[Python] Random Forest Strategy for Imbalanced Data

Basically this is my code, but my accuracy score is around 50%. There are 565 positives and around 13000 negatives in my dataset. How would I fix the accuracy without undergoing possible bias?

import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestClassifier
from imblearn.over_sampling import SMOTE

#Build SMOTE SRF model
SMOTE_SRF = RandomForestClassifier(n_estimators=150, random_state=0)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, stratify=y)

SMOTE_SRF.fit(X_train,y_train)
#SMOTE SRF prediction result
y_pred = SMOTE_SRF.predict(X_test)

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