code: import numpy as np
  
  class LogisticRegression:
      
      
      def __init__(self, lr = 0.001, n_iters = 1000):
          self.lr = lr
          self.n_iters = n_iters
          self.weights = None
          self.bias = None
      
      def fit(self, X, y):
          #init parameters
          n_samples, n_features = X.shape
          self.weights = np.zeros(n_features)
          self.bias = 0
          
          #gradient descent
          for _ in range(self.n_iters): 
              linear_model = np.dot(X, self.weights) + self.bias
              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)
  
              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 y_predicted_cls
  
      def _sigmoid(self, x):
          return 1 / (1 + np.exp(-x))
apis: numpy.sum numpy.dot numpy.zeros numpy.exp