In this paper, I build automated machine learning models to recognize and predict handwritten digits. The models are supervised learning models used to predict every possible pair of digits. I evaluate the models by comparing the training and testing accuracies to explore the pairs that are most and least difficult to distinguish. I find that the pair 0 and 1 has the highest accuracy and the pair 3 and 5 has the lowest accuracy.
Andy W. Chen (2018); Digit Recognition using Machine Learning; International Journal of Scientific and Research Publications (IJSRP)
8(6) (ISSN: 2250-3153), DOI: http://dx.doi.org/10.29322/IJSRP.8.6.2018.p7817