Hey, so I was building the logistic regression model (vectorized implementation) and this is the code I have written, I don’t know why it isn’t working, could you please help me with this and point out the mistake in the code,it would be of great help.The dataset I have used is here dataset.

Here is the link for the same question I asked on StackExchange (it has a couple of other codes as well).

Thanks in Advance!

```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
#sigmoid function
def sigmoid(z):
a=1/(1+np.exp(-z))
return a
#predicted y value for a given w,b ans x
def y_out(x_test,w_ini,b_ini):
m,n=x_test.shape
a=sigmoid(np.dot(w_ini,np.transpose(x_test))+b_ini)
a=a.reshape(m,1)
return a
#loss of a single training example
def loss(x_train,y_train,w_ini,b_ini):
l=0
l=-(y*np.log(y_out(x_train,w_ini,b_ini))+(1-y)*np.log(1-y_out(x_train,w_ini,b_ini)))
return l
#the cost function
def cost(x,y,w,b):
J=0
m,n=x.shape
J=(1/m)*np.sum(loss(x,y,w,b))
return J
#derivative terms
def gradient(x_train,y_train,w_ini,b_ini):
m,n=x_train.shape
dw=np.zeros(n)
dw=dw.reshape(len(dw),1)
db=0.
dw=(1/m)*(np.dot(np.transpose(x_train),y_out(x_train,w_ini,b_ini)-y_train))
db=(1/m)*(np.sum(y_out(x_train,w_ini,b_ini)-y_train))
dw=dw.reshape(1,len(dw))
return dw,db
#gradient descent
def optimize(x_train,y_train,w_ini,b_ini,alpha,itirations):
m,n=x.shape
w=w_ini
b=b_ini
for i in range(itirations):
dw,db=gradient(x_train,y_train,w,b)
w=w-alpha*(dw)
b=b-alpha*(db)
return w,b
dataset=pd.read_csv("ex2data1.csv")
x=dataset.iloc[:,:-1].values
y=dataset.iloc[:,-1].values
y=y.reshape(len(y),1)
m,n=x.shape
#initialization
w=np.zeros(n)
b=0.
#running the algoritm
w_out,b_out=optimize(x,y,w,b,1e-5,10000)
#parameters after running gradient descent
print(w_out,b_out)
```