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Rahilshah01/README.md

💫 About Me:

👋 Hi, I'm Rahil Shah
🎓 MS in Data Science Student @ Stevens Institute of Technology


I am an aspiring Data Scientist and Machine Learning Engineer with a passion for turning raw data into actionable insights. Currently, I am seeking full-time opportunities or internships in Data Science, Data Analytics, or Machine Learning.

🌐 Socials:

LinkedIn email

💻 Tech Stack:

Python R Dart AWS Azure Apache Hadoop nVIDIA Django Flutter OpenCV Streamlit AmazonDynamoDB Postgres MySQL MicrosoftSQLServer SQLite Keras Matplotlib NumPy Pandas Plotly PyTorch scikit-learn Scipy TensorFlow mlflow GitHub Git Docker Kubernetes Power Bi

📊 GitHub Stats:

✍️ Random Dev Quote

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  1. medical-rag-intelligence-system medical-rag-intelligence-system Public

    A specialized RAG (Retrieval-Augmented Generation) system for healthcare. Uses Gemini 2.0 Flash and FAISS vector search to provide grounded, evidence-based answers with zero-hallucination guardrails.

    Jupyter Notebook

  2. autonomous-financial-research-agent autonomous-financial-research-agent Public

    An autonomous multi-agent system built with CrewAI and Gemini 2.0 Flash to automate financial market research. Features specialized agents for real-time data analysis and technical report writing.

    Jupyter Notebook

  3. multimodal-vision-ai-chat multimodal-vision-ai-chat Public

    A high-performance Multimodal AI Chatbot using Gemini 2.0 Flash to perform visual reasoning, OCR, and object detection. Features robust exponential backoff logic to handle API rate limits and token…

    Jupyter Notebook 2

  4. customer-churn-prediction-api customer-churn-prediction-api Public

    A production-ready machine learning microservice that uses FastAPI and Docker to serve a Random Forest model for predicting customer churn in real-time

    Jupyter Notebook

  5. urban-traffic-vision-yolov8 urban-traffic-vision-yolov8 Public

    Real-time traffic analytics engine using YOLOv8. Detects, tracks, and counts vehicles in city footage to identify peak congestion patterns and optimize urban throughput.

    Jupyter Notebook

  6. credit-card-fraud-detection credit-card-fraud-detection Public

    End-to-end fraud detection pipeline using XGBoost and SMOTE. Achieved an AUPRC of 0.85 by optimizing for precision-recall trade-offs in highly imbalanced financial transaction data.

    Jupyter Notebook