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Vivek P. - Fullstack Developer, Image Classification, Web Scraping

At Softaims, I have been fortunate to work in an environment that values creativity, precision, and long-term thinking. Each project presents a unique opportunity to transform abstract ideas into meaningful digital experiences that create real impact. I approach every challenge with curiosity and commitment, ensuring that every solution I design aligns not just with technical requirements, but also with human needs and business objectives. One of the most rewarding aspects of my journey here has been learning how to bridge the gap between innovation and practicality. I believe technology should simplify complexity, enhance efficiency, and empower people to do more with less friction. Whether building internal systems, optimizing workflows, or helping bring client visions to life, my focus remains on developing solutions that stand the test of time. Softaims has encouraged me to grow beyond coding—to think about design, communication, and sustainability in technology. I see every project as part of a larger ecosystem, where small details contribute to long-lasting results. My daily motivation comes from collaborating with people who share the same passion for doing meaningful work, and from seeing the tangible difference our efforts make for clients around the world. More than anything, I value the culture of learning and improvement that defines Softaims. It’s a place where ideas evolve through teamwork and constructive feedback. My goal is to continue refining my craft, exploring new approaches, and contributing to solutions that are not only efficient but also elegant in their simplicity.

Main technologies

  • Fullstack Developer

    2 years

  • Machine Learning

    1 Year

  • Artificial Intelligence

    1 Year

  • Computer Vision

    1 Year

Additional skills

  • Machine Learning
  • Artificial Intelligence
  • Computer Vision
  • Python
  • Image Processing
  • YOLO
  • OpenCV
  • Python Script
  • Python Scikit-Learn
  • PyTorch
  • TensorFlow
  • Deep Learning
  • Object Detection & Tracking
  • Image Classification
  • Web Scraping

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Experience Highlights

Project Planning Genie

This project planning agent helps developers and teams break down complex project ideas into manageable, step-by-step implementation plans. By leveraging advanced AI agents and workflow orchestration, it generates detailed project roadmap that can be directly used as GitHub issues, project boards, or documentation.

Heart Attack Predictor End.2-End ML

The heart attack datasets were collected at Zheen hospital in Erbil, Iraq, from January 2019 to May 2019. The attributes of this dataset are: age, gender, heart rate, systolic blood pressure, diastolic blood pressure, blood sugar, ck-mb and troponin with negative or positive output. According to the provided information, the medical dataset classifies either heart attack or none. The gender column in the data is normalized: the male is set to 1 and the female to 0. The glucose column is set to 1 if it is > 120; otherwise, 0. As for the output, positive is set to 1 and negative to 0.

Yoga Pose Estimation with YOLO

This project focuses on training and evaluating a YOLO (You Only Look Once) model for yoga pose estimation. It utilizes the Ultralytics framework to streamline the training and validation process.

🧾 Invoice Data Extraction Using OCR

This project automates the extraction of key client information (such as name, address, and tax ID) from invoice images using Optical Character Recognition (OCR). It's designed to efficiently process a batch of invoice images, intelligently crop relevant sections, extract the structured data, and then compile it into a clean Excel spreadsheet for easy analysis.

chest-x-ray-diagnosis using DenseNet

ChestX-ray8 dataset which contains 108,948 frontal-view X-ray images of 32,717 unique patients. Each image in the data set contains multiple text-mined labels identifying 14 different pathological conditions. These in turn can be used by physicians to diagnose 8 different diseases. We will use this data to develop a single model that will provide binary classification predictions for each of the 14 labeled pathologies. In other words it will predict 'positive' or 'negative' for each of the pathologies. Sample datasets is downloaded from kaggle

Education

  • FH Joanneum

    Master of Science (MS) in

    2016-01-01-2019-01-01

  • Nirma University, Ahmedabad, Gujarat, India

    Bachelor of Technology (BTech) in

    2012-01-01-2015-01-01

  • A.V.Parekh Technical Institute,Rajkot 602

    Bachelor's degree in

    2007-01-01-2011-01-01

Languages

  • German
  • English
  • Hindi

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