March 2025
Problem Statement:
Predicting stock market trends is challenging due to the volatile and complex nature of
financial time-series data. Traditional models often fail to capture long-term dependencies
in historical patterns.
Solution:
Developed a domain-specific stock trend prediction model using LSTM (Long Short-Term Memory)
neural networks trained on historical financial time-series data. Built an interactive
Streamlit dashboard to visualize stock trends, predictions, and model performance metrics
in real-time.
May 2025
Problem Statement:
Extracting meaningful insights from structured, graph-based data requires advanced AI
techniques. Traditional approaches struggle to generate contextual and intelligent
responses from complex node-edge relationships.
Solution:
Built a Graph-based Generative AI system that represents data as interconnected nodes
and edges, leveraging Generative AI techniques to generate meaningful insights and
responses from structured graph data. The system can understand relationships and
produce intelligent, context-aware outputs.
Check out my GitHub profile for additional projects, contributions, and code samples.
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