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1. 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

2. 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

3. Fake-News-Detection-using-LSTM-and-BiLSTM Fake-News-Detection-using-LSTM-and-BiLSTM Public

   Compares LSTM and Bidirectional LSTM models for fake news detection, highlighting how bidirectional context improves text classification performance using deep learning.

   Jupyter Notebook 1

4. sms-spam-classification-transformer sms-spam-classification-transformer Public

   SMS spam classification using a Transformer-based model built with HuggingFace and PyTorch, demonstrating modern NLP techniques for contextual text classification and model evaluation using a confu…

   Jupyter Notebook 1