Venkata Narendra Nuka

This project detects financial fraud using machine learning techniques. It includes a pipeline from data collection and model training to deployment with a Flask web app. Users can input transaction data and receive real-time fraud predictions instantly. The models are trained in Google Colab and deployed locally using Flask for easy access.

This project analyzes CPU scheduling algorithms and system errors by gathering real-time system metrics, training machine learning models, and visualizing performance through an interactive dashboard.

This project analyzes sentiment and topics from social media text (like Twitter) using Natural Language Processing (NLP), machine learning, and deep learning. It identifies public opinions, trending topics, and emotions from the data, and presents the results through an interactive dashboard with charts and word clouds.

This project leverages data collection, SQL preprocessing, and Power BI to deliver in-depth sales analytics and performance optimization. It includes web-scraped news data, sales trends, and insights into Power BI performance using the Performance Analyzer.

An AI-powered system detects triple riding on two-wheelers using DETR for object detection, CNN for rider counting, and OCR for license plate recognition. It ensures real-time, scalable traffic monitoring with temporal validation to reduce false positives, enhancing enforcement, safety, and public accountability.

An Autonomous SRE System powered by a Tri-Brain Architecture: Reflex (Transformer), Reasoning (SLM), and Adaptive (Reinforcement Learning).

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