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Linear Regression in Python - ML From Scratch 02

Implement the Linear Regression algorithm, using only built-in Python modules and numpy, and learn about the math behind this popular ML algorithm.

In this Machine Learning from Scratch Tutorial, we are going to implement the Linear Regression algorithm, using only built-in Python modules and numpy. We will also learn about the concept and the math behind this popular ML algorithm.

All algorithms from this course can be found on GitHub together with example tests.

Further readings:

Implementation

import numpy as np

class LinearRegression:

    def __init__(self, learning_rate=0.001, n_iters=1000):
        self.lr = learning_rate
        self.n_iters = n_iters
        self.weights = None
        self.bias = None

    def fit(self, X, y):
        n_samples, n_features = X.shape

        # init parameters
        self.weights = np.zeros(n_features)
        self.bias = 0

        # gradient descent
        for _ in range(self.n_iters):
            y_predicted = np.dot(X, self.weights) + self.bias
            # compute gradients
            dw = (1 / n_samples) * np.dot(X.T, (y_predicted - y))
            db = (1 / n_samples) * np.sum(y_predicted - y)

            # update parameters
            self.weights -= self.lr * dw
            self.bias -= self.lr * db


    def predict(self, X):
        y_approximated = np.dot(X, self.weights) + self.bias
        return y_approximated

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