This project aims to compare and contrast Principal Component Regression (PCR) and Partial Least Squares Regression (PLS) using the Air Quality Dataset. The dataset includes measurements of various air pollutants and meteorological variables. The primary objective is to evaluate the performance of PCR and PLS in handling multicollinearity and reducing dimensionality to predict benzene (C6H6) concentrations. The analysis includes data cleaning, outlier handling, and implementation of both regression techniques. Performance metrics such as RMSE and R-squared are used to compare the models, highlighting the strengths and weaknesses of each approach.

<The Geographical Analysis of Positive and Negative Reviews of the Three Hotels: Correlating Hotel Locations with Guest Sentiments>

Prototype of a human disturbance index for wolverines.