value error encountered when using sklearn for linear regression

Question

I am working with linear regression (SKlearn) and when predicting a value I am getting an error. I'm not sure what to do and have tried switching up the format in which I input the prediction value but so far I have drawn a blank.

Here's my code:

import pandas as pd import numpy as np from sklearn.linear_model import LinearRegression data = pd.read_csv("data.csv") print(data.head()) X = data['Machine Age (Months)'].values y = data['Mean Time Between Failure (Days)'].values X.shape # (30,) y.shape # (30,) X = [X] y = [y] model = LinearRegression() model.fit(X,y) prediction = model.predict([[30]])

when running this code i get this error:

matmul: Input operand 1 has a mismatch in its core dimension 0, with gufunc signature (n?,k),(k,m?)->(n?,m?) (size 30 is different from 1)

here's the data I'm importing (i made it a CSV file)

https://drive.google.com/file/d/10fEjJj2znOmRufq3cFuc0CB_t2HAgudI/view?usp=sharing

any help would be appreciated :)

it just returned: ValueError: Found input variables with inconsistent numbers of samples: [1, 30] — Ironkey
– Ironkey, Commented Mar 27, 2020 at 19:13
do you think passing X and y into numpy arrays and then reshaping them with (-1,1) would help? — Ironkey
– Ironkey, Commented Mar 27, 2020 at 19:24

Catalina Chircu · Accepted Answer · 2020-03-27 19:24:49Z

I am not sure about your input for the prediction.

Try this:

import numpy as np X_test = np.array([[30]]) prediction = model.predict(X_test)

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