Performance Of Deep Learning Model For Prediction Of Cryptocurrency Adoption In Nigeria

Abstract: The use of cryptocurrencies is rapidly increasing in developing countries as governments and financial institutions become more aware of them. With millions of people actively transacting in digital assets, Nigeria is a global leader in the adoption of cryptocurrencies. Our knowledge of the socioeconomic elements influencing adoption is still lacking, despite this. Gaps remain in understanding the socio-economic drivers of adoption. This study explores cryptocurrency adoption in Nigeria by using Convolutional Neural Networks (CNN) for predictive analytics. We develop and evaluate a CNN model using adoption-related datasets to classify and predict adoption trends. The CNN model was evaluated on five (5) performance evaluation metrics and achieved 92% accuracy, 90% precision, an 86% recall score, an 86% F1 score and a 95% ROC-AUC. Therefore, results indicate that CNN can effectively capture nonlinear relationships in adoption patterns, outperforming traditional machine learning models in accuracy and generalisation. The study revealed that Convolutional Neural Networks (CNN) can accurately estimate and forecast Nigeria's adoption of cryptocurrency and provides insights for policymakers, financial institutions and technology innovators.

Keywords: Deep learning, Cryptocurrency, Convolutional Neural Network (CNN), Adoption, Nigeria.