Low prediction/classification accuracy due to imbalance in data feeding
When the data is highly imbalanced, accuracy could be a bad measure of model performance. Instead consider precision and recall values, which separately takes into account the no of positive and negative samples. One idea to improve the performance is to use class weights (generally inversely proportional to the no of samples in that class) while calculating loss metric. This makes the model understand that class with less samples should be given more priority while training.