Department of Environmental Protection and Occupational Safety Technologies, Kyiv National University of Construction and Architecture, Kyiv, 03037, Ukraine.
F1000Res. 2024 Aug 20;13:490. doi: 10.12688/f1000research.149391.2. eCollection 2024.
This research explores the application of quadratic polynomials in Python for advanced data analysis. The study demonstrates how quadratic models can effectively capture nonlinear relationships in complex datasets by leveraging Python libraries such as NumPy, Matplotlib, scikit-learn, and Pandas. The methodology involves fitting quadratic polynomials to the data using least-squares regression and evaluating the model fit using the coefficient of determination (R-squared). The results highlight the strong performance of the quadratic polynomial fit, as evidenced by high R-squared values, indicating the model's ability to explain a substantial proportion of the data variability. Comparisons with linear and cubic models further underscore the quadratic model's balance between simplicity and precision for many practical applications. The study also acknowledges the limitations of quadratic polynomials and proposes future research directions to enhance their accuracy and efficiency for diverse data analysis tasks. This research bridges the gap between theoretical concepts and practical implementation, providing an accessible Python-based tool for leveraging quadratic polynomials in data analysis.
本研究探索了在 Python 中使用二次多项式进行高级数据分析的应用。该研究通过利用 NumPy、Matplotlib、scikit-learn 和 Pandas 等 Python 库，展示了二次模型如何有效地捕捉复杂数据集中的非线性关系。该方法包括使用最小二乘法对数据进行二次多项式拟合，并使用确定系数（R 平方）评估模型拟合度。结果突出了二次多项式拟合的强大性能，高 R 平方值表明该模型能够解释数据变异性的很大一部分。与线性和三次模型的比较进一步强调了二次模型在许多实际应用中在简单性和精度之间的平衡。该研究还承认二次多项式的局限性，并提出了未来的研究方向，以提高它们在各种数据分析任务中的准确性和效率。本研究在理论概念和实际实现之间架起了桥梁，为在数据分析中利用二次多项式提供了一个基于 Python 的易于使用的工具。
