# 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.

## 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

for _ in range(self.n_iters):
y_predicted = np.dot(X, self.weights) + self.bias
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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