# Logistic Regression in Python - ML From Scratch 03

Implement the Logistic 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 Logistic 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 LogisticRegression:

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):
# approximate y with linear combination of weights and x, plus bias
linear_model = np.dot(X, self.weights) + self.bias
# apply sigmoid function
y_predicted = self._sigmoid(linear_model)

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):
linear_model = np.dot(X, self.weights) + self.bias
y_predicted = self._sigmoid(linear_model)
y_predicted_cls = [1 if i > 0.5 else 0 for i in y_predicted]
return np.array(y_predicted_cls)

def _sigmoid(self, x):
return 1 / (1 + np.exp(-x))
``````

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