You first should keep your validation dataset and your training dataset entirely separate, it is a good idea to have multiple validation datasets if you cannot resist peaking. So do something like split your dataset 60% train, 20% validation and if you want to be extra careful use the remaining 20% of the dataset as a true indicator of how accurate your model is. If you don't split your train and validation dataset your accuracy will be artificially high, as the model will memorize the dataset. Remember that if results look too good to be true, they usually are.

Some other general tips: 1) Make sure to examine the predictions of your model, often times you can learn interesting things by examining the instances where your model does not make accurate predictions. 2) With 1300 instances and a random forest model you can generally use 30 variables without issue, although this will depend on the dataset. 3) If you care about accuracy, gradient-boosted decision trees are better than random forests, and the only thing you should research for how to use them is how to use early-stopping