/Regression-with-Combined-Cycle-Power-Plant-Data-Set

This is a simple machine learning regression with a Combined Cycle Power Plant Dataset using 5 types of regressions.

Primary LanguageJupyter Notebook

Regression-with-Combined-Cycle-Power-Plant-Data-Set

Dataset : Combined Cycle Power Plant Data Set UCI Machine Learning Repository

  • Number of Instances : 47840
  • Number of Columns : 5
  • No Missing Values

Attribute Information

  • 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

Regressions Types

  • Multiple Linear Regression
  • Polynomial Regression
  • Support Vector Regression (SVR)
  • Decision Tree Regression
  • Random Rorest Regression