Logistic Empirical Study of Regression Model in Python Class Model Examination
College of Digital Technology Application Industry, Shangrao Normal University, Shangrao 334001, China
| Abstract: | In the information age, data holds increasingly significant value, and computer-based data analysis and visualization have become widespread practices. This paper explores the application of Logistic regression models in analyzing factors influencing student performance. Logistic regression is a supervised learning algorithm that predicts a binary dependent variable based on a set of independent variables, transforming the predicted value of linear regression into a probability through a sigmoid function. In the context of education, student achievement is often categorized, making Logistic regression well-suited for analyzing its influencing factors. By collecting multifaceted data from students, the Logistic regression model can establish a predictive model with multiple independent variables, such as personal characteristics, family factors, and school environment. This study focuses on the analysis of Python course model examination results, preprocessing the data to ensure integrity and addressing abnormal values. Visualization analysis is employed to intuitively reveal the potential effects of various factors, including gender, average daily learning time, undergraduate school level, and registered school level, on model exam scores. The results demonstrate significant correlations between these factors and exam scores, with Logistic regression analysis further explaining about 72% of data performance variation. This study provides insights into using data visualization and Logistic regression for educational research and decision-making. |
| Keywords: | Visualization; Python; Logistic Regression Model; Matplotlib |
| DOI: | 10.57237/j.jeit.2025.01.001 |
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收稿日期: 2024-12-17
接受日期: 2025-01-17
在线出版日期: 2025-02-08
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