How to fit data in sklearn

Question

I want to write code that read a csv file and then use linear regression for prediction.
The CSV file is like this:

math	physics
17	15
16	12
18	19

I try this code:

import pandas as pd
from sklearn.linear_model import LinearRegression

score_file = pd.read_csv('scores.csv')
math_score = score_file['math']
physic_score = score_file['physics']
cls = LinearRegression().fit(math_score,physic_score)

but it gives me this error:

Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample

Answer 1

import pandas as pd
from sklearn.linear_model import LinearRegression

score_file = pd.read_csv('scores.csv')
# score_file = pd.DataFrame.from_dict(
#     {'math': [17, 16, 18], 
#      'physics': [15, 12, 19]})

physic_score = score_file['physics']

print(score_file.shape)  # (3, 2)
print(physic_score.shape) # (3,)

# take care of the dimentions
cls = LinearRegression().fit(score_file,physic_score)

# this should be made with a test subdataset or so...
predictions = cls.predict(score_file)

print(predictions) # [15. 12. 19.]