Linear Regression in NumPy

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Problem

Write a code for linear regression in bare NumPy.

Implement ordinary least squares using only NumPy, automatically adding an intercept term and supporting multiple features. The implementation should remain defined for rank-deficient or underdetermined design matrices by returning the minimum-norm least-squares solution. Input: X, a numeric 2D array of shape (n_samples, n_features), and y, a numeric 1D array of shape (n_samples,). Output: a 1D array of shape (n_features + 1,), ordered as intercept followed by feature coefficients.

Constraints

1 <= n_samples <= 5000
1 <= n_features <= 100
X and y contain finite numeric values
len(y) == number of rows in X
The output includes an intercept before all feature coefficients

Function Signature

def linear_regression(X, y):

Your solutionPython 3

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