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When I try to use .predict on my linear regression, I get thrown the following error:

ValueError: Expected 2D array, got scalar array instead: array=80. 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.

I don't really understand the reshape feature and why its needed. Can somebody please explain to me what this does, and how to apply it to get a prediction for my model?

import matplotlib.pyplot as plt
import numpy as np
from sklearn.linear_model import LinearRegression
x = np.array([95,85,80,70,60])
y = np.array([85,95,70,65,70])
x = x.reshape(-1,1)
y = y.reshape(-1,1)
plt.scatter(x,y)
plt.show()
reg = LinearRegression()
reg.fit(x,y)
reg.predict(80)
input of predict() is 2d array you are passing integer value that's why you are getting error. You need to pass 80 as a 2d list [[80]]
reg.predict([[80]])
        
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