Simple Linear Regression-----How to make my model more efficient?? I am working on a simple linear regression model,


  This is my Python code : 


import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

dataset=pd.read_csv('sample.csv')
X=dataset.iloc[:,:-1].values
Y=dataset.iloc[:,1].values

from sklearn.model_selection import train_test_split
X_train,X_test,Y_train,Y_test=train_test_split(X,Y,test_size=1/3)

from sklearn.linear_model import LinearRegression
regressor=LinearRegression()
regressor.fit(X_train,Y_train)

plt.scatter(X_train,Y_train,color='red')
plt.plot(X_train,regressor.predict(X_train),color='blue')
plt.title('X vs Y(Training Set)')
plt.xlabel('X')
plt.ylabel('Y')
plt.show()

plt.scatter(X_test,Y_test,color='red')
plt.plot(X_train,regressor.predict(X_train),color='blue')
plt.title('X vs Y(Test Set)')
plt.xlabel('X')[enter image description here][1]
plt.ylabel('Y')
plt.show()`



  This is my plot of Training Set Training Set 
  This is my plot of Test Set Test Set


How can I increase the efficiency of my ML model???
This is my first ML model, so all suggestions are welcome.
Thanks in Advance
You cannot really do much: the fit of the regressor is optimum, therefore it is the best that the algorithm can do given these points. What you can do, is change the weights of individual points of the dataset using the sample weight parameter of 

LinearRegression.fit(X, y, sample_weight)


method, to "attract" the line towards them and see how this affects the accuracy.

I would not expect notable difference because your data do not follow a linear pattern.