Dealing With Small Datasets In ML

Machine learning models need a lot of data to train and adjust their parameters. In the case of small datasets, because of the lack of data, it becomes harder to get better results. This issue may lead to overfitting.

In this project, we’ll be given a Sequential model with all of the boilerplate code. This model has around 95% training with 75% validation accuracy, which shows that the model is overfitted.

Throughout the project, we’ll apply different techniques to reduce overfitting while retaining high accuracy.