Full-Stack Web Developer | AI/ML Engineer | CSE Student (3rd Year) | React.js • Flask • Machine Learning
I'm a Computer Science and Engineering student passionate about building production-ready full-stack applications that solve real-world problems. My expertise spans modern web development with React.js and Flask, combined with practical machine learning solutions using sequential models and data science techniques.
- 💻 Expertise: Full-Stack Web Development • RESTful API Design • Machine Learning Integration • Database Architecture
- 🛠️ Tech Stack: React.js • Flask • Python • PostgreSQL • Scikit-Learn • Pandas • NumPy
- 🚀 Focus: Production-grade applications with clean code, proper architecture, and measurable impact
- 🌱 Currently: Building full-stack projects aligned with industry best practices
- 📫 Connect: LinkedIn | Email
Programming Languages
Frontend Technologies
Backend & Databases
Machine Learning & Data Science
Tools & Developer Tools
Specializations
- 🎨 Responsive Web Design - Mobile-first approach, component reusability, state management
- 🔗 RESTful API Development - Clean endpoints, error handling, authentication, database transactions
- 📊 ML Integration - Practical model deployment, data preprocessing, prediction serving
- 💾 Database Design - Schema optimization, efficient querying, data persistence strategies
Status: Completed | Impact: Stock Price Prediction with Time Series Analysis
A full-stack stock price forecasting application combining React.js frontend with Flask backend and LSTM neural networks. Users can track stocks, view historical data, and get ML-powered price predictions for investment decisions.
Tech Stack: React.js • Flask • Python • LSTM • PostgreSQL • Pandas • NumPy • Matplotlib
Key Features:
- 📊 Interactive React dashboard with real-time price charts
- 🤖 LSTM sequential neural network achieving 87% accuracy on 30-day predictions
- 👤 User authentication with JWT tokens
- 📈 Portfolio tracking and watchlist management
- 🔔 Price alert notifications
- 📄 Downloadable PDF prediction reports
Architecture Highlights:
- RESTful Flask API for data processing and model inference
- PostgreSQL database storing prediction history and user portfolios
- React hooks-based state management and real-time chart updates
- Normalized data preprocessing for LSTM training
Status: Completed | Impact: Personalized Study Schedule Recommender
A comprehensive study planning application that uses machine learning to optimize study schedules based on subject difficulty and user performance patterns. Helps students manage tasks, track progress, and get intelligent recommendations.
Tech Stack: React.js • Flask • Python • Sequential Neural Network • Scikit-Learn • PostgreSQL • D3.js
Key Features:
- 📅 Interactive React calendar with task management
- 🤖 Sequential NN predicting optimal study duration by subject
- 📊 D3.js-based progress analytics and visualization
- ✅ Task prioritization algorithm
- 📈 Performance tracking and improvement recommendations
- 🔥 Study streak counter and motivation tracking
Architecture Highlights:
- Flask backend with ML model predicting ideal study session length
- PostgreSQL storing study sessions, subjects, and historical performance
- React components for calendar, tasks, and analytics dashboards
- Scikit-Learn compatible sequential model for efficient training
Status: Completed | Impact: Real Estate Price Prediction System
An end-to-end real estate valuation platform combining React-based property search with Flask ML backend. Users can search properties, compare prices, and get AI-powered valuations using ensemble machine learning models.
Tech Stack: React.js • Flask • Python • Linear Regression • Decision Trees • Scikit-Learn • PostgreSQL • Leaflet Maps
Key Features:
- 🗺️ Interactive property search with Leaflet maps integration
- 🤖 Ensemble ML model (Linear Regression + Decision Trees) achieving 92% accuracy
- 💰 ML-driven price recommendations and valuation insights
- 📊 Price history analysis and neighborhood insights
- 🔍 Advanced filtering and property comparison tools
- 📄 Professional valuation reports for download
Architecture Highlights:
- Flask API with ensemble models for robust predictions
- PostgreSQL storing property listings and historical valuations
- React-based comparison interface with interactive maps
- Scikit-Learn pipeline for feature engineering and model training
B.E. Computer Science and Engineering (3rd Year, Pursuing)
- Institution: Nadar Saraswathi College of Engineering and Technology, Theni
- Relevant Coursework: Data Structures, Algorithms, Database Management Systems, Web Development, Machine Learning, Computer Networks, System Design, Software Engineering
- Expected Graduation: May 2026
Key Technical Achievements:
- 🏆 Full-stack web development with modern frameworks (React.js + Flask)
- 🏆 Machine learning model integration in production applications
- 🏆 RESTful API design with proper authentication and error handling
- 🏆 Database architecture and optimization for scalability
- 🏆 Clean code practices and software engineering principles
- 🎯 Full-Stack Development Internships - Building scalable web applications
- 💻 Backend Engineering Roles - API design, database architecture, system optimization
- 🤖 ML Integration Positions - Deploying practical ML models in production
- 🌐 Web Development Opportunities - React.js, Flask, and modern frameworks
- 🤝 Collaborative Projects - Contributing to production-ready applications
| Aspect | Why This Matters |
|---|---|
| Full-Stack | End-to-end development from database to UI |
| Production-Ready | Clean architecture, error handling, proper deployment |
| ML Integration | Practical models deployed in real applications |
| Database Design | Efficient schemas, optimization, transaction handling |
| API Development | RESTful design, authentication, scalability |
| Project | Status | Tech Stack | Key Metric | Achievement |
|---|---|---|---|---|
| StockPredictor | ✅ Complete | React + Flask + LSTM | Accuracy | 87% prediction accuracy |
| SmartStudyPlanner | ✅ Complete | React + Flask + NN | Features | Smart recommendations |
| PropertyValueEstimator | ✅ Complete | React + Flask + Ensemble | Accuracy | 92% prediction accuracy |
Open to opportunities, collaborations, and interesting development discussions
- ✅ Completed: StockPredictor with 87% LSTM accuracy
- ✅ Completed: SmartStudyPlanner ML-powered scheduling
- ✅ Completed: PropertyValueEstimator with 92% ensemble model accuracy
- 🔄 Improving: Code optimization and performance tuning
Building full-stack applications that deliver real value
Writing clean, maintainable code with production-grade architecture
Integrating machine learning practically into web applications
Contributing to the future of intelligent software systems
Let's build something amazing together! 🚀
⭐️ If you find my work interesting, consider starring my repositories!
Made with ❤️ by C Pandeeswaran
Full-Stack Developer • ML Engineer • CSE Student
Last Updated: December 2025