Wind-Power-Output-Predictor

This is a simple regressor which is trained on telemetry from a bunch of wind turbines from several different locations. It takes environmental factors such as temperature, wind speeds at different altitudes, wind direction at different altitudes, etc as input & predicts the percentage of the turbine output compared to it's full capacity.

The data was pulled from this Kaggle page.

The project contains the following models trained on the dataset:

• Decision tree
• Random forest
• Linear regression
• Gradient boost
• XG Boost

I have chosen multiple metrics to compare all the models, they are as follows:

• Mean absolute error
• Root mean squared error
• R²
• Adjusted R²

They're visually plotted on graphs corresponding to their scales (R squared & Adj R squared are converted into percentages)

_{Note: Can you guess the colour theme of the graphs?}