Impact of funding and geographical factors on software startups' success
Authors
Ondiba, Florence
Issue Date
2024-05
Degree
MSc in Business Analytics
Publisher
Dublin Business School
Rights holder
Rights
Abstract
The purpose of this study was to investigate and adress two important questions regarding the success of software startups. To begin, it investigated the level of impact that different types of funding structures and geographical factors have on the success of the software startup companies. It explored various machine learning models to predict the outcomes of startup ventures, taking into account important features, model performance and cost-effectiveness. As demonstrated in this report, the research provided answers to these questions.
The study identified the primary factors that contribute to over 64 per cent of the success or failure of a software startup company. Location-region accounts for 18%, Time initial funding has received accounts for 16%, timing of Final Funding accounts for 15%, Access to Venture Capital accounts for 11%, and Location - Country accounts for 5%.
Logistic regression emerged as the most suitable model for deployment, achieving an accuracy of 96.58%, precision of 96.51%, recall of 100%, and an F1 score of 98.22%. This is by utilising the CRISP-DM methodology, Python code, and Power BI for data scrutiny and analysis. In addition, this model provides significant cost savings, which amount to 73.81 million dollars. The study does, however, acknowledge that there are challenges associated with limitations in the dataset scope and timeframe. These findings highlight the significance of conducting a comprehensive analysis of a startup, which should include aspects such as financial evaluation, geographical analysis, and predictive modelling.