About

I am C Gowtham, a recent graduate with a Bachelor's in Computer Engineering from K S School of Engineering & Management, Bangalore South, completed in 2026. During my studies, I worked on projects such as NGOCONNECT, a role-based web application, and a Machine Learning-based fraud detection system, gaining hands-on experience in software development and data analysis. I also completed an internship as a Software Development Intern at Destination Career, where I worked on web applications using Core Java and SQL. My strongest skills include Python, Java, SQL, JavaScript, Git, and tools like Jupyter, VS Code, and Vercel. I am now looking for my first full-time role as a Software Engineer.

Experience

1. Software Development Intern

   Destination Career

   January 2026 – March 2026

   Worked on developing and testing web applications using Core Java and modern web development concepts. Implementing backend logic and integrating database connectivity using SQL for efficient data management. Exploring the use of Generative AI tools and APIs to enhance application functionality. Collaborating with team members to debug issues, optimize code, and improve overall application performance.

Education

1. K S School of Engineering & Management, Bangalore South

   Bachelor's, Computer Engineering

   2022 – 2026 · Grade: 9.0

2. Vijayam Junior College

   Certification, MPC

   2020 – 2022 · Grade: 9.0

3. Sri Saraswati Vidya Niketan

   Certification, Mathematics

   2019 – 2020 · Grade: 9.6

Projects

• NGOCONNECT - Link to project

  • Python
  • Java
  • SQL
  • JavaScript
  • Git
  • Jupyter
  • VS Code
  • Vercel
  • Netlify
  • Eclipse

  Developed NGO-CONNECT, a role-based web application that brings NGOs, volunteers, and donors together on a single platform to improve collaboration and social impact. The application supports volunteer management, donation tracking, and transparent data handling through structured workflows.

• Fake Transaction Detection System

  • Python
  • Scikit-learn

  Built a Machine Learning-based fraud detection system using Python and Scikit-learn. Applied data preprocessing, feature engineering, and classification algorithms to detect anomalies in financial transactions with high accuracy.