regivm

boston = datasets.load_boston()
y = boston.target
X = pd.DataFrame(boston.data, columns = boston.feature_names)

ss = StandardScaler()
Xs = ss.fit_transform(X)
Xs = pd.DataFrame(Xs, columns=boston.feature_names)
Xs['const'] = 1

# calculate y

regivm

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import datasets
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
from scipy.optimize import minimize

regivm

from datetime import date

sundays=0
for year in range(1901,2001):
	for month in range(1,13):
		if date(year,month,1).weekday()==6:
			sundays+=1
print sundays