Samir Gaykar

- Developed a Fuel Prediction system (85% accuracy) using ML models. - Built an Engine Fault Detection system (79% accuracy) for early fault identification. - Created a Failure Forecasting system (76% accuracy), reducing failures by 10%. - Designed a Wear Segmentation system, cutting part replacements by 18%. - Optimized exit ticket calculations, reducing processing time from 3 days to 5 seconds.

- Developed a Fuel Prediction system (85% accuracy) using ML models. - Built an Engine Fault Detection system (79% accuracy) for early maintenance. - Created a Failure Forecasting system (76% accuracy), reducing failures by 10%. - Applied Clustering for wear segmentation, optimizing maintenance. - Improved model accuracy using multiple evaluation metrics. - Reduced data redundancy (20%) & code complexity (12%). - Cut exit ticket processing from 3 days to 5 seconds with SQL & Power BI.

Project: Image Classification and Grading • Developed Neural Network models using Deep Leaning algorithms • Classified and graded wood based on its quality as shown in the respective image

Project: Application areas for Machine Learning in Vessel Performance Management • Created models using Machine Learning algorithms • Performed exploratory data analysis on the vessel data

Project: Baseline Generator • Created models using Machine Learning algorithms • Developed web application with interactive graphs using Python

Project: Customer Classification • Created Deep Neural Network to classify the customers

Project: NAMOS • Created and executed test cases

Project: Dashboards and Reports • Created technical design to develop charts, dashboards and reports • Developed charts for the better data visualization using Tableau • Developed interactive dashboards using Power BI • Created SSRS reports & changes in the existing SSRS reports