A Machine learning tool to predict early-stage start-up success in Africa
| dc.contributor.author | Gichohi, B. W. | |
| dc.date.accessioned | 2024-03-11T10:29:22Z | |
| dc.date.available | 2024-03-11T10:29:22Z | |
| dc.date.issued | 2023 | |
| dc.description | Full - text thesis | |
| dc.description.abstract | Most start-ups do not celebrate their first year in operation, and a few survive to see their fifth year of operation. This has been a challenge for all the stakeholders involved. Therefore, an effective tool for predicting the possibility of a start-up surviving its infancy stages and eventually growing into a profitable venture could be a breakthrough for entrepreneurs, innovators, and investors. This study assessed the factors that make early-stage start-ups successful, specifically in Africa and developed a web-based prototype that uses machine learning algorithms to predict the success of proposed start-ups. The study adopted both descriptive research design and applied research. Data was collected using a secondary data source called CrunchBase, a global investor platform. This data formed the basis for the development of the prediction tool. The tool was designed to predict the success or failure of start-ups based on the collected data. To ensure the accuracy and reliability of the prediction model, 80% of the collected data was used for training the model, while the remaining 20% was utilized for testing and validation purposes. The model development employed Artificial Neural Networks (ANNs) algorithm, known for its capability to analyze complex patterns and relationships in data. The developed model achieved an impressive accuracy of 86.81%, indicating its effectiveness in predicting the success of start-ups. The tool was implemented using Flask, a Python web framework, along with other Python machine learning frameworks such as Keras and Sci-kit Learn. This allowed for the development of a user-friendly and interactive web-based prototype. A number of users were provided access to the tool for usability testing, and their feedback indicated that the tool was intuitive, easy to use, and effective in predicting the success of start-ups. This study successfully developed a web-based prototype using agile methodology, integrating machine learning algorithms based on Artificial Neural Networks. The prototype demonstrated high accuracy in predicting start-up success, making it a valuable tool for entrepreneurs, innovators, and investors in Africa and beyond. Keywords: Business start-ups, machine learning algorithms, prediction tool, start-up success. | |
| dc.identifier.citation | Gichohi, B. W. (2023). A Machine learning tool to predict early-stage start-up success in Africa [Strathmore University]. http://hdl.handle.net/11071/15384 | |
| dc.identifier.uri | http://hdl.handle.net/11071/15384 | |
| dc.language.iso | en | |
| dc.publisher | Strathmore University | |
| dc.title | A Machine learning tool to predict early-stage start-up success in Africa | |
| dc.type | Thesis |
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