• Ziya Fatima Sayed
• Dr. Sonali Kadam
• Sakshi Priya
• Janhavi Pardeshi
• Satvika Nemanwar

The time interval when the system doesn’t work is downtime. Downtime duration is the amount of time when a system fails or develops a fault to perform its primary function. The main aim of factory downtime prediction is to identify the potential areas to quickly increase efficiency without operational changes. By doing the literature survey we got to know about the issues, tools available and algorithms like decision trees, Random Forest, Gradient Boosting, Ada Boosting, XGBoost, and SVM are important approaches for building a predictive model that can predict the downtime duration of the factory which can help engineers to quickly identify factories that would cause the most significant downtime. This paper talks about the prerequisite for utilizing quicker, less complex, and more powerful working techniques in predicting downtime duration so that the company can better handle downtimes and increase operational efficiency to better handle demands.

downtime, predictive analytics, machine learning, data wrangling, and visualization.

"Factory Downtime Prediction Using Machine Learning Algorithms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 5, page no.i424-i430, May-2023, Available :http://www.jetir.org/papers/JETIR2305881.pdf

"Factory Downtime Prediction Using Machine Learning Algorithms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 5, page no. ppi424-i430, May-2023, Available at : http://www.jetir.org/papers/JETIR2305881.pdf