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shahidabatool/README.md

Hi, I'm Shahida Batool 👋

🎓 Master's in Data Science and Analytics @ Toronto Metropolitan University (Graduating September 2026)
📊 Data Analyst & Data Scientist — turning messy data into decisions
🛠️ Data Analytics, NLP, Deep Learning, Computer Vision, and Machine Learning
🌍 Based in Toronto, Canada-

🛠️ Tech Stack

Languages: Python | R | SQL | MATLAB | C++ | PySpark
ML/DL: PyTorch | TensorFlow | Keras | Scikit-learn | PennyLane
NLP & LLMs: LangChain | HuggingFace | FAISS | ChromaDB | Groq | OpenAI | Gemini | Mistral | Claude
Data & Viz: Pandas | NumPy | Matplotlib | Seaborn | Power BI | DAX | Power Query
Databases: SQL Server (T-SQL) | SSMS | SQLite | PostgreSQL
Tools: Git | Docker | Google Colab | Streamlit | Jupyter

🔬 Featured Projects

🏗️ SQL Data Warehouse Project

Modern data warehouse built in SQL Server using the Medallion Architecture (Bronze → Silver → Gold). Covers ETL pipelines, data cleaning/standardization, and a star schema data model exposed as views for analytics.

🩸 Forensic Bloodstain Pattern Analysis (MRP)

Multi-task deep learning framework for forensic bloodstain classification using EfficientNet-B0, ResNet-50, and ViT with physics-constrained HSV/YCrCb preprocessing, Grad-CAM/SHAP explainability, and bloodstain-age estimation. 🚧 Master's Research Project @ TMU

✨ GlowSense

Sentiment analysis on product reviews, using datasets sourced from Hugging Face and Kaggle to classify customer sentiment and surface insights from review text.

🌌 Ising Model Phase Transition — Classical & Quantum ML

CNN, VGG16, and Hybrid Quantum ML models trained to detect phase transitions in the 2D Ising model, benchmarked against Onsager's exact critical temperature (Tc ≈ 2.269). Built with PyTorch and PennyLane.

📚 Academic Literature RAG Assistant

Multi-provider RAG chatbot supporting OpenAI, Gemini, Claude, Mistral, and Groq — built with LangChain, ChromaDB, and HuggingFace embeddings. Supports PDF, DOCX, XLSX, and PPTX ingestion. 🚧 Actively developed as part of MRP at TMU.

🏥 Machine Learning for Health Risk Insights

ML pipeline for health risk prediction using lifestyle data from Kaggle. Covers data preprocessing, feature engineering, and classification models in Python.

📈 What I'm Currently Working On

  • 🧠 MRP Research @ TMU — Bloodstain Pattern Analaysis tool
  • 📖 Finishing Master's in Data Science (August 2026)
  • 🌐 Building my data science/analytics portfolio

🤝 Connect With Me

LinkedIn Email

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  1. Ising-Model-Phase-Transition-Discovery-Classical-and-Quantum-Machine-Learning- Ising-Model-Phase-Transition-Discovery-Classical-and-Quantum-Machine-Learning- Public

    CNN-based phase transition detection on the 2D Ising model, benchmarked against Onsager's exact critical temperature.

    Python 1

  2. Machine-Learning-for-Health-Risk-Insights Machine-Learning-for-Health-Risk-Insights Public

    Machine learning project for health risk insights using lifestyle data from Kaggle.

    Python 1

  3. Recommendation-System-for-Amazon- Recommendation-System-for-Amazon- Public

    Advanced Machine Learning Algorithms for Personalized Product Recommendations

    Python 1

  4. dana1994123/NLP dana1994123/NLP Public

    Jupyter Notebook

  5. GlowSense GlowSense Public

    Python