I have a 1000+ samples dataset of 19 variables. My objective is to predict a binary variable based on the other 18 variables (binary and continuous). I'm quite confident that 6 of the predicting variables are associated with the binary response, however, I would like to further analyse the dataset and look for other associations or structures that I might be missing. In order to do this, I decided to use PCA and clustering.
I'm not sure on what's next... I see no significant pattern in the pca and I am wondering what this means and if it could have been caused by the fact that some of the variables are binary. By running a clustering algorithm with 6 clusters I get the following result which is not exactly an improvement although some blobs seem to stand out (the yellow ones).
As you probably can tell, I'm not an expert on PCA, but saw some tutorials and how it can be powerful to get a glimpse of structures in high dimensional space. With the famous MNIST digits (or the IRIS) dataset it works great. My question is: what should I do now to make more sense out of the PCA? Clustering does not seem to pick up anything useful, how can I can I tell that there's no pattern in the PCA or what should I try next to find patterns in the PCA data?