import pandas as pd
data = pd.read_csv(filename)

## Separate Training & Validation Dataset
from sklearn.model_selection import train_test_split
X = data.values[:,0:60]
Y = data.values[:,60]
X_train, X_val, Y_train, Y_val = train_test_split(X,Y, test_size = 0.2, random_state=42)



# Build Pipelines – import necessary libraries


#1. Single Pipeline for Prediction
pipe = Pipeline([
                ('LR', LogisticRegression())
                ])

pipe.fit(X_train,Y_train)
pred = pipe.predict(X_val)
print(accuracy_score(Y_val, pred))

#2 Single Pipeline with data scaling

pipe = Pipeline([
                ('Scaler', StandardScaler()),
                ('LR', LogisticRegression())
                ])

pipe.fit(X_train,Y_train)
pred = pipe.predict(X_val)
print(accuracy_score(Y_val, pred))