text: I am working on a data set that has 10 features, and my label ed output is the 'Weight of humans'.
  I want to find, out of 10 features, which are he 2 or 3 features due to which the 'Weight' varies.. What are these 2-3 features on which the weight varies...
  Please let me know, how i can find it out, using any ML technique or algorithm..
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  Hi, I'm a new student in data science, I'm doing the process the following way.
  from sklearn.feature_selection import RFE
  from sklearn.linear_model import LogisticRegression
  modelo = LogisticRegression()
  rfe = RFE(modelo, 8)
  fit = rfe.fit(X,Y)
  print(X.columns)
  print(fit.support_)
  print(fit.ranking_)
  melhores_colunas = X.columns[fit.support_]
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  Depends on what algorithm you are using. Assuming this is a classification problem and you are using RandomForestClassifier from sklearn, you can simply use it feature_importances_ method to look at the a sorted list of the features and determine which are more important.
  Sklearn RandomForestClassifier:....
  Although it talks about RandomForestRegressor but it may help you understand this...
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  Thanks the above links are helpful, but here my case is a regression one, but here i don't want to predict anything, i want to know features that are impacting my label ed output.
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  Have you tried looking at a correlation in the variables? Maybe you could use a scatter plot matrix or a correlation matrix to visualize your data and see what variables are impacting your label output.
  Check out this article on visualizing your data, especially the Multivariate Plots section:-...
  Hope that helps!!
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