This study is to use correlation matrix for feature selection in data mining—more especially inside CNC machines is explored in this thesis. Prediction modeling and classification are the main areas of concentration, with various scenarios utilized to maximize model performance and feature selection. Regarding classification, a thorough investigation of multiple cases demonstrates the effectiveness of a hybrid strategy that employs correlation heatmap-guided feature selection and bases the procedure on power factor values. The Random Forest model stands out as a top performer with an accuracy of 96.8% in the base learner stage and an astounding 97.2% post-tuning, both in original and normalized data settings. Notably, better performance with fewer features supports the adage less is more, highlighted by the reduced feature set decision. The K-Nearest Neighbors model regularly performs better than other models in predictive modeling, showing the lowest error values in various circumstances. The K-Nearest Neighbors model demonstrates its robustness with low prediction errors, regardless of whether it uses all features without a power factor or just non-redundant features without a power factor. This work lays the groundwork for developing feature selection techniques, investigating ensemble learning opportunities, refining prediction models, and customizing strategies to the complexities of CNC machine datasets. These results add to the ongoing discussion in data mining and provide useful information for improving categorization and predictive modeling in the complex field of CNC machines.

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