Curve fitting is a fundamental task in data analysis, allowing researchers to uncover underlying patterns and relationships in their datasets. In this paper, we introduce CurvPy, a powerful data analysis tool designed to streamline the curve-fitting process. CurvPy offers three main functionalities: DataSleuth, FuncPlot, and OptiFit. DataSleuth analyses input data in CSV format and provides a best-guess estimate of the underlying mathematical function. FuncPlot enables users to visually inspect the fit between the function and the data by generating graphs. OptiFit harnesses the power of optimal parameters, allowing effortless optimisation of equation parameters for precise and efficient data modelling. CurvPy is built using Flask, pandas, numpy, matplotlib, scipy, and scikit-learn, providing a user-friendly interface and efficient computational capabilities. By integrating these tools, CurvPy empowers researchers to gain insights from their data and will help to make decisions. Evaluation demonstrates the effectiveness and efficiency of CurvPy in diverse curve-fitting scenarios. The availability of CurvPy as an open-source tool further encourages collaboration and expands its potential applications in various domains. Overall, CurvPy offers a comprehensive solution for curve-fitting tasks and holds great promise for advancing data analysis techniques.

翻译：曲线拟合是数据分析中的基础任务，可使研究者挖掘数据集中的潜在模式和关系。本文介绍CurvPy——一款旨在简化曲线拟合流程的强大数据分析工具。该工具提供三大核心功能：DataSleuth、FuncPlot和OptiFit。DataSleuth可分析CSV格式的输入数据，并提供对潜在数学函数的最佳猜测估计；FuncPlot允许用户通过生成图表直观检查函数与数据的拟合程度；OptiFiT则利用最优参数的力量，实现方程参数的无缝优化，从而完成精确高效的数据建模。CurvPy基于Flask、pandas、numpy、matplotlib、scipy和scikit-learn构建，兼具用户友好界面与高效计算能力。通过整合这些工具，CurvPy使研究者能够从数据中获取洞见并辅助决策。评估结果表明，CurvPy在多种曲线拟合场景中均展现出卓越的有效性和效率。其开源特性进一步鼓励协作，并拓展了其在各领域的潜在应用。总体而言，CurvPy为曲线拟合任务提供了综合性解决方案，对推进数据分析技术具有重要价值。