Curve Fitting Simplified: Exploring the Intuitive Features of CurvPy

TL;DR

CurvPy is an open-source Python library that simplifies advanced curve fitting and regression analysis, making sophisticated statistical techniques more accessible through an intuitive interface and comprehensive features.

Contribution

This paper introduces CurvPy, a new Python library that combines advanced statistical algorithms with user-friendly design for automated and customizable curve fitting.

Findings

Provides a comprehensive suite of tools for curve fitting and regression analysis.

Balances usability and advanced features through simplified interfaces and options.

Utilizes well-established mathematical techniques like least squares, gradient descent, and regularization.

Abstract

CurvPy is an open-source Python library for automated curve fitting and regression analysis, aiming to make advanced statistical and machine learning techniques more accessible. This paper explores the mathematical foundations and implementation of key CurvPy components for optimization, smoothing, imputation, summarization, visualization, regression, evaluation, and tuning. The methodology leverages well-established statistical and computational algorithms adapted through both simplification and exposure of advanced options to balance usability and customizability. Mathematical techniques utilized include least squares estimation, Savitzky-Golay filtering, matrix completion, gradient descent optimization, regularization, basis function regression, and standard model evaluation metrics.