An indicator you mentioned is having a whitepaper, but not having a whitepaper does not mean it is a scam, it just adds to the risk score.
Some important items to consider:
[...snip...]
Hi @jc12345 . Thank you for your constructive inputs. I'll make sure to take note all of the important aspect you've mentioned and I'll discuss this with my groupmates.
And, to be honest, based on your recommendations, identifying scam projects using machine learning appears to be quite difficult due to the large number of factors that must be taken into account, and I agree that having some type of barometer or scale to measure the risk factor is more realistic than simply declaring whether a project is a scam or not. And, just so you know, this is just a research topic for my undergrad degree, and it's one of the few options we're considering.
Thank you very much.
I think there's always a market for everything but it will depend on what's your purpose in creating it. What I mean by this is that is it for profit or is it for the betterment of the whole economic system to help identify scams and automate them?
This is for my undergraduate research paper, but upon seeing all the recommendations given by @jc12345, it seems that it is impossible to create such system due to an extremely vast sets of data especially now that we are still on the introduction of Machine Learning without any prior experience of at least the basics.
![Cry](https://bitcointalk.org/Smileys/default/cry.gif)
Anyway, I tried looking at the article for the machine learning for IcoRating but can't seem to download a copy of their paper. I wanted to learn the introduction and conclusion that they have with it. How they handle false positives and true negatives and what are the percentage of it etc.
I forgot to include the download link of the said research paper but here it is:
https://www.researchgate.net/publication/323722883_IcoRating_A_Deep-Learning_System_for_Scam_ICO_IdentificationThanks for additional references. Very much appreciated.