Linear Regression
Skeleton Code:
Skeleton Code:
import pandas as pd
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
import numpy as np
from sklearn.metrics import mean_squared_error, r2_score
dataset = pd.read_csv('Total.csv',names = ['alpha','PIR', 'ptt', 'bpmax' ,'bpmin', 'hrfinal', 'ih', 'il', 'meu'])
X = dataset[['alpha','PIR', 'ptt']]
y = dataset[['bpmax', 'bpmin']]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
from sklearn.preprocessing import StandardScaler
sc_X=StandardScaler()
x_train=sc_X.fit_transform(X_train)
x_test=sc_X.transform(X_test)
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(x_train, y_train)
y_pred = regressor.predict(x_test)
print('Coefficients: \n', regressor.coef_)
from sklearn import metrics
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)))
# # Explained variance score: 1 is perfect prediction
print('Variance score: %.2f' % r2_score(y_test, y_pred))
3 features :
('Mean Absolute Error:', 21.918282691660238)
('Mean Squared Error:', 740.111279427762)
('Root Mean Squared Error:', 27.204986297143435)
Variance score: 0.11
[Finished in 0.5s]
bpmax:
- ('Mean Absolute Error:', 22.037723537655015)
- ('Mean Squared Error:', 750.5385174185102)
- ('Root Mean Squared Error:', 27.395958048925944)
- Variance score: 0.21
- [Finished in 0.4s]
bpmin:
('Mean Absolute Error:', 21.798841845675398)
('Mean Squared Error:', 729.6840414375896)
('Root Mean Squared Error:', 27.012664463869342)
Variance score: 0.01
[Finished in 0.4s]
7 feature:
avg:
('Mean Absolute Error:', 14.262672444398717)
('Mean Squared Error:', 433.5516139097295)
('Root Mean Squared Error:', 20.82190226443611)
Variance score: 0.52
[Finished in 0.5s]
bpmax:
('Mean Absolute Error:', 21.891395993250995)
('Mean Squared Error:', 745.21412281394419)
('Root Mean Squared Error:', 27.298610272575125)
Variance score: 0.21
bpmin:
- ('Mean Absolute Error:', 6.6339488955463848)
- ('Mean Squared Error:', 121.88910500551522)
- ('Root Mean Squared Error:', 11.040339895379816)
- Variance score: 0.83