Recommendation System for Skincare Item

Abstract

This research paper thoroughly investigates the analysis of taxi demand at airports over a six-month period. Through the use of time series analysis and regression modeling techniques, the study aims to uncover significant patterns and trends in taxi demand, incorporate new features through time series insights, standardize the training data, and ultimately develop a predictive model to anticipate future airport taxi orders. The evaluation method utilized is the root mean square error (RMSE) metric. Leveraging the "taxi.csv" dataset, this study employs popular Python libraries such as pandas, matplotlib, scikit-learn, and stats models for data manipulation, visualization, and modeling. The scope of the study expands beyond mere data analysis to address key issues and concerns in the field.