Paper Details

Name: Umamaheswari.D, Jaisree M K & Madhumitha P
Country: India

Year: 2025
Volume: Volume No: 12, Issue No: 2, Year: 2025 (Specials Issue -2)
Page Number: 73-86
DOI: https://doi.org/10.5281/zenodo.17934841

ABSTRACT
This study presents a novel approach Edupreneurial Leadership Prediction Model that uses a
machine learning based framework which has been specifically designed for the
identification of the leadership potential in the educational entrepreneurship. Most of the
traditional methods use the concept of subjective judgment and prolonged observation. The
proposed model leverages the data-driven techniques which can be used to enhance and
improve the accuracy and the performance efficiency of the model. This model uses
clustering algorithms to group similar candidate profiles and then applies a Random Forest
classifier to predict the leadership qualities of an individual. To uncover the hidden patterns
in the data association rule mining is used which also improves the feature selection and the
predictive analytics of the model. The incorporation of VADER sentiment analysis to access
the written reports leverages deep insights into a candidate’s attitude and communication
styles. This framework is built on Python and scikit-learn which includes modules for
evaluating the educational backgrounds, entrepreneurial experience, personality traits and the
contextual factors. This allows the advantage of customization across various educational
institutions and universities thereby demonstrating a higher accuracy. The future
enhancements include connecting the professional networking platforms with adaptive
learning mechanisms for continuous improvement. This model supports informed decision
making in the educational sector by transforming the leadership identification into a fast datadriven process.
Keywords— Edupreneurial Leadership, Machine Learning, Predictive Analytics, Random
Forest, Clustering Algorithms, Data-Driven Techniques, Association Rule Mining, VADER
Sentiment Analysis, Entrepreneurial Experience, Informed Decision Making, Personality
Trait, Adaptive Learning, Leadership Identification.