Abstract: Over the past few decades, there has been rapid growth in the formation of new start-ups around the world. Thus, it is an important and challenging task to understand what makes start-ups successful and to predict their success. Several reasons are responsible for the success and failure of a start-up, including bad management, lack of funds, etc. This work aims to create a predictive model for start-ups based on many key factors involved in the early stages of a start-up’s life. Current research on predicting success mainly focuses on financial data such as ROI, revenue, etc. Therefore, in this paper, a different approach is proposed by first investigating other non-financial factors affecting start-up success and failure. Second, the adoption of an algorithm that has not been used much in predicting start-up success, which is Convolutional Neural Network (CNN). The dataset was acquired from Kaggle. The final model was reached through a series of four experiments to determine which model predicts better. The final model was implemented using a CNN with an average accuracy of 82%, an average loss of 0.4, an average 0.9 recall and an average 0.9 precision.

Lobna Hsairi, “Deep Learning to Predict Start-Up Business Success” International Journal of Advanced Computer Science and Applications(IJACSA), 15(3), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0150336

@article{Hsairi2024,
title = {Deep Learning to Predict Start-Up Business Success},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150336},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150336},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {3},
author = {Lobna Hsairi}
}