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About me


  • - 2024
    University at Buffalo, The State University of New York

    Masters of Science: Data Science

     Core courses: Statistical Learning and Data Mining, Programming Database Fundamentals, Introduction to Machine Learning, Data Models and Query Languages, Story-telling through Data Visualization, Probability Theory, Numerical Computing and Mathematics for Data Scientists, Introduction to Computer Vision and Image Processing.

  • - 2022
    Mumbai University

    Bachelors of Engineering: Information Technology

     Core courses: Applied Mathematics, Data Mining and Business Intelligence, Data Structures and Algorithms, Database Management Systems, Big Data Analytics, R programming, Python Programming, Soft Computing, Internet Programming, Software Engineering and Project Management.


  • Analysis of Factors Affecting Poverty in Buffalo

    Python, SQLite3, Pandas, NumPy, Seaborn
     Executed an advanced analysis to uncover factors impacting poverty in Buffalo leveraging Python, SQLite3,
    Pandas, NumPy, and Seaborn.
     Managed, processed, and extracted insights from a staggering 550,000-row dataset originating from Buffalo\’s
    Assessment Rolls (2017-2023).
     Unearthed 46 intricate attributes steering property value and tax dynamics within Buffalo\’s neighborhoods.
     Demonstrated analytical prowess by sculpting 6 distinct SQLite3 tables, accompanied by insightful

  • ResStorage: A Recruitment Portal with Keyword Detection

    Python, spaCy, BeautifulSoup4, Solidity, ReactJS, Flask
     Conceptualized and built ResStorage: A ReactJS and Flask-powered web app combining NLP-based resume
    keyword highlighting, scoring, Ethereum-based document storage, and LinkedIn data verification.
     Designed an NLP model using spaCy to identify job-relevant keywords in resumes.
     Implemented a scoring system leveraging Supervised Learning (Decision Tree classification) for effective resume
     Developed secure Ethereum-based document storage with Solidity for confidential files deploying MetaMask for
     Utilized web scraping with BeautifulSoup and Selenium to improve candidate profile verification through LinkedIn.
     Collected candidate data from LinkedIn to further verify candidate profile using web-scraping.

  • Multiclass Digit Recognition using CNN on SVHN Dataset

    Python, Pytorch
     Modeled a CNN (AlexNet) employing Pytorch to accurately perform digit classification on the SVHN dataset,
    consisting of 10-digit classes with 73,257 training and 26,032 testing digits.
     Performed comprehensive image preprocessing, including resizing, tensor conversion, normalization, and
    efficient data loader implementation deploying Pytorch for streamlined model training.
     Engineered an adaptable AlexNet architecture in PyTorch, optimizing hyperparameters, achieving a remarkable
    model performance with a test accuracy of 93.93%.
     Achieved a robust test accuracy of 91.77% by retraining the model with the augmented dataset, leveraging the
    optimized setup.

  • Fantasy Premier League Analysis

    PostgreSQL, Microsoft Excel, Python
     Employed PostgreSQL, Excel, and Python to dissect 6-season Fantasy Premier League data.
     Structured it ingeniously with normalized tables, each meticulously brought to BCNF form to foster seamless and
    efficient analysis.
     Boosted query performance by strategically deploying B-tree indexing on the pivotal player_id attribute.
     Designed an intuitive Streamlit interface, enhancing the experience for end users.