I am a passionate developer skilled in Flutter, MERN stack, and machine learning, currently pursuing a B.Tech in Artificial Intelligence and Data Science at VIT Pune. My journey in tech has been enriched with hands-on experience in web and mobile development, and a deep dive into the world of machine learning. Beyond coding, I am enthusiastic about community engagement, technical events, and exploring new technologies that shape the future.
- 🌐 Web Development (MERN Stack)
- 📱 Mobile App Development (Flutter)
- 🧩 Data Structures and Algorithms
- 🤖 Artificial Intelligence
- 📈 Machine Learning
- 💡 Innovation and Community Building
- 🔍 Advanced Machine Learning Techniques
- 🖥️ MERN Stack Development
- 📱 Advanced Flutter Animations and State Management
May 2024
Developed a machine learning-based system with a MERN stack interface to recommend GitHub repositories for open-source contributions.
Technologies: Python, TF-IDF, LDA, BERT, Cosine Similarity, GitHub API, MongoDB, Express.js, React, Node.js
- Automated metadata retrieval with Python scripts.
- Applied NLP techniques, including TF-IDF, LDA, and BERT, for text preprocessing and keyword extraction.
- Enhanced recommendation accuracy using Cosine Similarity, clustering, and BERT-based embeddings.
Outcome: Provided personalized repository suggestions, improved user experience, and encouraged open-source contributions.
Dec 2023
Developed a Flutter application integrated with a machine learning model to detect 120 unique dog breeds from images.
Technologies: Python, TensorFlow, Flutter, Firebase
- Trained a CNN with transfer learning using TensorFlow Hub, classifying 120 dog breeds from 10,000+ images.
- Created a real-time breed detection app in Flutter with TensorFlow Lite for efficient on-device predictions.
- Integrated Firebase for data management and authentication, allowing seamless image uploads and breed predictions.
Outcome: Created an interactive mobile app that accurately identifies dog breeds, enhancing user engagement with a responsive UI.
Dec 2023
Created a model to predict flood-affected areas using neural networks and geospatial data.
Technologies: Python, Scikit-learn, NetCDF4, Cartopy, Folium
- Utilized historical geospatial data (aspect, elevation, slope, TWI, etc.) to identify vulnerable areas.
- Applied machine learning algorithms for analyzing susceptibility, enhancing prediction accuracy.
- Developed interactive visualizations with Cartopy and Folium, highlighting flood-prone regions.
Outcome: Enhanced accuracy in flood prediction and provided actionable insights through visualizations.
- ✌️ I’m looking to collaborate on innovative AI, ML, and web development projects that push the boundaries of technology.
- 🤝 If you are working on a project that could benefit from my expertise or if you have an exciting idea for collaboration, let's connect!
- 📧 Email: cdingale04@gmail.com
- 😄 Pronouns: He/Him
Together, let's learn and contribute!