Yogesh Dhumal

Data Science Intern

• Job Details:Predicted machine failure mode by using machine learning algorithms which will help digital maintenance management starting from condition monitoring to remote support to effectively reduce equipment downtime of HCM machines. Gathered and analyzed information, designed and created code data processes, and built a predictive analytical model for digital maintenance. Developed and analyzed statistical and mathematical models and also identified key trends and data characteristics within the datasets that required further investigation; and used analysis for the digital maintenance use case. Developed a classification model for the early prediction of machine breakdown by using logistic regression, random forest and XGBoost model achieving 90% F1 score on test data. Developed a digital twin of operations to understand the process bottlenecks by experimenting with different what-if scenarios in a risk-free environment.

Data Science Intern

• Job Details:Predicted machine failure mode by using machine learning algorithms which will help digital maintenance management starting from condition monitoring to remote support to effectively reduce equipment downtime of HCM machines. Gathered and analyzed information, designed and created code data processes, and built a predictive analytical model for digital maintenance. Developed and analyzed statistical and mathematical models and also identified key trends and data characteristics within the datasets that required further investigation; and used analysis for the digital maintenance use case. Developed a classification model for the early prediction of machine breakdown by using logistic regression, random forest and XGBoost model achieving 90% F1 score on test data. Developed a digital twin of operations to understand the process bottlenecks by experimenting with different what-if scenarios in a risk-free environment.

Data Science Intern

• Job Details:Predicted machine failure mode by using machine learning algorithms which will help digital maintenance management starting from condition monitoring to remote support to effectively reduce equipment downtime of HCM machines. Gathered and analyzed information, designed and created code data processes, and built a predictive analytical model for digital maintenance. Developed and analyzed statistical and mathematical models and also identified key trends and data characteristics within the datasets that required further investigation; and used analysis for the digital maintenance use case. Developed a classification model for the early prediction of machine breakdown by using logistic regression, random forest and XGBoost model achieving 90% F1 score on test data. Developed a digital twin of operations to understand the process bottlenecks by experimenting with different what-if scenarios in a risk-free environment.