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Erfan-Moshfeghi/README.md

Hi, I'm Erfan Moshfeghi 👋

Computer Engineering Student | AI/ML & Cybersecurity Learner
Python | Machine Learning | Computer Vision | NLP/RAG | Networking | Cybersecurity

I am building a practical technical portfolio for internship opportunities in AI, software development, networking, and cybersecurity.

My current portfolio is strongest in AI and machine learning, with projects in medical risk prediction, computer vision, document question answering, and e-commerce review intelligence.
I am also expanding my skills in computer networks and cybersecurity to build a broader foundation for future research and industry work.

Current Portfolio Focus

  • Machine Learning and data-driven applications
  • Computer Vision and real-time detection systems
  • NLP / RAG applications with source-grounded answers
  • Clean Python code, testing, documentation, and GitHub workflow
  • Growing focus on networking, Linux, and cybersecurity fundamentals

Featured AI Projects

Project Area Description
Medical Risk Prediction ML Machine Learning Predicts medical risk using classic ML models, evaluation metrics, and a Streamlit app.
Real-Time Driver Drowsiness Detection Computer Vision Detects driver drowsiness in real time using OpenCV, MediaPipe face landmarks, EAR, and webcam/video input.
PDF Question Answering RAG Chatbot NLP / RAG A source-grounded PDF question answering chatbot using Streamlit, semantic embeddings, vector retrieval, and no-answer handling.
Product Review Insight Analyzer NLP / E-commerce Analytics An AI-powered product review analysis dashboard using FastAPI, Streamlit, sentiment analysis, rating insights, buyer summaries, and positive/negative feature tags.

Next Learning Track: Networking & Cybersecurity

I am currently expanding my portfolio toward networking and cybersecurity topics such as:

  • Computer network fundamentals
  • TCP/IP, DNS, HTTP, routing, and basic network protocols
  • Linux command line and system fundamentals
  • Network monitoring and traffic analysis
  • Security fundamentals and defensive cybersecurity concepts

Planned portfolio direction:

Future Project Area Goal
Network Traffic Analyzer Analyze packets and basic network behavior using Python and Wireshark concepts.
Cybersecurity Log Analysis Detect suspicious patterns from system or network logs.
Secure Network Fundamentals Lab Document practical networking and security labs for learning and interviews.

Tech Stack

Programming: Python, basic SQL
AI / Machine Learning: scikit-learn, pandas, NumPy, matplotlib
Computer Vision: OpenCV, MediaPipe
NLP / RAG: Sentence Transformers, TF-IDF, vector search, PDF processing, sentiment analysis, review analytics
Web Apps & Tools: Streamlit, pytest, Git, GitHub, VS Code
Currently Learning: Linux, networking fundamentals, cybersecurity basics

What I am looking for

I am interested in internship opportunities where I can learn, contribute, and grow in one or more of these areas:

  • Artificial Intelligence / Machine Learning
  • Python software development
  • Computer networks
  • Cybersecurity
  • Research-oriented technical projects

Contact

Popular repositories Loading

  1. medical-risk-prediction-ml medical-risk-prediction-ml Public

    End-to-end machine learning project for heart disease risk prediction using Python, scikit-learn, and Streamlit.

    Python

  2. real-time-driver-drowsiness-detection real-time-driver-drowsiness-detection Public

    Real-time driver drowsiness detection using OpenCV, MediaPipe Face Landmarker, EAR, visual/audio alerts, CSV logging, and Streamlit demo.

    Python

  3. pdf-question-answering-rag-chatbot pdf-question-answering-rag-chatbot Public

    A source-grounded PDF Question Answering RAG Chatbot built with Streamlit, semantic embeddings, and vector retrieval.

    Python

  4. Erfan-Moshfeghi Erfan-Moshfeghi Public

  5. product-review-insight-analyzer product-review-insight-analyzer Public

    AI-powered product review insight analyzer using FastAPI, Streamlit, sentiment analysis, rating analytics, buyer summaries, and positive/negative feature tags.

    Python