Agfdhyk

I have a problem with SciKit Learn.

I'm doing a really simple linear regression problem. Based on input values of Hours Studied & the resulting grade, I want to be able to estimate a students grade, based on how long they study.

In [1]: import pandas as pd

In [2]: path = 'Desktop/hoursgrades.csv'

In [3]: df = pd.read_csv(path)

In [4]: X = df['Hours Studied']

In [5]:  y = df['Grade']

In [6]: training_data_in = list() 

In [7]: training_data_out = list()

In [8]: training_data_in.append(X)

In [9]: training_data_out.append(y)  

In [11]: from sklearn.linear_model import LinearRegression

In [12]: model = LinearRegression(n_jobs =-1) 

In [13]: model.fit(X = training_data_in, y = training_data_out)

Out[13]: LinearRegression(copy_X=True, fit_intercept=True, n_jobs=-1, normalize=False)

In this example, the DF looks like this:

In [16]: df

Out[16]: 

   Hours Studied  Grade

0              1   10.0

1              2   20.0

2              3   30.0

3              4   40.0

4              5   50.0

5              6   60.0

6              7   70.0

7              8   80.0

8              9   90.0

9             10  100.0

And X looks like this:

In [17]: X

Out[17]: 

0     1

1     2

2     3

3     4

4     5

5     6

6     7

7     8

8     9

9    10

Name: Hours Studied, dtype: int64

And y looks like this:

In [18]: y

Out[18]: 

0     10.0

1     20.0

2     30.0

3     40.0

4     50.0

5     60.0

6     70.0

7     80.0

8     90.0

9    100.0

Name: Grade, dtype: float64

So far so good, it seems to have accepted everything I've put in so far. So now, I want to test the model with some input data. So, I want to say, the number of hours this student studied is 5 & for the model to tell me the expected grade.

But when I put that into the model, I get the below error.

Can anyone advise?

In [14]: studied_hour = [[5]]



In [15]: outcome = model.predict(X = studied_hour)

---------------------------------------------------------------------------

ValueError                                Traceback (most recent call last)

<ipython-input-15-6fdab4ae2efd> in <module>()

----> 1 outcome = model.predict(X = studied_hour)



~/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/base.py in predict(self, X)

    254             Returns predicted values.

    255         """

--> 256         return self._decision_function(X)

    257 

    258     _preprocess_data = staticmethod(_preprocess_data)



~/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/base.py in _decision_function(self, X)

    239         X = check_array(X, accept_sparse=['csr', 'csc', 'coo'])

    240         return safe_sparse_dot(X, self.coef_.T,

--> 241                                dense_output=True) + self.intercept_

    242 

    243     def predict(self, X):



~/anaconda3/lib/python3.7/site-packages/sklearn/utils/extmath.py in safe_sparse_dot(a, b, dense_output)

    138         return ret

    139     else:

--> 140         return np.dot(a, b)

    141 

    142 



ValueError: shapes (1,1) and (10,10) not aligned: 1 (dim 1) != 10 (dim 0)

I should add:

In [39]: X.shape

Out[39]: (10,)



In [40]: y.shape

Out[40]: (10,)

The input shape of both X and y is not correct, it has to be (n_samples, n_features) for X and (n_samples,) for y as per the docs.

You see the error because the model thinks you have ten features and ten different outputs (hence the (10, 10)).

You get the correct results by using

X = df[['Hours Studied']]  # note the double brackets, shape (10, 1)

y = df['Grade']

model = LinearRegression().fit(X, y)



model.predict([[5]])

array([50.])