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I am relative newbie to data science so please excuse me if its a trivial question.
I have 6 features and want to predict the 'y'. These features are related to y in the training data-set as follows; (x-axis being the feature data points and y axis being the value to be learnt to predict) Feature1 vs y
Feature2 vs y
Feature3 vs y Feature4 vs y
Feature5 vs y
Feature6 vs y Just on observing this it seems that my y can be predicted by using Linear Regression using only Feature 1 & 6. Even if I wanted to use the other features;
a) Can I ?
b) Feature 3,5 & 2,4 can be combined ?

The correlation matrix looks like this;

varCorr = featureMatrix.corr()
sns.heatmap(varCorr, xticklabels=varCorr.columns, yticklabels=varCorr.columns, annot=True)

Correlation matrix

How can include other features for regression or is my dataset such that I have limited options (linear regression using 2 features) and should I look for more/better features ?

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You have some pretty obvious colinearity. This has been discussed here a lot (see the colinearity tag) and if you then have additional questions, ask a new question.

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