This is missing the universally true #1 mistake probably (nearly) anyone commits when starting in ML: Missing an excellent understanding of the problem/domain (Unless you happen to be a domain expert for the problem you are working on, but that is a rarity).
If you do not know which features to choose and why, what the lables mean, which background data you should use, and even more important, what the actual problem is that needs to be solved, you will be wasting lots of time - and not just yours...
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This is missing the universally true #1 mistake probably (nearly) anyone commits when starting in ML: Missing an excellent understanding of the problem/domain (Unless you happen to be a domain expert for the problem you are working on, but that is a rarity).
If you do not know which features to choose and why, what the lables mean, which background data you should use, and even more important, what the actual problem is that needs to be solved, you will be wasting lots of time - and not just yours...