Machine Learning Pipeline Application on Power Plant. (Part 1) - Projects Based Learning

This is an end-to-end Project of performing Extract-Transform-Load and Exploratory Data Analysis on a real-world dataset, and then applying several different machine learning algorithms to solve a supervised regression problem on the dataset. Our goal is to accurately predict power output given a set of environmental readings from various sensors in a natural gas-fired power generation plant. Power generation is a complex process, and understanding and predicting power output is an important element in managing a plant and its connection to the power grid. The operators of a regional power grid create predictions of power demand based on historical information and environmental factors (e.g., temperature). They then compare the predictions against available resources (e.g., coal, natural gas, nuclear, solar, wind, hydro power plants).