# A Proposal for a New Python Library Implementing Stepwise Procedure  
[PERSON]  
1 Faculty of Economics, Administration, and Accounting, University of Sao Paulo, Sao Paulo 05508-900, Brazil; 1 [EMAIL_ADDRESS] (L.P.F. ); [EMAIL_ADDRESS] (H.P.S. ); [EMAIL_ADDRESS] (W.T.J.) [PERSON]  
1 Faculty of Economics, Administration, and Accounting, University of Sao Paulo, Sao Paulo 05508-900, Brazil; 1 [EMAIL_ADDRESS] (L.P.F. ); [EMAIL_ADDRESS] (H.P.S. ); [EMAIL_ADDRESS] (W.T.J.) [PERSON]  
2 Department of Management Engineering, Federal University of ABC, Sao Bernardo do Campo 09606-045, Brazil; [EMAIL_ADDRESS] 2  
[PERSON]  
3 Polytechnic School, University of Sao Paulo, Sao Paulo 05508-010, Brazil; [EMAIL_ADDRESS] 3  
[PERSON]  
4 Production Engineering Department, Fluminense Federal University, Niteroi 24210-240, Brazil; 4 [EMAIL_ADDRESS] (A.V.T. ); [EMAIL_ADDRESS] (M.A.L.M) 4  
[PERSON]  
4 Production Engineering Department, Fluminense Federal University, Niteroi 24210-240, Brazil; 4 [EMAIL_ADDRESS] (A.V.T. ); [EMAIL_ADDRESS] (M.A.L.M) 4  
[PERSON]  
4 Production Engineering Department, Fluminense Federal University, Niteroi 24210-240, Brazil; 4 [EMAIL_ADDRESS] (A.V.T. ); [EMAIL_ADDRESS] (M.A.L.M) 4  
[PERSON]  
1 Faculty of Economics, Administration, and Accounting, University of Sao Paulo, Sao Paulo 05508-900, Brazil; 1 [EMAIL_ADDRESS] (L.P.F. ); [EMAIL_ADDRESS] (H.P.S. ); [EMAIL_ADDRESS] (W.T.J.) 1 [EMAIL_ADDRESS] (L.P.F. ); [EMAIL_ADDRESS] (H.P.S. ); [EMAIL_ADDRESS] (W.T.J. Also, stepwise provides a systematic approach to building models, which is especially useful when dealing with many variables. It allows analysts to focus on the most relevant variables, simplifying the interpretation of results [7]. Stepwise ensures the validity and importance of the chosen variables and reduces the additional error introduced by redundant variables [8]. Considering that the practical selection of variables plays a crucial role in constructing regression models, the computational implementation of the stepwise procedure offers an automated and efficient approach to selecting variables, allowing the construction of more robust models. In this sense, it is possible to perform the computational implementation of the stepwise regression procedure in several programming languages, each offering specific packages or libraries to assist in this process. The stepwise procedure is widely found in the literature, with multidisciplinary applications, such as storm prediction [9]; modeling of real estate sales prices [10]; agricultural production forecast [11; 12]; environment-related analyses [13; 14; 15; 16; 17]; regression models in healthcare [18; 19; 20]; and analyses related to human behavior [21]. These are just a few of the many applications of the stepwise procedure in machine-learning models of multiple regressions. As for the computational implementation of stepwise, we observe packages and libraries in several programming languages and software, such as R (v. 4.3.1), SAS (v. 9.4M8), MATLAB (v. R2024b), Julia (v. 1.9.3), SPSS (v. 29.0.1.1), Stata (v. 18), Java (v. 21), and C++ (v. 20), among others. However, Python (v. 3.13.0) requires a specific library that implements stepwise selection based on the statistical significance of the variables. As the most popular programming language for data science [22], Python has gained widespread adoption for statistical analysis and machine learning [23]. This highlights the importance of having tools that cater to rigorous statistical methodologies within such a versatile programming environment. It is possible to perform the stepwise selection procedure based on the statistical significance of the variables manually; however, for models with a large number of variables, it is convenient to have a package that automates this process, making it easier to select relevant and statistically significant independent variables for the model. In Python, there is the mlxtend library, which has the SequentialFeatureSelector (SFS) function that performs stepwise selection.