Nitesh Srivastav

Implemented real-time predictive maintenance for vehicle components using machine learning techniques. Integrated sensor data streams from engines, brakes, and suspension systems in real-time. Utilized machine learning algorithms (Random Forest, Support Vector Machines) for building predictive models, continuously trained and updated with real-time data. Leveraged real-time performance metrics, such as precision, recall, and F1-score, to assess and optimize the models' accuracy. Contributed to 25% cost savings by reducing unplanned maintenance and improving overall operational efficiency through real-time predictive maintenance.

Framing the Business Scenario: Identify the problem or opportunity that needs to be addressed. Choosing the Right Visuals: Identify the key messages that need to be conveyed through the visuals. Creating Effective Visuals: Ensure that the visual is easy to read and understand. Communicating Insights and Analysis: Provide context and explain the significance of the insights and analysis.