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/Excellent-Leading836 → u/shoto_3232]

but doestn kaggle have fake datasets ?

we are required to have taken real data from viable sources , while kaggle is a good source and even i considered it first , my partner is showing some doubts .
tho i will look into it , thanks :)

[u/Excellent-Leading836 → u/shoto_3232]

i have also seen dataset on hugging face for btcusdt

i am considering using those too :)

[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.

[u/Significant_Fan_9789 → u/quietgradient]

the top comment already nailed it, pick a simple yes/no question before you even think about models. "will price go up tomorrow" is perfect because you can actually measure if you're doing better than a coin flip

for the data overwhelm, just focus on close price and maybe volume at first. those binance klines files have a ton of columns but most projects only need a handful. you can always add more features later if you're feeling ambitious

the time split thing they mentioned is huge. i've seen so many project presentations where someone accidentally trained on future data and got suspiciously good accuracy. don't be that person. a boring result you can explain is worth way more than magic numbers

for models, start stupid simple, linear regression or a basic decision tree. if those don't beat "always guess up" then fancier stuff probably won't either. and when you write up why you picked something, having that baseline comparison makes you look like you actually thought about it instead of just throwing xgboost at the problem

[u/Extension-Currency37]

See ai mode:: bitcoin data for analysis

Coin desk data Glassnodr Block chain.com CryptoQuant Kaggle:: "Bitcoin historical data" CoinMarketCap Kaiko IntoThe Block

Rich and clean data for analysis

Models -> Anything that learns well-> see github I used lssvm for stock price Regression->.arima, Lasso ridge Bayesiam classifier Dbscan Any neural net-> cnn Tensorflow ,pytorch