ML Regression on CCPP dataset

Data Analysis through ML Regression on the Combined Cycle Power Plant dataset (2006-2011).

Introduction

In this project, various regression models such as multi-linear, polynomial, SVR, Decision Tree Regression, and Random Forest Regression will be built in order to perform prediction of the net hourly electrical energy output (EP) of the plant. The dataset is from UCI ML dataset repository here.

Dataset

5 Features in total:

• Temperature (T)
• Ambient Pressure (AP)
• Relative Humidity (RH)
• Exhaust Vacuum (V)
• Net hourly electrical energy output (EP)

Model Results

Model Name	$R^2$ score	Adjusted $R^2$ score	MSE	RMSE
Multiple Linear Regression	0.9325315554761303	0.9323901862890713	19.733699303497644	4.44226285844249
Polynomial Linear Regression	0.9458193300147094	0.9457058032048922	15.847186890065263	3.9808525330719378
Support Vector Regression	0.9480784049986258	0.9479696117141808	15.186434937782039	3.896977667087924
Decision Tree Regression	0.922905874177941	0.9227443359363023	22.549093991640547	4.748588631545224
Random Forest Regression	0.9615908334363876	0.9615103532550076	11.234213991640557	3.3517479009675766