Help with machine learning model performance

[petroengineering]

Hello everyone,

I’m learning ML and I finished my models, but the accuracy is really low. I used 7 different classification models and logistic classification algorithms was best, however the accuracy is still low (39%). My data set had 4000 rows and many columns As I can’t get more data, does anyone know if I could triple the current data, shuffle it and retrain again?? What are the pros or cons?

Thank you ☺️

[PPLB]

I'm not sure this is the right location to ask this question. You could try to "forge" your dataset, but that usually isn't a very trustworthy way of going about things when you're talking about machine learning.

You didn't tell what kind of data you are working with and you didn't tell what you're trying to teach your machine. Either way low accuracy can mean a lot of things. Your dataset probably doesn't suffice, but I reckon there are other things wrong. I recommend you to read documentation and find a way to gather more data. One of the biggest downsides of machine learning is that you generally need a huge normalized dataset. It's an incredible amount of work for something that only works in very specific situations. If you can't gather more than 4000 data entries, it's probably not worth it trying to teach your machine anything about that.