  Selecting techniques is a crucial element of the business analysis approach
planning in IT projects. Particular attention is paid to the choice of
techniques for requirements elicitation. One of the promising methods for
selecting techniques is using machine learning algorithms trained on the
practitioners' experience considering different projects' contexts. The
effectiveness of ML models is significantly affected by the balance of the
training dataset, which is violated in the case of popular techniques. The
paper aims to analyze the efficiency of the Synthetic Minority Over-sampling
Technique usage in Machine Learning models for elicitation technique selection
in case of the imbalanced training dataset and possible ways for positive
feature importance selection. The computational experiment results confirmed
the effectiveness of using the proposed approaches to improve the accuracy of
machine learning models for selecting requirements elicitation techniques.
Proposed approaches can be used to build Machine Learning models for business
analysis activities planning in IT projects.
