🌐 Technical Head at UPES-CSA | 💻 MERN Stack Developer | 🔍 Aspiring AI/ML Specialist | 📚 Pre Final year B.Tech in Computer Science Engineering (CSE)
I'm a passionate and results-driven tech enthusiast with a strong foundation in software development and a specialization in AI/ML. Proficient in the MERN stack, Python, JavaScript, Java, and SQL, I enjoy creating scalable, impactful solutions—especially those focused on data-driven systems and database management.
As the Technical Head at UPES-CSA, I lead all technical operations, oversee the development and management of the official website, and guide the core team in building innovative projects while fostering a culture of learning, collaboration, and growth.
- Deepening my expertise in Artificial Intelligence and Machine Learning (AI/ML) to excel in predictive analysis, model creation, and data-driven solutions.
- Expanding my practical knowledge of the MERN Stack by developing hands-on projects that showcase dynamic, responsive web applications.
- AI/ML & Data Science: Scikit-learn, TensorFlow, Keras, Pandas, NumPy, Matplotlib, Seaborn, Model Deployment
- Backend Development: Node.js, Express.js, REST APIs, Google Apps Script
- Frontend Development: React.js, Next.js, Tailwind CSS, Responsive Web Design, HTML, CSS, JavaScript
- Database Management: MongoDB, MySQL, SQL
- Programming Languages: Python, JavaScript, Java, C
- Tools & Platforms: Git, GitHub, Streamlit, Docker (basic), VS Code
- Core CS Skills: Data Structures & Algorithms (DSA), Problem Solving
- Hackathon Website for UPES-CSA Hackathon 2023: Played a key role in designing and optimizing the UI for seamless event registration and user experience.
- UPES-CSA Website: Spearheaded design and functionality enhancements, streamlining event registration and maximizing user engagement.
- Dog vs Cat Image Classifier
- Technology Stack: TensorFlow, Keras, Streamlit, Python
- Built a CNN-based image classifier to distinguish between dog and cat images using the Kaggle "Dogs vs. Cats" dataset. Achieved 81.42% accuracy, with model training and evaluation performed in Jupyter Notebook. Deployed via Streamlit, allowing users to upload an image and receive real-time predictions with confidence scores.
- Technology Stack: React.js, Material-UI
- Created a dashboard displaying COVID-19 statistics, including active cases, deaths, and recoveries, powered by the World Health Organization (WHO) API.
- Temperature Prediction App
- Technology Stack: Python, Streamlit, Scikit-learn
- Built a machine learning web app that predicts temperature based on weather parameters (humidity, dew point, wind speed, etc.) using a Random Forest Regressor (R²: 0.94). Deployed via Streamlit with real-time inputs and interactive visualizations using Plotly and Matplotlib.
- Email: rudragupta0123@gmail.com
- LinkedIn: Rudra Gupta on LinkedIn