Evaluating the Predictive Accuracy and Precision of AlexNet and Linear Regression for Women’s Education Post-Independence

In this work, the conventional linear regression algorithm and the deep learning architecture AlexNet are compared and contrasted. With a focus on measuring accuracy and precision in the context of a real-time educational system, the main goal is to assess their effectiveness in anticipating changes in women’s education following periods of independence. The findings indicate that the AlexNet algorithm’s accuracy (84.22%) is much greater than the linear regression algorithm’s accuracy (80.07%). The results show that, with a mean accuracy of 83.33% versus 79.27%, the Novel AlexNet algorithm beats the logistic regression technique in terms of accuracy. The findings of this comparative research will aid in the creation of data-driven solutions for women’s education, giving stakeholders like policymakers, educators, and educators the ability to make decisions that are well-informed in real-time.