Creating Decision Boundary for simple Classification

import numpy as np
import random
import pandas as pd
import matplotlib.pyplot as plt
#% matplotlib inline

Input_Matrix = np.matrix([[1, 4], [1, 5], [2, 4], [2, 5], [3, 1], [3, 2], [4, 1], [4, 2]])
Target = np.matrix([[1], [1], [1], [1], [0], [0], [0], [0]])


# Answers to (iii) bit
# Input and Targets can be changed like that for more robustness :
# x0 = np.random.normal(3.5, 0.7, 10)
# y0 = np.random.normal(1.5,0.7,10)
# x1 = np.random.normal(1.5, 0.7,10)
# y1 = np.random.normal(4.5,0.7,10)
# p0 = np.column_stack((x0,y0))
# p1 = np.column_stack((x1,y1))
# Input_Matrix = np.concatenate((p0,p1))
# print(Input_Matrix)
# Target = np.matrix([1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0])

Weights = np.random.rand(1, 2)
Bias = np.random.rand(1)


# Weights=np.random
# Bias = [5]

def transfer(n):
    a = []
    if n > 0:
        a.append(1)
    else:
        a.append(0)
    return a


def Weight_UPDATED(Weights, Bias, error, i, itercount):
    Weights = np.add(Weights, np.dot(error, Input_Matrix[i]))
    Bias = np.add(Bias, error)
    itercount += 1
    return Weights, Bias, itercount


status = "Learning"
itercount = 0
while (status == "Learning"):
    counter = 0

    for i in range(0, 8):
        n1 = transfer(np.add(np.dot(Weights, Input_Matrix[i].transpose()), Bias))
        error = Target[i] - n1
        if error != 0:
            print(Weights, Bias)
            Weights, Bias, itercount = Weight_UPDATED(Weights, Bias, error, i, itercount)
            counter += 1
    if counter == 0:
        status = "success"

print("Final weights are ", Weights, Bias)
W_Final = Weights
B_Final = Bias
print("No if iterenations performed", itercount)
output = [transfer(np.add(np.dot(Weights, Input_Matrix[i].transpose()), Bias)) for i in range(8)]
print(output)

# For Plotting the line using matplotlib
# df = pd.DataFrame(Input_Matrix)
# df
# df.plot(kind='scatter', x=0, y=1, s=30, color='DarkBlue');
# df1 = pd.DataFrame(W_Final)
# slope = -((df1[0] - 0) / (df1[1] - 0))
# print(slope)
# print(df1)
# x = np.arange(0, 5, 1)
# intercept = pd.DataFrame(B_Final / W_Final[0, 1])
# y = [slope * i + intercept for i in x]
# df1
# plt.plot(x, y)