Scikit-learn, Pandas, Matplotlib
- Notebook: Jupyter
Installation
- Install requirements:
pip install pandas scikit-learn jupyter. - Launch Jupyter Notebook.
- Run the
analysis.ipynbfile.
Project Description
Credit Card Fraud Detection
Machine learning project that identifies fraudulent credit card transactions to prevent financial loss.
Key Features
- Anomaly Detection: Identifies outliers in transaction patterns.
- Imbalanced Data Handling: Uses SMOTE/ADASYN techniques.
- Model Comparison: Evaluates Random Forest, Logistic Regression, and SVM.
- Metrics: Focuses on Precision-Recall AUC rather than just accuracy.
Technology Stack
- Language: Python
- Libraries: Scikit-learn, Pandas, Matplotlib
- Notebook: Jupyter
Installation
- Install requirements:
pip install pandas scikit-learn jupyter. - Launch Jupyter Notebook.
- Run the
analysis.ipynbfile.
Technologies Used
Python
Scikit-learn
Pandas
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