The task is to perform regression on Combined Cycle Power Plant dataset to predict the net hourly electrical energy output (EP) of the plant.

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

Scatterplots and various statistics like Mean, Median, Range, First Quartile, Third Quartile and Inter-Quartile Range of each of the variables in the dataset are used to analyze the data.

For each predictor, fit a simple linear regression to predict the response variable EP and find out the statistically significant predictors using t-test. Also, fit a multiple regression model to predict the response using all the predictors and perform hypothesis testing. Compare the results of univariate regression models with multiple regression.
Fit a linear regression model by considering non-linear associations and pairwise interaction between the predictors and the response and do hypothesis testing.

Perform K-Nearest Neighbor Regression on the dataset using both normalized and raw features to predict the response. Plot the training and testing error for different values of K ranging from 1 to 100.