Onafowokan-Testament/Enhanced-Digit-Classification-using-Data-Augmentation

digit prediction with more than 97 percent accuracy on test data.

Developed a robust digit classification system using the MNIST dataset with an emphasis on data augmentation and model diversity.

Implemented data augmentation techniques by shifting images in four directions to expand the dataset.

Achieved an average accuracy of approximately 97.3% for both Random Forest and KNN.