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Programmer7129/README.md

Hello, I'm Vedant Patel 👋

About Me

I'm a Machine Learning and Artificial Intelligence enthusiast with a proven track record in developing innovative AI solutions and optimizing system efficiency in industry and research settings. Currently interning in a dynamic ML role where I apply automation and advanced pattern recognition techniques, achieving measurable improvements in workflow efficiency. As a Research Assistant at UC Davis, I led the design and deployment of a cutting-edge TinyML project, achieving high accuracy for ECG signal classification and significantly enhancing device efficiency for wearable technology. With a diverse portfolio of projects and strong technical skills in Python, SQL, Next.js, and AWS, I am passionate about harnessing AI to drive meaningful outcomes and advance solutions in real-world applications. I am eager to contribute my expertise and innovative approach to the fast-evolving tech landscape.

  • 🔭 I’m currently working on InvestIQ AI, an AI-driven personal finance web application that offers predictive financial analysis.
  • 🌱 I’m learning Rust, Deep Learning, Qiskit and Horse Riding
  • 👯 I’m looking to collaborate on AI, machine learning, and finance tech projects.
  • 💬 Ask me about Quantum Computing
  • 📫 How to reach me: vsxpatel@ucdavis.edu

Skills

Programming Languages

Python JavaScript Java C++ C# SQL

Frameworks and Libraries

Next.js React Node.js Django Spring Boot

Tools and Platforms

Git Docker AWS Jenkins

💼 Professional Experience

  • Software Engineering Intern, Drevol (Oct 2024 – Present)

    • Developed automation solutions in C# and .NET, optimizing core processes for a 20% efficiency boost.
    • Implemented pattern recognition tools to improve system reliability, reducing bottlenecks by 30%.
    • Deployed ML-driven tools on Azure DevOps, enhancing project tracking accuracy by 15%.

  • Machine Learning Research Assistant, UC Davis (Feb 2024 – Present)

    • Engineered low-power algorithms for ECG classification, achieving 92.8% accuracy.
    • Applied SHAP and LIME techniques for Deep Neural Network optimization, boosting accuracy by 7%.

🚀 Projects

  • InvestIQ AI | Next.js, TailwindCSS, Vercel, NLP

    • Developed a predictive finance app with 90% accuracy, helping users optimize their portfolio performance by up to 15%.
    • Integrated an intelligent chatbot with a 95% query resolution rate and data visualizations that cut decision-making time by 30%.

  • Scream Detection System | Python, MLP, Deep Learning, React

    • Designed a deep neural network to detect distress calls, achieving 93% accuracy by distinguishing acoustic differences.

GitHub Stats

Your GitHub Stats

Connect with Me

LinkedIn Personal Website Email

Pinned Loading

  1. Scream_Detection_AI-ML_Model Scream_Detection_AI-ML_Model Public

    This program uses a deep-neural network to detect the scream and take immediate actions to send help.

    Jupyter Notebook 4

  2. Blogging-Website Blogging-Website Public

    This is a personal blogging website built using NodeJS, MongoDB, and Express

    JavaScript

  3. Breast-Cancer-Prediction-Machine-Learning-Model Breast-Cancer-Prediction-Machine-Learning-Model Public

    This project file demonstrates a machine learning algorithm used for predicting Breast Cancer by taking in several variables and predicting if the breast cancer is benign or malign.

    Jupyter Notebook

  4. Boston-Housing-Prediction-Model Boston-Housing-Prediction-Model Public

    This code file predicts the House prices (MEDV) based on given features.

    Jupyter Notebook

  5. Face-Recognition Face-Recognition Public

    This program helps you to check whether a test image is similar to the image already processed. It also gives you the face distance which if less than 0.6 is similar but if greater than it, the ima…

    Python

  6. Pose-Estimation Pose-Estimation Public

    This program estimates the pose of a person shown in the video - marking specified colored dots on the required joints of our body.

    Python