I am a complete beginner to machine learning so excuse me for the general question.
I'm trying to map column names from random datasets to the columns of a known dataset. For example the column name 'image_link' would need to match the random dataset where the same column name could be 'Image Link' and then another could be 'img_url'.
I have a large dataset of the different variations for each column name
I believe machine learning is something that could help with this and have started looking into this. I have done some machine learning with python, mainly linear regression which i feel doesn't suit this problem.
I have done quite a lot of research via google to see if I can get any examples of something similar but I'm not able to find much. Can anyone help me and advise if this is even something I should be solving using machine learning and if so, is there any particular machine learning techniques that might fit this problem so I know what direction to go in with my research.
Any help would be appreciated.
EDIT**
After a bit more research I kind of feel like a classifier is the way to go maybe using SVM or Naive Bayes?
I also have created a very basic data set but what would be the best way to prepare this kind of data for processing?
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| **Category** | **Term** |
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| id | SKU |
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| id | id |
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| id | productID |
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| link | productLink |
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| link | URL |
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| link | link |
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| image_large | Image |
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| image_large | ImageMedium |
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| image_large | image_link |
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| image_thumb | ImageSmall |
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| image_thumb | Image |
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| image_thumb | image link |
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