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Vaisakh-Nirupam/README.md

💫 About Me:

Hi, I’m Vaisakh Nirupam — a BCA graduate and job-ready Data Scientist from Kerala, India.

I specialize in building real-world, data-driven applications using Python, Streamlit, and Flask. With hands-on experience in ML, data pipelines, and dashboard development, I love transforming raw data into insights and interactive tools.

Currently interning at Labmentix, I work with real-world datasets and gain professional-level exposure to industry practices.

I earned my Data Science certification from Luminar Technolab, where I built strong foundations and later stepped up as a team lead.

Beyond data, I also have experience in game and web development — and I’m passionate about combining these skills to solve real-world problems.

🌐 Socials:

LinkedIn email Instagram

💻 Tech Stack:

Python Flask Jinja Streamlit HTML5 CSS3 JavaScript SQLite MySQL Pandas NumPy Matplotlib scikit-learn Scipy TensorFlow OpenCV Keras Git GitHub Canva

📊 GitHub Stats:

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  1. Yes_Bank_Stock_Closing_Price_Prediction Yes_Bank_Stock_Closing_Price_Prediction Public

    ML and EDA-based project predicting Yes Bank’s monthly closing prices with real-world data insights, hypothesis testing, and model evaluation.

    Jupyter Notebook

  2. Task_Manager Task_Manager Public

    Upgraded version of my original Flask-based Task Manager app, enhanced with improved UI, better routing, and new features for managing tasks more efficiently using MySQL and custom frontend design.

    HTML

  3. PhonePe_Transaction_Insights PhonePe_Transaction_Insights Public

    Analyzed and visualized PhonePe transaction data using SQL, Python, and Streamlit to extract user behavior, payment trends, and regional insights, enabling smarter financial decision-making through…

    Jupyter Notebook

  4. Loan_Status_Prediction Loan_Status_Prediction Public

    A machine learning model integrated with an interactive and responsive Flask web app to collect user data and predict loan eligibility status.

    Jupyter Notebook

  5. Shopper_Spectrum Shopper_Spectrum Public

    Developed a customer segmentation and product recommendation system using RFM clustering (KMeans) and item-based collaborative filtering with cosine similarity, deployed via Streamlit.

    Jupyter Notebook

  6. Book_Recommendation_System Book_Recommendation_System Public

    An NLP-powered book recommendation system built using a dataset scraped and cleaned with my earlier python web scraper project.

    Jupyter Notebook