Java Software Developer

Email Spam Detection

Machine learning model for detecting spam emails using Naive Bayes

Developed a machine learning model to detect spam emails using the Naive Bayes algorithm. The system classifies incoming emails as spam or legitimate based on content analysis. Used Python with Anaconda environment for development. Key libraries include TensorFlow for model training, Pandas and NumPy for data manipulation, Matplotlib for visualization, and Scikit-learn for preprocessing and model evaluation.

The model was trained on a labeled dataset of spam and ham emails, achieving high accuracy in classification. Features extracted included word frequency, sender information, and subject line patterns. The project demonstrated practical application of NLP techniques and supervised learning for real-world email filtering.

This project strengthened my understanding of machine learning workflows, data preprocessing, and model evaluation metrics in the context of text classification problems.

Travel Recommendation Platform

Microservices-based backend for personalized travel recommendations

Developed a microservices-based backend system using Java, Spring Boot, Hibernate, and MySQL to manage personalized travel recommendations and user data. Implemented RESTful APIs and integrated with front-end components. Secured the application with Spring Security and JWT authentication. Ensured code quality with comprehensive unit testing using JUnit and Mockito. API documentation was created using Swagger for seamless integration.

Technologies used: Spring Boot, Spring Security, JWT, Hibernate/JPA, Maven, Postman, Swagger, MySQL. The system supports user management, destination recommendations, booking workflows, and real-time data processing across multiple microservices.

The project demonstrated strong backend architecture skills with proper separation of concerns, scalable microservices design, and industry-standard security practices.

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