here is my code. after submission the accuracy i am getting is 10%, what improvisation should i do to have accuracy between 97-100% as told by you!!
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
dfx = pd.read_csv(‘C:/Users/JAINY/Downloads/Training Data/Linear_X_Train.csv’)
dfy = pd.read_csv(‘C:/Users/JAINY/Downloads/Training Data/Linear_Y_Train.csv’)
dfxx = pd.read_csv(‘C:/Users/JAINY/Downloads/Test Cases/Linear_X_Test.csv’)
x = dfx.to_numpy()
y = dfy.to_numpy()
xx = dfxx.to_numpy()
x_new = (x-x.mean())/x.std()
y_new = y
xx_new = xx
plt.scatter(x_new,y_new)
#plt.show()
#def difference(x_new,y_new,theta):
theta[0] * x_new[i] + theta[1] - y_new[i]
theta[0] * x_new[i] + theta[1] - y_new[i]
def error(x_new,y_new,theta):
er = 0
for i in range(x_new.shape[0]):
er = er + ( (theta[0] * x_new[i] + theta[1]) - y_new[i]) ** 2
return er
def gradient(x_new,y_new,theta):
gradi = np.zeros((2,))
for i in range(x_new.shape[0]):
gradi[0] = (theta[0] * x_new[i] + theta[1] - y_new[i]) * x_new[i]
gradi[1] = (theta[0] * x_new[i] + theta[1] - y_new[i])
return gradi
def descent(x_new,y_new,rate=0.01):
theta = np.zeros((2,))
itr = 0
err_list = []
while (itr <= 1000):
e = error(x_new,y_new,theta)
err_list.append(e)
grad = gradient(x_new,y_new,theta)
theta[0] = theta[0] - rate * grad[0]
theta[1] = theta[1] - rate * grad[1]
itr = itr + 1
return theta,err_list
slope_inter,list_error = descent(x_new,y_new,rate=0.01)
plt.plot(list_error)
plt.show()
#print(list_error[0])
#plt.scatter(x_new,y_new,color=‘blue’)
#plt.show()
#plt.scatter(x_new,slope_inter[0]*x_new+slope_inter[1],marker=’^’,color=‘orange’)
#plt.show()
#plt.scatter(xx_new,slope_inter[0]*xx_new+slope_inter[1],marker=’^’,color=‘orange’)
#plt.show()
lm = np.zeros((1250))
lm1 = np.zeros((1250))
for i in range(1250):
lm[i] = slope_inter[0]*xx_new[i]+slope_inter[1]
#print(lm[0],lm[1])
file = np.savetxt(‘C:/Users/JAINY/Downloads/ans2.csv’,lm,delimiter=","‘w+’)