Skeleton Code:

#Download Total.csv from FYP drive for verification.

import pandas as pd

import numpy as np

from sklearn import metrics

#loading the dataset

dataset = pd.read_csv('Total.csv')

S = dataset.iloc[:,0:7].values

t = dataset.iloc[:,7:8].values

#feature scaling

#feature scaling

from sklearn.preprocessing import StandardScaler

sc_S = StandardScaler()

sc_t = StandardScaler()

S2 = sc_S.fit_transform(S)

t2 = sc_t.fit_transform(t)

#fitting the SVR to the dataset

from sklearn.svm import SVR

regressor = SVR(kernel = 'rbf')

regressor.fit(S2, t2)

pred_t = regressor.predict(S2)

t_pred =sc_t.inverse_transform(pred_t)

errs = metrics.mean_absolute_error(t, t_pred)

print(errs)

result for 7 features:

• bpmax:

('Mean Absolute Error:', 17.920415662979593)

('Mean Squared Error:', 574.55000643544315)

('Root Mean Squared Error:', 23.969772765619684)

Variance score: 0.39

bpmin:

• ('Mean Absolute Error:', 4.1293880055297576)
• ('Mean Squared Error:', 101.27077558626165)
• ('Root Mean Squared Error:', 10.063338192978593)
• Variance score: 0.86

average:

('Mean Absolute Error:', 11.02)

('Root Mean Squared Error:', 17.01

Variance score: 0.625

results for 3 features:

• bpmax:
• ('Mean Absolute Error:', 19.836865103969824)
• ('Mean Squared Error:', 667.70987599952321)
• ('Root Mean Squared Error:', 25.840082739796387)
• Variance score: 0.29

bpmin:

• ('Mean Absolute Error:', 14.884539824328918)
• ('Mean Squared Error:', 937.56161531935163)
• ('Root Mean Squared Error:', 30.619627942209743)
• Variance score: -0.27

avg:

('Mean Absolute Error:', 17.35)

('Root Mean Squared Error:', 28.225)

Variance score: 0.01