understading the corr heatmap

Looks like your features psfMag_u and fiberMag_u are highly correlated. Remove one of them. Also, psfMag_u and petroMag_u are negatively correlated. So you can get rid of one of them.

Other than that, nothing much can be deduced just from this graph.

This sounds like a feature engineering exercise. Check the links below for relevant info.

Feature Selection Techniques in Machine Learning with Python | Towards Data Science

A Feature Selection Tool for Machine Learning in Python | Towards Data Science

And this last one, directly below, is probably the easiest to implement, and probably the best case for your scenario.

http://blog.yhat.com/tutorials/5-Feature-Engineering.html

From this heatmap, you could infer the correlation between features, if they are positively correlated, negatively correlated, or not correlated at all. The intensity of the colors shows the correlation. Dark red, strongly positively correlated, i.e., if the value in one of the features increases, the value of the other feature increases as well. Dark blue indicates a negative correlation, i.e., if a value of one feature increases, the value of the other feature decreases. White refers to no correlation.

Hope that helps.