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CS & statistics | Data Science & Machine Learning
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CS & statistics | Data Science & Machine Learning

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manasmehra-rgb/README.md

Hi, I'm Manas Mehra!


A little more about me…

I’m a double major in Statistics & Data Science and Computational Mathematics at UMass Amherst, focused on building practical systems in machine learning, data analysis, and applied software engineering.

I’ve worked on end-to-end data science and ML projects, including publication-grade statistical modeling of 1,200+ NCAA Division I football games, deploying a breast cancer recurrence prediction app, and implementing reinforcement learning algorithms from scratch. I care about clean data pipelines, rigorous modeling, and reproducible results.

On campus, I’m one of 25 selected Peer Mentors, supporting hundreds of first-year students through academic mentoring and community programs.

Currently, I’m deepening my skills in ML systems, statistical modeling, and data-driven applications while preparing for roles in AI, data science, and quantitative tech.

Outside of tech, I follow Basketball (Celtics all the way ☘️), love Chess ♟️, and solve linkedin puzzles 🧩. I geek out on anime sometimes.


Languages

Python Java JavaScript TypeScript HTML5 CSS3


Frameworks & Data

MySQL scikit-learn pandas


Tools & Platforms

Visual Studio Google Colab Git

Pinned Loading

  1. Crewlytics Crewlytics Public

    Architected an automated resource optimization platform that identifies team bottlenecks and suggests reassignments using a custom TypeScript matching algorithm

    TypeScript 1

  2. tic-tac-toe-learning-agent tic-tac-toe-learning-agent Public

    Reinforcement learning agent implementing MENACE to learn optimal Tic-Tac-Toe strategies

    Jupyter Notebook 1

  3. breast-cancer-predictor breast-cancer-predictor Public

    Supervised machine learning model for breast cancer recurrence with class imbalance handling and performance evaluation

    Jupyter Notebook

  4. fanPower-mls fanPower-mls Public

    Regression-based analysis of MLS attendance and home-team performance using match-level data

    Jupyter Notebook

  5. 2D-percolation-simulation 2D-percolation-simulation Public

    Computational simulation of 2D site percolation to study phase transition behavior

    Jupyter Notebook

  6. cryptogram-decoder cryptogram-decoder Public

    A cryptogram solver that uses statistics, language patterns, and randomness to uncover hidden messages

    Jupyter Notebook