Predictive modeling for industrial productivity: Evaluating linear regression and decision tree regressor approaches
Abstract
This research discusses the importance of predictive modeling in optimizing efficiency in various sectors, particularly in industrial settings. It compares the effectiveness of linear regression and decision tree regression models in predicting productivity. The study aims to provide insights into the strengths and limitations of each technique, assisting decision-makers in selecting the best model for their needs. It begins by explaining the theoretical foundations of both models and conducts a literature review to highlight their practical implementations. The methodology involves data collection, preprocessing, model training, evaluation, and comparison using real-world datasets. Performance metrics such as Mean Squared Error (MSE) are used for evaluation. The comparative analysis reveals that the linear regression model consistently outperforms the decision tree regressor model in terms of lower MSE values across all datasets. Overall, the study offers empirical evidence and practical insights into the predictive capabilities of both models, with potential implications for strategic decision-making in various industries.
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