Dealing with biased binary classifier
What you have in your data called imbalanced classes

From Datacamp


  Imbalanced data typically refers to classification tasks where the
  classes are not represented equally.
  
  For example, you may have a binary classification problem with 100
  instances out of which 80 instances are labeled with Class-1, and the
  remaining 20 instances are marked with Class-2.


In this link, you can find a nice article that explains more what it is and how you can handle it -> https://www.datacamp.com/community/tutorials/diving-deep-imbalanced-data

One of the solutions is to use over or under-sampling. You can achieve this with the SMOTE algorithm. Here is an example in Python.

from imblearn.over_sampling import SMOTE
from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)

sm = SMOTE(random_state=2)
X_train_res, y_train_res = sm.fit_sample(X_train, y_train.ravel())