Polynomial Regression

Combined Cycle Power Plant

Source

Attribute Information:

   Features consist of hourly average ambient variables

    - Temperature (T) in the range 1.81°C and 37.11°C,

    - Ambient Pressure (AP) in the range 992.89-1033.30 milibar,

    - Relative Humidity (RH) in the range 25.56% to 100.16%

    - Exhaust Vacuum (V) in teh range 25.36-81.56 cm Hg

    - Net hourly electrical energy output (EP) 420.26-495.76 MW

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

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)

Training the Polynomial Regression model on the Training set

from sklearn.preprocessing import PolynomialFeatures

poly_reg = PolynomialFeatures(degree = 4)

X_poly = poly_reg.fit_transform(X_train)

from sklearn.linear_model import LinearRegression

model = LinearRegression()

model.fit(X_poly, y_train)

LinearRegression()

Predicting the Test set results

y_pred = model.predict(poly_reg.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	430.93	426.18
1	448.01	451.10
2	444.38	442.87
3	445.81	443.70
4	461.45	460.59
...	...	...
1909	467.41	468.19
1910	433.69	431.16
1911	455.09	454.20
1912	446.74	444.13
1913	433.52	436.58

1914 rows × 2 columns

Evaluating the Model Performance

from sklearn.metrics import r2_score

r2_score(y_test, y_pred)

0.9428968311604174