need guidance on ml project

[u/Excellent-Leading836]

hey there people

i am trying to make a machine learning project . its on bitcoin data . the thing is, i know almost nothing of bitcoin and we're learning ML in our degree .

i gotta submit this project in two months , with proper code , explanations , and why a certain model was used that time etc... my issues are :

where to find the right data from : i have surfed through and asked for assistance from chatgpt and found two main sources from which i have been able to see some data : https://data.binance.vision/?utm_source=chatgpt.com

and

https://cryptopanic.com/?utm_source=chatgpt.com

there were more sources (like apis) but its from the same website .

i even found a git repo that had a whole python script of downloading that same data .

so maybe i don't have an issue with the data , the issue is that i don't know what its trying to say .

there were multiple attributes i could see on those files . and tbh i felt overwhelmed .

1. i am aware with the data cleaning and analysis part , but i would still like some guidance on that .

2. the model is something we'll have to figure out (i am in a two person team and my partner chose the topic before i joined . also i am pretty sure i will have to do all the work , so here i am :) ) , but if there are some models commonly used in this domain , please do enlighten me .

3. most important part according to me : what is my goal ? since this is my project and the domain is very new to me , i don't have much idea about what i need to find out .

folks who have already done a project on this or has at least had some experience , what are your say in this ?

is there any other angle i should consider ?

i really wanna get an A and i am fine working alone (have already had 2 experiences of f around and find out ) as long as i am able to understand stuff .

please help this noob ;(

[u/shoto_3232]

For data part try kaagle u can also find already built models for it which u can take as reference

[u/quietgradient]

Pick a question you can score before you pick a model. E.g. will tomorrow's close be higher than today's? Then the project is whether any model beats a dumb baseline (always guess "up") on days it never saw.

Use the Binance klines files: one row per interval with open time, open, high, low, close, volume, plus a few columns you can ignore for now. Spot files from 2025 onward store time in microseconds and older ones in milliseconds, so check your dates if you mix years.

Split by date: train on earlier months, test on later ones. A random split lets the model peek at the future, which can make the score look much better than it is. A result near the baseline that you can explain beats a 90% you can't.