# pip install aif360 scikit-learn pandas 
 
import pandas as pd 
from sklearn.linear_model import LogisticRegression 
from aif360.datasets import BinaryLabelDataset 
from aif360.metrics import ClassificationMetric 
 
data = { 
    'Experience': [4, 6, 7, 8, 9, 10, 5, 4, 3, 2, 1, 0], 
    'Gender': [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0], 
    'Hired': [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0] 
} 
df = pd.DataFrame(data) 
 
protected_attribute = 'Gender' 
privileged_groups = [{'Gender': 1}] 
unprivileged_groups = [{'Gender': 0}] 
 
X = df[['Experience', 'Gender']] 
y = df['Hired'] 
model = LogisticRegression() 
model.fit(X, y) 
 
df['predictions'] = model.predict(X) 
 
dataset_original = BinaryLabelDataset( 
    df=df, 
    label_names=['Hired'], 
    protected_attribute_names=[protected_attribute], 
    favorable_label=1,  
    unfavorable_label=0 
)