Using deep learning or random forest I am very new to machine learning, and I am trying to build a model to classify this data set (UCI heart disease). I have built a simple model using random forest and got an 80% accuracy. The size of the set is about 900. To get the best value possible, should I continue with random forest or go with deep learning (such as TensorFlow)?

Thanks!
The given dataset has 303 observations and 76 features. This is usually considered very small. Deep learning needs plenty of data to be stable since it is highly nonlinear. Random forest is one option but simpler structured models can be considered. One important thing which have to be investigated in those situations is regularization. Fighting with over fitting looks like the main goal.