# What to do when a classification problem doesn't seem possible?

I'm working on a binary classification problem using 10 predictors. I have 4508 data points. I am required to have a prediction scheme which achieves an accuracy of 80% or better. However I am discouraged by the appearance of my data. It seems like the classification problem is not possible with what I have been given. Here are two group/scatter plots over two pairs of predictors. (It's same over all pairs.)

What do you recommend?

Update: Here is a non-linear embedding of a subset of the data into 2 dimensions. Red colours are class one and blue colours are class zero. (Sorry for the picture size)

And another one using another method:

• It might be well separated in the original feature space $R^{10}$. If you do want to visualize the data in low-dimensional space, it is suggestive to use the first few principal components. Oct 31 '16 at 4:13
• One of the great things about machine learning algorithms is that they can find patterns that we humans have trouble seeing. @Zhanxiong's idea is a good way to look at the data, but you can also try some algorithms and see what you get. I guess the answer to your question is not to get discouraged until you've tried a lot of things. Oct 31 '16 at 4:35