Projects and Publication

Machine Learning-Based Predictive Analytics (from Aglow Engineers)

Overview

Developed and deployed an AI-driven predictive analytics model to enhance data security and governance. This project focused on identifying vulnerabilities, reducing risks, and improving system efficiency through machine learning algorithms.

Key Contributions

• Developed AI-Driven ETL Pipelines: Integrated Python-based ETL (Extract, Transform, Load) processes with ElasticSearch, optimizing data retrieval and processing efficiency.

• Security Threat Detection: Implemented anomaly detection models using machine learning to identify potential security vulnerabilities, reducing risk by 15%.

• Automated Predictive Insights: Used supervised learning algorithms to forecast system anomalies before they occurred, ensuring proactive security measures.

• Real-time Dashboard Integration: Designed interactive Tableau dashboards connected with Oracle and SQL Server, cutting reporting time by 10%.

Tech Stack & Tools

🔹 Programming: Python (Pandas, NumPy), SQL
🔹 ML Frameworks: Scikit-learn, TensorFlow
🔹 Data Engineering: PostgreSQL, ElasticSearch
🔹 Visualization: Tableau

Impact

• Improved data retrieval efficiency and system security by integrating machine learning models into business intelligence workflows.

• Enabled real-time monitoring and proactive threat detection, reducing security risks.

• Enhanced operational efficiency, leading to a 10% decrease in reporting delays.

Surface Plasmonic Resonance for Biomarker Detection