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1. Spam-detection Spam-detection Public

   Builds a spam detection model using machine learning to classify messages as spam or not spam, demonstrating text preprocessing, feature extraction, and model evaluation.

   Jupyter Notebook 1

2. Anomaly-detection-using-dbscan Anomaly-detection-using-dbscan Public

   Detects anomalies using the DBSCAN algorithm by identifying low-density noise points, with clear visualizations of original data, highlighted outliers, and isolated anomaly points.

   Jupyter Notebook 1

3. DBSCAN_Clustering DBSCAN_Clustering Public

   Applies DBSCAN clustering on two different datasets to identify density-based clusters and compare clustering quality using silhouette scores and visual analysis.

   Jupyter Notebook 1