How to improve accuracy of random forest multiclass classification model?
Try doing a feature selection first using PCA or Random forest and then fit a chained classifier where first do a oneversesall and then a random forest or a decision tree. You should get a slightly better accuracy.
I am working on a multi class classification for segmenting customers into 3 different classes based on their purchasing behavior and demographics. I cannot disclose the data set completely but in general it contains around 300 features and  50000 rows. I have tried the following methods but I am unable to achieve accuracy above 50% :

Tuning the hyperparameters ( I am using tuned hyperparameters after doing GridSearchCV)
Normalizing the dataset and then running my models
Tried different classification methods : OneVsRestClassifier, RandomForestClassification, SVM, KNN and LDA
I have also removed irrelevant features and tried running my models
My classes were imbalanced, so I have also tried using class_weight = balanced, oversampling using SMOTE, downsampling and resampling.

Is there something else I can try to improve my accuracy ( and by accuracy I mean f-score, precision and recall ).
Any help will be appreciated.