DATA_SCIENCE_PROJECT
Python
0.0(0 reviews)
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.
Academic integrity matters.
Our support is designed for study guidance, editing, structure examples, research support, proofreading and learning assistance. You should use any model material ethically and follow your university's academic-integrity policy.
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
Customer Reviews
No reviews yet. Be the first to review this project!
Instant Download
$79.99
Secure payment via Stripe. Source code included.
Deliverables
Files available after purchase.
Full Source Code
Documentation Included