Bharath Kumar, S Pavani, T Kamal Tej, Ujwala, G Vamshi 03 09 The Machine Downtime Analysis and Prediction system is designed to analyze industrial machine downtime data and predict possible future downtime events using data analytics and machinelearning techniques In manufacturing industries unexpected machine failures and downtime can reduce production efficiency increase maintenance costs delay production schedules and affect overall productivity Traditional maintenance approaches often depend on fixed schedules or manual monitoring which may not effectively identify early signs of machine failure The proposed system collects historical machine information such as operating hours temperature vibration pressure machine load maintenance history failure type and downtime duration The collected data is preprocessed to handle missing values duplicate records inconsistent information and categorical attributes Exploratory Data Analysis is performed to identify downtime patterns machine failure trends maintenance requirements and factors associated with production interruptions Data visualization techniques are used to display machine utilization downtime frequency downtime duration failure categories and maintenance trends through charts and dashboards Relevant machine and operational features are selected for developing predictive models Machinelearning algorithms such as Logistic Regression Decision Tree Random Forest Support Vector Machine and Gradient Boosting can be used to predict whether a machine is likely to experience downtime Regression techniques can also be applied to estimate the expected duration of downtime when sufficient historical data is available The trained models are evaluated using suitable metrics such as accuracy precision recall F1score ROCAUC MAE RMSE and R depending on the prediction objective The system provides analytical reports and prediction results that can help maintenance teams take preventive actions before serious failures occur Overall the Machine Downtime Analysis and Prediction system combines data analysis visualization and machine learning to reduce unexpected downtime improve machine reliability support preventive maintenance and increase overall industrial productivity Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026