I’m an MSc Data Science, AI & Digital Business and MSc International Business Management student based in Berlin, Germany. My work focuses on applying machine learning, data analytics, and intelligent systems with my experience in Electronics domain knowledge to solve practical engineering and business problems.

I have experience in healthcare R&D, predictive diagnostics, battery health assessment, machine learning model development, and IoT-based systems. Previously, I worked with Philips India Limited in Image Guided Therapy, where I contributed to diagnostic process development, prototype data logging, and predictive battery diagnostics. I also worked as a Data Analyst Intern at Nitya Innovations LLP, where I analyzed EV battery datasets and developed machine learning models for State of Health prediction.

• Machine Learning and Predictive Analytics
• Battery Health Diagnostics and Predictive Maintenance
• Data Science for Healthcare and Engineering Systems
• IoT, Embedded Systems, and Intelligent Devices
• Data Visualization and Business Analytics

Machine learning project focused on estimating Li-ion battery State of Health using diagnostic data, feature engineering, and predictive models such as XGBoost and LightGBM.

Focus: Battery Analytics, Predictive Maintenance, Machine Learning
Tools: Python, Pandas, NumPy, Scikit-learn, XGBoost, LightGBM

Prototype diagnostic workflow for battery health assessment, designed around data logging, parameter analysis, predictive diagnostics, and reliability improvement.

Focus: Diagnostics, Data Logging, Reliability, Predictive Maintenance
Tools: Python, GUI Development, Data Analysis

Cloud-connected IoT system for managing microcontroller-based lab experiments using ESP8266, Arduino, Raspberry Pi, Firebase, and a custom web interface.

Focus: IoT, Embedded Systems, Cloud Connectivity
Tools: ESP8266, Arduino, Raspberry Pi, Firebase, HTML, JavaScript

Wearable device prototype for movement tracking using embedded sensors and microcontroller-based data acquisition.

Focus: Wearables, Embedded Systems, Sensor Data
Tools: Microcontrollers, Sensors, Embedded C / Arduino

• Granted Patent: “A System and Method for Calculation of State of Health of Li-Ion Battery”
• Springer Nature Conference Paper: “EV Lithium-Ion Battery SoH Estimation using LightGBM”
• AIP Conference Paper: “The Fitness Tracker Band”
• Research Paper: “Sun Flora: Dual Axis Solar Tracker using 8051 in Assembly Language”

• Building machine learning projects for battery diagnostics and predictive maintenance
• Developing data analytics and visualization portfolio projects
• Documenting research-based engineering and AI projects

• GitHub: github.com/mayurmore0812
• LinkedIn: linkedin.com/in/mayurmore0812
• Email: [email protected]
• Location: Berlin, Germany