Hey all,

Firstly I know it’s a basic question but there are a few constraints that make it interesting.

The problem is as follows – I have the following equation –

a1 * b1 * c1…f1 = x/y

here

x/y is a ratio which can range from 0 to +inf.

a1, b1….f1 are features that are all binary.

I want to measure the change in the ratio( x/y ) due to the presence of a1 feature and similarly for other features as well.

I applied LOG transformation of the equation to convert into sums and applied Linear Regression ( sticking to basics ). The learned weights by the model actually denote the change my output value which can be reduced back by taking Exponent of the weights. However, the results were not good.

I needed help with the following –

• Are there any other methods by which I can measure such change, like using weights of Linear Regression, etc?
• Since ratio tends to be from 0 to +inf, applying, is there some other transformation I should prefer?
• All my features are categorical which were one hot encoded, is there some other encoding that might be preferred in such use case?

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It’s a college project that I am needing help with , appreciate any sort help ! 🙂