This is a good thought, i haven't thought this way. Will try this out and see if it's possible to represent it this way. Thanks.

Okay, thanks. I'll keep that in mind and I'll look into it.

This is an interesting thought. They do have some inherent relationship but defining it would be a problem. But I'll look into it, thanks for your input.

Thanks for your input, ya i will try that out. The features do have a relation, that's why I was wondering whether packaging that in the input will make sense or not. I'll do some experiments and see how it turns out.

Ya true, but it's a bit more than that. I've added an explanation to another comment, adding that below.

My doubt is this: for a row, how to put the velocity and time together for a single feature. For eg: row 1 column 1 would be - 1st out of 8 side -> 1st out of 5 values -> velocity, distance. Here how do I represent (velocity, distance)? Do I keep them as a tuple or as separate adjacent features or any other format? This is exactly where I'm stuck.

My confusion/question is on what could be the right way to put these values together. Do I directly stack them as 80 features like you mentioned or do I adopt another format such that the 2 values (velocity, time) are bundled together like a tuple or a dict or something else? Thanks.

Ya this is almost what I did. My doubt is this: for a row, how to put the velocity and time together for a single feature. For eg: row 1 column 1 would be - 1st out of 8 side -> 1st out of 5 values -> velocity, distance. Here how do I represent (velocity, distance)? Do I keep them as a tuple or as separate adjacent features or any other format? This is exactly where I'm stuck.

I understand, I'll update the main post with more info.

To answer your question, a label corresponds to all the readings of a subject. Here, all the readings are a combination of the 8 subsequent readings done from different sides. Which again have 5 values for each side. Easiest/closest example i could think of is like taking multiple snapshots of an object from different sides to create a 3d model. Similar to that. My question is regarding how to efficiently wrap this data up for each subject.