Optimizing Linear Regression model in Water Hardness Prediction for Industry 4.0

Industry 4.0 requires water hardness forecast optimization for efficiency and problem-free operations. Common prediction method linear regression can be improved by feature selection approaches like Recursive Feature Elimination. This abstract optimizes linear regression for Industry 4.0 water hardness prediction utilizing RFE for feature selection. A dataset of water hardness parameters and industrial measurements is used. First, the dataset’s structure and attributes are assessed. Temperature, mineral content, pH, and other industrial process factors are found. RFE then iteratively removes unnecessary features and picks the linear regression model’s most important ones. The initial model is fitted with all attributes and prioritized by importance. The model is refitted after deleting the least important feature. This procedure is repeated until a stopping criteria, like a certain number of features or performance metrics, is met. Optimized linear regression model evaluation uses R2, adjusted R2, and mean squared error. In Industry 4.0, model coefficients show parameter-water hardness connections. RFE and linear regression improve industrial water hardness prediction in this study. The selected features expose water hardness variables to assist the industry monitor and optimize operations, enhance efficiency, and reduce hazards. This study optimizes linear regression models for water hardness prediction using Industry 4.0 feature selection methods, particularly RFE. Results boost operational efficiency, data-driven decision-making, and industrial innovation.