A machine learning-based analytical system for researching cab transportation services in the Indian tourism industry is presented in this paper. Taxi services are essential for promoting tourism since they give passengers easy and adaptable transportation. However, the amount of transportation data created in urban contexts makes it difficult to analyse taxi usage trends, service effectiveness, and transportation demand. This paper suggests a data-driven approach that combines machine learning methods with an online application for examining transportation trends and taxi system performance in order to solve this problem.

In order to find trends in transportation utilisation, demand fluctuations, and operational efficiency, the suggested system gathers and analyses taxi-related data and uses machine learning algorithms. The web framework used in the system's implementation allows administrators and users to engage with the platform for analytical tasks and taxi service monitoring. Before the machine learning model is trained, the dataset is cleaned and arranged using data preprocessing techniques. In order to improve taxi services in tourist locations, the trained model aids in the analysis of transportation behaviour and produces valuable insights.