Shyam Kiran

This project is to develop a stock price prediction tool that forecasts future prices using historical market data, supported by a robust data engineering framework. It features ETL data pipelines for efficient data ingestion from yfinance and incorporates real-time data streaming with Apache Kafka, seamlessly integrated with Python. The infrastructure enables next-minute trading price predictions using efficient strategies like Long Short-Term Memory (LSTM) neural networks.Data is stored and managed using PostgreSQL, while AWS services provide scalable cloud storage and computing resources. The tool focuses on capturing temporal dependencies in time-series data to achieve high precision in predictions. By providing data-driven support, it helps investors and financial analysts anticipate short-term market trends, refine trading strategies, and make informed investment decisions. Working on the enhancements to include integrating additional financial metrics like trading volume and technical indicators (RSI, MACD), deploying the tool as a web-based application with real-time predictions and user-friendly visualizations, and expanding its accessibility to a broader audience within financial markets.

Developed COVID safety tool using Raspberry Pi and Machine Learning to detect mask usage and fever symptoms in real time, minimizing human contact during viral outbreaks. This system features RBAC control, a user-friendly interface, and achieved 99.4% accuracy , providing real-time health insights, alerts, and access restrictions. I have Successfully deployed in my school campus to enhance safety during critical times. The primary purpose of this tool is to improve safety in public and corporate spaces by automating health monitoring. It enables rapid detection of potential risks, proactive emergency alerts, and restricted access for symptomatic individuals, reducing the spread of infectious diseases. For Future enhancements, I am working on to include advanced health metrics like heart rate and oxygen levels, integrating cloud-based analytics for centralized monitoring, deploying AI-driven models for better adaptability, and creating a mobile app for real-time alerts and data access on-demand.