# Logistic Regression in Python - ML From Scratch 03

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.

### Further readings:

- https://ml-cheatsheet.readthedocs.io/en/latest/logistic_regression.html
- https://towardsdatascience.com/logistic-regression-detailed-overview-46c4da4303bc

## 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
# gradient descent
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)
# 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):
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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# ML From Scratch

Implement popular Machine Learning algorithms from scratch using only built-in Python modules and numpy.