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Used PCA, t-SNE for dimensionality reduction, Isolation Forest for anomaly detection, and K-Means/DBSCAN for clustering. Applied Hampel filter for noise correction and evaluated performance using Random Forest-derived silhouette scores.\n---\n\n## 🧠 Key Concepts\n\n- **Unsupervised Learning**: No labeled data required.\n- **Dimensionality Reduction**: Visualization and structure discovery.\n- **Clustering \u0026 Isolation**: Identify anomalous transactions.\n- **Feature Analysis**: Understand key drivers of anomalies.\n\n---\n\n## 🚀 Technologies \u0026 Libraries\n\n- Python 3.x\n- NumPy / Pandas\n- Scikit-learn\n- Matplotlib / Seaborn\n- t-SNE / PCA\n- Isolation Forest / DBSCAN / K-Means\n- Hampel Filter for outlier preprocessing\n\n---\n\n## 📊 Pipeline Overview\n\n### 1. 📂 Data Preprocessing\n\n- Transaction data is cleaned and normalized.\n- **Hampel filter** is applied to remove extreme outliers and reduce noise.\n\n### 2. 🔻 Dimensionality Reduction\n\n- **PCA** is used to reduce feature space while retaining variance.\n- **t-SNE** helps in visualizing complex, high-dimensional patterns.\n\n### 3. 📌 Clustering for Pattern Discovery\n\n- **K-Means Clustering** for identifying common behavior groups.\n- **DBSCAN** for density-based anomaly detection and noise separation.\n- **Silhouette Score** is used to evaluate cluster quality.\n\n### 4. 🚨 Outlier Detection\n\n- **Isolation Forest** detects anomalous transactions by isolating rare patterns.\n\n### 5. 📈 Feature Importance\n\n- A **Random Forest** model ranks the most influential features post-clustering to help interpret anomaly causes (e.g., transaction value, frequency, mining difficulty, sentiment metrics).\n\n---\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkrishaa1803%2Fbitcoin-transaction-anomaly-detection-using-unsupervised-machine-learning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkrishaa1803%2Fbitcoin-transaction-anomaly-detection-using-unsupervised-machine-learning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkrishaa1803%2Fbitcoin-transaction-anomaly-detection-using-unsupervised-machine-learning/lists"}