Sklearn NotFittedError: This LinearRegression instance is not fitted yet - python-2.7

Question

I am trying a simple linear regression and I have tried this code:
x1=data.iloc[:, 9].values
y1=data.iloc[:,1].values
from sklearn.model_selection import train_test_split
seed=7
x1_train,x1_test,y1_train,y1_test=
train_test_split(x1,y1,test_size=0.15,random_state=seed)
x1_train=nm.reshape(nm.array(x1_train),(-1,1))
from sklearn.linear_model import LinearRegression
lireg=LinearRegression()
model1=LinearRegression().fit(x1_train,y1_train)
y_pred=lireg.predict(x1_test)
NotFittedError: This LinearRegression instance is not fitted yet. Call 'fit' with appropriate arguments before using this method
Please help

Answer 1

Here is the explanation why you got this error. Let's have a look at the following lines:
lireg=LinearRegression()
model1=LinearRegression().fit(x1_train,y1_train)
y_pred=lireg.predict(x1_test)
What happens here?
You initialise 2 LinearRegression models named: lireg and model1
For the lireg you DO NOT call .fit but for model1 you do.
y_pred=lireg.predict(x1_test) throws the error because you try to .predict using lireg, but lireg is not trained / fitted.
You just need this:
Way 1:
from sklearn.linear_model import LinearRegression
lireg=LinearRegression() # initialize the model
lireg.fit(x1_train,y1_train) # fit he model
y_pred=lireg.predict(x1_test) # now predict
Way 2:
from sklearn.linear_model import LinearRegression
lireg=LinearRegression().fit(x1_train,y1_train) # initialize & fit the model
y_pred=lireg.predict(x1_test) # now predict