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taljindergill78/README.md

๐Ÿ‘‹ Hi, I'm Taljinder Singh

๐ŸŽ“ Masterโ€™s Student in Data Science @ Arizona State University (Class of 2026)
๐Ÿง  Machine Learning | NLP | GenAI Systems | Ex-Data Analyst | IIT Roorkee
๐Ÿ“ Tempe, Arizona

LinkedIn GitHub Portfolio


๐Ÿ’ก About Me

Iโ€™m a data scientist in training with a passion for turning real-world complexity into structured, actionable insights.

My journey began at IIT Roorkee, where I developed a strong foundation in analytical thinking. Since then, Iโ€™ve worked on pricing models, time series forecasting, and financial analytics at the enterprise level. These experiences taught me how to use data to solve high-stakes business problems.

Currently, Iโ€™m pursuing a masterโ€™s degree at Arizona State University. My focus is on machine learning, big data systems, and building personalized AI agents that adapt to human behavior. I enjoy solving problems that blend statistical modeling, practical intuition, and clean execution.

If it involves messy data, thoughtful modeling, and clear storytelling, Iโ€™m all in.


๐Ÿ› ๏ธ Tech Stack

Programming & Databases
Python SQL PostgreSQL

Data & Machine Learning
Pandas NumPy Scikit-learn TensorFlow Apache Spark

NLP & LLM Systems
NLP RAG Vector Databases Multi--Agent Systems

Tools & Analytics
Git Tableau


๐Ÿš€ Featured Projects (Pinned on Profile)

Fine-tuned LLMs (LLAMA2, LLAMA3.2, Mistral-7B) for personalized recipe generation using QLoRA. Achieved 96% precision and BLEU 0.52.

Built a collaborative filtering recommendation engine on the Book-Crossing dataset, leveraging Spark MLlib.

Analyzed Yelp data to uncover trends in restaurant performance and user behavior using Spark SQL.

Developed an NLP-based text classification pipeline to identify real-world disaster tweets using TF-IDF features and linear models, with a strong focus on evaluation rigor.

Built a star schema data warehouse for analyzing US weather patterns and air quality indices using SQL Server. Features 7 tables, views, stored procedures, UDFs, cursors, and a complete audit trail system with triggers. IFT 530 Advanced Database Management Systems Final Project.


๐ŸŽฏ Currently Exploring

  • ๐Ÿง  Researching Personalized AI Agents using LLMs (ASU โ€“ under Prof. Ming Zhao)
  • ๐Ÿงฎ Deepening my understanding of Machine Learning via applied projects and Udemy coursework
  • โ˜๏ธ Learning cloud deployment tools (AWS/GCP) to scale ML models
  • ๐Ÿ› ๏ธ Enhancing Git and GitHub workflows for collaborative development

๐Ÿ“ฌ How to Reach Me

If you're working in Machine Learning, AI, or data-driven product development, Iโ€™m always up for meaningful conversations, idea exchanges, or research collaborations. Iโ€™m especially passionate about real-world applications of ML in forecasting, pricing, and personalization. Letโ€™s connect and build something impactful together.

๐Ÿ“ง Email: [email protected]
๐Ÿ”— LinkedIn: linkedin.com/in/taljinder-singh78
๐Ÿ’ป GitHub: github.com/taljindergill78

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  1. AI-Indian-Recipe-Generator AI-Indian-Recipe-Generator Public

    AI-powered system that generates authentic Indian recipes using GPT-2 and BERT-based transformer models.

    Python

  2. yelp-arizona-analysis yelp-arizona-analysis Public

    This project analyzes the Yelp dataset for the state of Arizona to extract insights about restaurant businesses and user behavior. Using Apache Spark and PySpark for distributed data processing, thโ€ฆ

    Jupyter Notebook

  3. disaster-tweets-classification disaster-tweets-classification Public

    NLP-based classification of disaster-related tweets using TF-IDF features, linear models, and ensemble techniques, with systematic evaluation and performance tuning.

    Jupyter Notebook

  4. us-weather-aqi-database us-weather-aqi-database Public

    Star schema data warehouse for US weather & air quality analysis | SQL Server | Features: Views, Stored Procedures, UDF, Cursors, Audit Triggers

    TSQL

  5. book-recommender-system book-recommender-system Public

    A personalized book recommender system that suggests books to users based on their reading preferences and past ratings, using collaborative filtering techniques on the Book Crossing dataset.

    Jupyter Notebook