sklearn: Found arrays with inconsistent numbers of samples when calling LinearRegression.fit()

It looks like sklearn requires the data shape of (row number, column number). If your data shape is (row number, ) like (999, ), it does not work. By using numpy.reshape(), you should change the shape of the array to (999, 1), e.g. using

data=data.reshape((999,1))

In my case, it worked with that.

Looks like you are using pandas dataframe (from the name df2).

You could also do the following:

regr = LinearRegression()
regr.fit(df2.iloc[1:1000, 5].to_frame(), df2.iloc[1:1000, 2].to_frame())

NOTE: I have removed "values" as that converts the pandas Series to numpy.ndarray and numpy.ndarray does not have attribute to_frame().

Seen on the Udacity deep learning foundation course:

df = pd.read_csv('my.csv')
...
regr = LinearRegression()
regr.fit(df[['column x']], df[['column y']])