Bitcoin-Transaction-Anomaly-Detection-using-Unsupervised-Machine-Learning

Built an unsupervised Machine Learning pipeline to detect anomalies in Bitcoin transactions by selecting 19 key features from 700. 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.

🧠 Key Concepts

• Unsupervised Learning: No labeled data required.
• Dimensionality Reduction: Visualization and structure discovery.
• Clustering & Isolation: Identify anomalous transactions.
• Feature Analysis: Understand key drivers of anomalies.

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🚀 Technologies & Libraries

• Python 3.x
• NumPy / Pandas
• Scikit-learn
• Matplotlib / Seaborn
• t-SNE / PCA
• Isolation Forest / DBSCAN / K-Means
• Hampel Filter for outlier preprocessing

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📊 Pipeline Overview

1. 📂 Data Preprocessing

• Transaction data is cleaned and normalized.
• Hampel filter is applied to remove extreme outliers and reduce noise.

2. 🔻 Dimensionality Reduction

• PCA is used to reduce feature space while retaining variance.
• t-SNE helps in visualizing complex, high-dimensional patterns.

3. 📌 Clustering for Pattern Discovery

• K-Means Clustering for identifying common behavior groups.
• DBSCAN for density-based anomaly detection and noise separation.
• Silhouette Score is used to evaluate cluster quality.

4. 🚨 Outlier Detection

• Isolation Forest detects anomalous transactions by isolating rare patterns.

5. 📈 Feature Importance

• 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).

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