Support Vector Regression (SVR)

Importing the Relevant Libraries

import numpy as np
import pandas as pd

Importing the Dataset

url = "https://DataScienceSchools.github.io/Machine_Learning/Sklearn/Case_Study/PowerPlant/PowerPlant.csv"

df = pd.read_csv(url)

df.head()

	AT	V	AP	RH	PE
0	8.34	40.77	1010.84	90.01	480.48
1	23.64	58.49	1011.40	74.20	445.75
2	29.74	56.90	1007.15	41.91	438.76
3	19.07	49.69	1007.22	76.79	453.09
4	11.80	40.66	1017.13	97.20	464.43

Declaring the Dependent & the Independent Variables

X = df.iloc[:, :-1].values

y = df.iloc[:, -1].values

y = y.reshape(len(y),1)

Splitting the dataset into the Training set and Test set

from sklearn.model_selection import train_test_split

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

Feature Scaling

from sklearn.preprocessing import StandardScaler


sc_X = StandardScaler()

X_train = sc_X.fit_transform(X_train)


sc_y = StandardScaler()

y_train = sc_y.fit_transform(y_train)

Training the SVR model on the Training set

from sklearn.svm import SVR

model = SVR(kernel = 'rbf')

model.fit(X_train, y_train)

/home/bahar/anaconda3/lib/python3.7/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  return f(**kwargs)

SVR()

Predicting the Test set results

y_pred = sc_y.inverse_transform(model.predict(sc_X.transform(X_test)))

Comparing Predicted Y with Real Y (Test Set)

data = pd.DataFrame()

pd.set_option('precision', 2)

data['Predicted_Y'] = y_pred

data['Real_Y'] = y_test

data

	Predicted_Y	Real_Y
0	431.22	426.18
1	448.88	451.10
2	444.04	442.87
3	446.71	443.70
4	460.43	460.59
...	...	...
1909	467.73	468.19
1910	433.68	431.16
1911	455.18	454.20
1912	447.12	444.13
1913	432.72	436.58

1914 rows × 2 columns

Evaluating the Model Performance

from sklearn.metrics import r2_score

r2_score(y_test, y_pred)

0.945384075850576