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Linear Regression in Python - ML From Scratch 02

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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