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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You can Download the code here.

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