regression

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
df=pd.read_csv('Salary.csv')
df.head()
X=df.iloc[:,:-1].values
y=df.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=0.33,random_state=0)
from sklearn.linear_model import LinearRegression
lr=LinearRegression()
lr.fit(X_train,y_train)
prd=lr.predict(X_test)
plt.scatter(X_train,y_train,c='red')
plt.plot(X_train,lr.predict(X_train),c='blue')
plt.xlabel("Years of Experience")
plt.ylabel("Salary")
plt.title("Years of Experience v/s Salary(training set)")
plt.show()
plt.scatter(X_test,y_test,c='red')
plt.plot(X_test,prd,c='blue')
plt.xlabel("Years of Experience")
plt.ylabel("Salary")
plt.title("Years of Experience v/s Salary(test set)")
plt.show()
from sklearn.metrics import mean_absolute_error,mean_squared_error
print(f'ABS: {mean_absolute_error(y_test,prd):.3f} ')
print(f'MSE: {mean_squared_error(y_test,prd):.3f} ')
print(f'RMSE: {np.sqrt(mean_squared_error(y_test,prd)):.3f} ')
print(f'Score: {lr.score(X_test,y_test):.3f} ')