diff --git a/ML Cookbook/perceptron1.py b/ML Cookbook/perceptron1.py
new file mode 100644
index 0000000..4900276
--- /dev/null
+++ b/ML Cookbook/perceptron1.py	
@@ -0,0 +1,41 @@
+import numpy as np
+
+class Perceptron(object):
+    """Implements a perceptron network"""
+    def __init__(self, input_size, lr=1, epochs=100):
+        self.W = np.zeros(input_size+1)
+        # add one for bias
+        self.epochs = epochs
+        self.lr = lr
+   
+   #activation function
+    def activation_fn(self, x):
+        #return (x >= 0).astype(np.float32)
+        return 1 if x >= 0 else 0
+ 
+ #we need a prediction function to run an input through the perceptron and return an output. 
+    def predict(self, x):
+        z = self.W.T.dot(x)
+        a = self.activation_fn(z)
+        return a
+ 
+    def fit(self, X, d):
+        for _ in range(self.epochs):
+            for i in range(d.shape[0]):
+                x = np.insert(X[i], 0, 1)
+                y = self.predict(x)
+                e = d[i] - y
+                self.W = self.W + self.lr * e * x
+#the easiset set of data that we can provide is the AND gate. Given is set of inputs and outputs.
+if __name__ == '__main__':
+    X = np.array([
+        [0, 0],
+        [0, 1],
+        [1, 0],
+        [1, 1]
+    ])
+    d = np.array([0, 0, 0, 1])
+ 
+    perceptron = Perceptron(input_size=2)
+    perceptron.fit(X, d)
+    print(perceptron.W)