PROP-VALUE : Web Platform That Estimates The Value Of Properties Using Customer-entered Metrics And Microservice Architecture.

As a Software and Machine Learning Engineer on this dynamic project, I was entrusted with pivotal responsibilities that encompassed both software development and machine learning implementation. My contributions played a critical role in shaping the success of our innovative property valuation platform.

Implemented microservice architecture to modularize functionality and ensure scalability.

Developed a web platform using Java and Spring Boot framework to estimate the value of properties online.

Utilized Eureka for service registry, allowing seamless discovery and registration of microservices.

Introduced Kafka for streaming and messaging, enabling real-time data processing and communication between different components of the system.

Used Ribbon for client-side load balancing, ensuring efficient distribution of requests across multiple instances of microservices.

Collaboratedwith the frontend team to define APIs and ensure smooth integration between frontend and backend systems.

Conducted thorough testing and debugging to ensure the accuracy and reliability of the property valuation algorithm.

Workedclosely with the product team to gather requirements, provide technical insights, and ensure alignment with the project goals.

Followed Scrum methodology and employed Atlassian Jira, Confluence, and Bitbucket for project management, sprint planning, issue tracking, and code management.

Led development of a cutting-edge property valuation platform, yielding 15% higher accuracy compared to traditional methods.

Implemented microservice architecture to modularize functionality and ensure scalability.

Developed a web platform using Java and Spring Boot framework to estimate the value of properties online.

Collaborated with data scientists to implement advanced ML algorithms, reducing prediction errors by 20% using regression and gradient boosting techniques.

Conducted rigorous testing and validation, achieving a 95% confidence level in valuation predictions.

Technologies Used: Java, Spring Boot, MongoDB, Eureka, Kafka, Ribbon, Microservices, MySQL, Jira, Confluence, Bitbucket, Scrum