Curve Fitting Simplified: Exploring the Intuitive Features of CurvPy
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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.
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