Python Sklearn Linear Regression Value Error

Question

Ive been trying out Linear Regression using sklearn. Sometime I get a value error, sometimes it works fine. Im not sure which approach to use. Error Message is as follows:

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/linear_model/base.py", line 512, in fit
    y_numeric=True, multi_output=True)
  File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/utils/validation.py", line 531, in check_X_y
    check_consistent_length(X, y)
  File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/utils/validation.py", line 181, in check_consistent_length
    " samples: %r" % [int(l) for l in lengths])
ValueError: Found input variables with inconsistent numbers of samples: [1, 200]

The code is something like this:

import pandas as pd
from sklearn.linear_model import LinearRegression
data = pd.read_csv('http://www-bcf.usc.edu/~gareth/ISL/Advertising.csv', index_col=0);
x = data['TV']
y = data['Sales']
lm = LinearRegression()
lm.fit(x,y)

Please help me out. I am a student, trying to pick up on Machine Learning basics.

Answer 1

lm.fit expects X to be a

numpy array or sparse matrix of shape [n_samples,n_features]

Your x has shape:

In [6]: x.shape
Out[6]: (200,)

Just use:

lm.fit(x.reshape(-1,1) ,y)