diff --git a/Lamina_Zafrullah_Segun b/Lamina_Zafrullah_Segun
new file mode 100644
index 0000000..cfec91e
--- /dev/null
+++ b/Lamina_Zafrullah_Segun
@@ -0,0 +1,68 @@
+import numpy as np
+import matplotlib.pyplot as plt
+%matplotlib inline
+import seaborn as sns
+from sklearn import datasets
+
+BC = datasets.load_breast_cancer()
+X, y = BC.data, BC.target
+
+class LogisticRegression:
+    def __init__(self, lr=0.0001, num_iter=300000, fit_intercept=True, verbose=False):
+        self.lr = lr
+        self.num_iter = num_iter
+        self.fit_intercept = fit_intercept
+        self.verbose = verbose
+    
+    def __add_intercept(self, X):
+        intercept = np.ones((X.shape[0], 1))
+        return np.concatenate((intercept, X), axis=1)
+    
+    def __sigmoid(self, z):
+        return 1 / (1 + np.exp(-z))
+    def __loss(self, h, y):
+        return (-y * np.log(h) - (1 - y) * np.log(1 - h)).mean()
+    
+    def fit(self, X, y):
+        if self.fit_intercept:
+            X = self.__add_intercept(X)
+        
+        # weights initialization
+        self.theta = np.zeros(X.shape[1])
+        
+        for i in range(self.num_iter):
+            z = np.dot(X, self.theta)
+            h = self.__sigmoid(z)
+            gradient = np.dot(X.T, (y - h))
+            self.theta += self.lr * gradient
+            
+            z = np.dot(X, self.theta)
+            h = self.__sigmoid(z)
+            loss = self.__loss(h, y)
+                
+            if(self.verbose ==True and i % 10000 == 0):
+                print(f'loss: {loss} \t')
+    
+    def predict_prob(self, X):
+        if self.fit_intercept:
+            X = self.__add_intercept(X)
+    
+        return self.__sigmoid(np.dot(X, self.theta))
+    
+    def predict(self, X):
+        return self.predict_prob(X).round()
+        
+        BC = datasets.load_breast_cancer()
+        X, y = BC.data, BC.target
+
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size= 0.3, random_state=1234)
+
+def accuracy(y_true, y_pred):
+    accuracy = np.sum(y_true == y_pred) / len(y_true)
+    return accuracy
+    
+regressor = LogisticRegression(lr=0.00001, num_iter=3000000)
+regressor.fit(X_train, y_train)
+predictions = regressor.predict(X_test)
+
+print ('Logistic Regression Accuracy:', accuracy(y_test, predictions))