ValueError while using linear regression
It seems in your code you build a pandas dataframe but you do not use it. I recreated your code line by line and was unable to get the same error. Try the following

from sklearn.datasets import load_boston
from sklearn.linear_model import LinearRegression
boston = load_boston()
X = boston.data
Y = boston.target

lineReg = LinearRegression()
lineReg.fit(X, Y)
lineReg.score(X, Y)


This results in an error of $0.7406$. Of course, this result is kind of meaningless because you should split your data into a training and testing set in order to accurately test your results. 

You should do the following

from sklearn.datasets import load_boston
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split

boston = load_boston()
X = boston.data
Y = boston.target
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.33, shuffle= True)

lineReg = LinearRegression()
lineReg.fit(X_train, y_train)
lineReg.score(X_test, y_test )


This will always give a different score because the data is shuffled before being split into your two sets. You MUST separate your training data are the start of your algorithm and do not pollute your training set with the testing set otherwise you risk biasing your model! This is very important.