About the dataset

The data points were collected from a Combined Cycle Power Plant over 6 years (2006-2011) when the power plant was set to work with a full load. Features consist of hourly average ambient variables - Ambient Temperature (AT), Ambient Pressure (AP), Relative Humidity (RH) and Exhaust Vacuum (V) to predict the net hourly electrical energy output (PE) of the plant.

To load the dataset in your jupyter notebook, use the below command:

import pandas as pd
ccpp_data = pd.read_csv('https://raw.githubusercontent.com/dphi-official/Datasets/master/CCPP/Training_set_ccpp.csv')

Data Description

The dataset contains 9568 observations with 5 features/variables.

AT: Ambient Temperature - Numerical (values ranges from 1.81°C to 37.11°C)
AP: Ambient Pressure - Numerical (values ranges from 992.89 to 1033.30 milibar)
RH: Relative Humidity - Numerical (values ranges from 25.56% to 100.16%)
EV: Exhaust Vacuum - Numerical (values ranges from 25.36-81.56 cm Hg)
PE: Net hourly electrical energy output - Numerical (values ranges from 420.26-495.76 MW)

Evaluation Dataset

Load the evaluation dataset (name it as 'CCPP_eval'). You can load the data using the below command.

CCPP_eval = pd.read_csv('https://raw.githubusercontent.com/dphi-official/Datasets/master/CCPP/Testing_set_ccpp.csv')

Reference

This dataset is downloaded from the UCI Machine Learning Repository - https://archive.ics.uci.edu/ml/datasets/Combined+Cycle+Power+Plant

Combined Cycle Power Plant

Challenge Starts

Challenge Ends