𝑀̲𝑎̲𝑐̲ℎ̲𝑖̲𝑛̲𝑒̲ 𝐿̲𝑒̲𝑎̲𝑟̲𝑛̲𝑖̲𝑛̲𝑔̲:• Developed predictive maintenance models for various industry sectors, employing advanced machine learning techniques to forecast equipment failures and optimize operational efficiency.• Created a machine learning model to predict the Remaining Useful Life (RUL) of manufacturing equipment, enhancing maintenance schedules and reducing unplanned production stops.• Implemented machine learning models to analyze system performance data, enabling preemptive servicing and improving system uptime.• Engineered models to evaluate product quality metrics, allowing for real-time adjustments to improve production outcomes.• Constructed multiple models that significantly reduced downtime and boosted daily production capabilities, achieving substantial savings in operational costs.𝐷̲𝑎̲𝑡̲𝑎̲ 𝐴̲𝑛̲𝑎̲𝑙̲𝑦̲𝑡̲𝑖̲𝑐̲𝑠̲:• Developed and maintained sophisticated data visualizations to track real-time performance metrics of industrial systems, facilitating quick decision-making and improvements.• Leveraged advanced analytics tools to interpret complex datasets, creating dynamic reports that highlighted trends, efficiencies, and areas for potential improvement in production processes.• Designed and executed robust reporting frameworks using Power BI and other visualization software, enabling stakeholders to gain actionable insights into system performance and maintenance needs.