Skip to content
View harshpate1's full-sized avatar
🤖
🤖
9 followers · 19 following

Block or report harshpate1

Block user

Prevent this user from interacting with your repositories and sending you notifications. Learn more about blocking users.

You must be logged in to block users.

Maximum 250 characters. Please don’t include any personal information such as legal names or email addresses. Markdown is supported. This note will only be visible to you.
Report abuse

Contact GitHub support about this user’s behavior. Learn more about reporting abuse.

Report abuse
harshpate1/README.md

Hey, I'm Harsh 👋

I graduated with a B.Tech in Artificial Intelligence and Data Science in 2025.

Right now I'm preparing for GATE 2027 while learning things that interest me along the way. Most of my time is split between studying, messing around with Linux, trying out new FOSS software, and occasionally breaking my setup while ricing it.

I use Arch Linux with Hyprland (btw)

Current questline

  • Preparing for GATE 2027
  • Learning DSA
  • Learning modern C++
  • Exploring Arduino and embedded projects
  • Building a stronger foundation in computer science

Likes

  • Machine Learning
  • Data Science
  • Linux
  • Open Source Software
  • Self-hosting
  • Arduino
  • TUI Apps
  • Learning random stuff

Tech I use

  • Python
  • Linux (Arch)
  • Hyprland
  • Git
  • VS Code

Goal

I'd like to work as a Data Scientist or ML Engineer someday. For now, I'm focused on learning, building projects, and improving my fundamentals.

If you're here because of a repository, feel free to open an issue or suggest improvements.

Pinned Loading

  1. Clinical-Diabetes-Risk-Assessment-with-Interpretable-Machine-Learning Clinical-Diabetes-Risk-Assessment-with-Interpretable-Machine-Learning Public

    An interpretable ML system for diabetes risk prediction using clinical data. Features SHAP explanations, model comparison (Random Forest vs XGBoost), and a deployment-ready pipeline. Achieves 0.85 …

    Jupyter Notebook 2

  2. HappinessInsights2020 HappinessInsights2020 Public

    Analyzing global happiness during COVID-19: A data science project exploring the World Happiness Report 2020. Features interactive visualizations, predictive modeling, and policy insights.

    Jupyter Notebook