LogisticRegression.py

import numpy as np

def sigmoid(x):
    return 1/(1 + np.exp(-x))

class LogisticRegression:
    def __init__(self,lr=0.001, epochs=1000):
        self.lr = lr
        self.epochs = epochs
        self.w = None
        self.b = None

    def fit(self,X,y):
        n_samples, n_features = X.shape
        self.w = np.zeros(n_features)
        self.b = 0

        for _ in range(self.epochs):
            linear_pred = np.dot(X, self.w) + self.b
            predictions = sigmoid(linear_pred)

            dw = (1 / n_samples) * np.dot(X.T, (predictions - y))
            db = (1 / n_samples) * np.sum(predictions - y)

            self.w -= self.lr * dw
            self.b -= self.lr * db



    def predict(self,X):

        linear_pred = np.dot(X, self.w) + self.b
        y_pred = sigmoid(linear_pred)
        class_pred = [0 if y <= 0.5 else 1 for y in y_pred]
        return class_pred