#!/usr/bin/python3
 import pandas as pd

 from sklearn.model_selection \
   import train_test_split
 from sklearn.preprocessing \
   import StandardScaler
 from sklearn.neural_network \
   import MLPClassifier

 train_df = \
     pd.read_csv("miles-per-day-wday.csv")
 X = train_df.drop('weekday', axis=1)
 y = train_df['weekday']

 X_train, X_test, y_train, y_test = \
     train_test_split(X, y)

 scaler = StandardScaler()
 scaler.fit(X_train)
 X_train_n = scaler.transform(X_train)
 X_test_n = scaler.transform(X_test)

 mlp = MLPClassifier(
     hidden_layer_sizes=(2,2),max_iter=1000)
 mlp.fit(X_train_n,y_train)

 print(mlp.predict(X_test_n))