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@article{184335,
        author = {Mr.Atul Rajesh Waman and Dr.Sangeeta Mahesh Borde},
        title = {Improving Predictive Accuracy through Correlation-Based Data Frame Splitting for Polynomial and Linear Models.},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {4},
        pages = {1208-1213},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=184335},
        abstract = {This paper presents a method to improve the accuracy of polynomial and linear models by splitting a dataset into smaller, specialized sub-data frames based on the correlation between the target variable and its highest correlated feature. The process involves plotting this relationship, sorting the data by the highest correlated feature, and using the mode or visual observation to identify split points. The resulting sub-data frames are used to create simpler models that better fit their respective regions. During prediction, the appropriate sub-model is selected based on the value of the highest correlated feature, leading to more accurate and context-specific predictions.},
        keywords = {Regional modeling, correlation-based splitting, sub-data frames, predictive accuracy, and mode-based split.},
        month = {September},
        }