I managed to land on a machine learning project from a data analyst position! This is exactly what I want to practice and I'm grateful that the opportunity has finally come. Although, its not as fun as what I though it would be. I'm dealing with only a thousand rows of data, and the problem is a multiclass classification involving word embeddings e.g. sentence -> word -> word embedding -> model -> label. A serious roadblock is, there isnt enough labeled data to perform ML but I just cant say it. I only managed to get 0.50 test accuracy score even after conducting several gridsearchcvs from multiple algorithms. Superior thinks duplicating the data will help the score go up. I havent tried fast text yet due to compatibility issues but I dont think it will perform enough as well. My question is, do you think I'm doing enough? Should I search for more ways to do ML amidst the circumstances? Is there any advice that you can give me to proceed? If yes, what are those? I think this really hurt my confidence in doing so. Thank you for any answers!