Linear Regression

medium· Machine Learningruns: 0

Implement linear regression trained with batch gradient descent. Fit weights and bias to minimize mean squared error, then predict targets for new inputs.

wpm 0acc 100%time 0:000 / 642

import numpy as np

class LinearRegression:
    def __init__(self, lr: float = 0.01, epochs: int = 1000):
        self.lr = lr
        self.epochs = epochs
        self.weights = None
        self.bias = 0.0

    def fit(self, X, y):
        n_samples, n_features = X.shape
        self.weights = np.zeros(n_features)
        for _ in range(self.epochs):
            y_pred = X @ self.weights + self.bias
            error = y_pred - y
            self.weights -= self.lr * (2 / n_samples) * (X.T @ error)
            self.bias -= self.lr * (2 / n_samples) * error.sum()

    def predict(self, X):
        return X @ self.weights + self.bias

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