# 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 npimport pandas as pddef Loss_Function(target,Y_pred):    return np.mean(pow((Y_pred-target),2))def pred(X_test):    return np.dot(X_test,w)+bdataset = pd.read_csv('E:/tutorials/linreg_data.csv')#print(dataset)X_train = dataset.iloc[:,:-1].valuesY_train = dataset.iloc[:,1].valuesprint(X_train)print(np.shape(Y_train))Y_train = Y_train.reshape(-1,1)print(np.shape(Y_train))# initializing weights and baisw=.5b=.5for 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    grad_bais = np.mean(Y_pred-Y_train)    w = w - .0001*grad_weight    b = b - .0001*grad_baisY_out = pred(1)print(Y_out)To Understand the code Visit Here.You can Download the code here.
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