How can I improve a machine learning model?
It is questionable whether doing the PCA and reducing the dimension to 2D was a good idea. You can try experimenting with the number of components in PCA or even trying some non-linear dimensionality reduction like LLE:


  Locally linear embedding (LLE) seeks a lower-dimensional projection of the data which preserves distances within local neighborhoods. It can be thought of as a series of local Principal Component Analyses which are globally compared to find the best non-linear embedding.