This project focuses on identifying anomalies (outliers) in datasets using Python. It utilizes data visualization and machine learning techniques to detect unusual patterns that could indicate errors, fraud, or other significant events.
Anomaly detection plays a crucial role in domains like finance, cybersecurity, manufacturing, and healthcare. This notebook walks through the steps of importing data, performing exploratory data analysis (EDA), and applying methods to detect anomalies.
- Data loading and preprocessing
- Visualization to understand data distribution
- Statistical methods for outlier detection
- Machine learning models for anomaly detection
- Interpretation and evaluation of results
- Python
- Jupyter Notebook
- Pandas
- NumPy
- Matplotlib
- Seaborn
- Scikit-learn
🧠 Sample Algorithms Used Z-score
IQR (Interquartile Range)
Isolation Forest
DBSCAN 📌 Use Cases Fraud Detection in Finance
Fault Detection in Machines
Network Intrusion Detection
Quality Control in Manufacturing
🤝 Contributing Contributions are welcome! Please fork the repo and submit a pull request.