# Code To implement Logistic Regression Algorithm in Python from scratch using Numpy only

**Following is the complete code to implement Logistic Regression Algorithm in Python from Scratch using Numpy only:**

import numpy as np

import pandas as pd

def Loss_Function(target,Y_pred):

return np.mean(pow((Y_pred-target),2))

def pred(X_test):

return np.dot(X_test,w)+b

dataset = pd.read_csv('E:/tutorials/linreg_data.csv')

#print(dataset)

X_train = dataset.iloc[:,:-1].values

Y_train = dataset.iloc[:,1].values

print(X_train)

print(np.shape(Y_train))

Y_train = Y_train.reshape(-1,1)

print(np.shape(Y_train))

# initializing weights and bais

w=.5

b=.5

for i in range(1000000):

Y_pred = np.dot(X_train,w)+b

loss = Loss_Function(Y_train,Y_pred)

if(i%100==0):

print("iteration",i,"loss---------------->>>>>", loss)

grad_weight = np.dot((Y_pred-Y_train).T,X_train)/X_train.shape[0]

grad_bais = np.mean(Y_pred-Y_train)

w = w - .0001*grad_weight

b = b - .0001*grad_bais

Y_out = pred(1)

print(Y_out)

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