I have a dataset with several indicators related to some geographical entities ,I want to study factors that influence an indicator A (among the other indicator) .I need to determine which indicators affect it the most (correlation) which ML algo should I use I want to have a kind of scoring function for my indicator A to allow its prediction

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CodePudding user response：

What you are looking for are correlation coefficients, you have multiple choices for that, the most commons are:

• Pearson's coefficient which only measure linear relationship between two variables, see [Scipy's implementation]
• Spearman's coefficient which can show non-linear relationship , see Scipy's implementation

You can also normalize your data using z-normalization and then do a simple Linear regression. The regression coefficient can give you an idea of the influence of each variable on the outcome. However this method is highly sensible to multi-collinearity which might be present, especially if your variables are geographical.

CodePudding user response：

Could you provide an example of the dataset? Discrete or continuous variables? Which software are you using?

Anyway an easy way to test correlation (without going into ML algorithms in the very sense) is to simply perform Pearson's or Spearman's correlation coefficient on selected features or on the whole dataset by creating a matrix of the data. You can do that in Python with NumPy (see this) or in R (see this).

You can also use simple linear regression or logistic/multinomial logistic regression (depending on the nature of your data) to quantify the influence of the other features on your target variables. Just keep in mind that "correlation is not causation. Look here to see some models.

Then it depends on the object of your analysis whether to aggregate all the features of all the geographical points or create covariance matrices for each "subset" of observation related to the geographical points.