linear-regression

rpld.py

from sklearn.model_selection import train_test_split  
from sklearn.linear_model import LinearRegression  
from sklearn import metrics  
import matplotlib.pyplot as plt  
import pandas as pd  
import numpy as np  

dataset  = pd.read_csv('machine.csv')  
X = dataset[['125', '256', '6000','256.1','16','128']]
Y = dataset['198']  

X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=0)  

regressor= LinearRegression()  
regressor.fit(X_train, y_train)  

coeff_df = pd.DataFrame(regressor.coef_, X.columns, columns=['Coefficient'])  
y_pred = regressor.predict(X_test)  
df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})

print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))  
print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred))  
print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))