Have a look at some of my work
This project demonstrates the use of multi-agent systems in the travel and tourism industry. The project is a web application that allows users to plan their trips by interacting with multiple agents, each responsible for different aspects of the trip, such as flights, hotels and weather . The agents communicate with each other to provide a seamless travel experience for the user.
This project was submitted as a part of my academic requirement towards my course in Deep Learning at LSE. The Data used in the project was generously provided by the organizers of PAN CLEF 2017. We apply 3 approaches to the project - Deep average Network, CNN with LSTM and Bi-Directional RNN with Attention mechanism
Data Mining has found its use case in a wide variety of industries. One such field is the travel and tourism industry, where different type of data is gathered for each traveler, whether from booking a cab or from booking hotel accommodation. The research in this project analyzes trends on data from the popular homestay and lodging website Airbnb for listed properties in London.
This project was submitted as a part of my academic requirement towards my course in LSE. The given situation is that our friend, say XYZ, has been learning to drive for a whole and is considering taking the practical automobile test in the United Kingdom. There are two viable options - Take the practical test at the nearest test centre to his or her residence or take it at the LSE's nearest exam centre. i.e. Wood Green
Carsafe is an insurance company dealing in automobile insurance. They are looking to analyze their downside risk by analyzing their customer's arrival, departure and accident patterns. Their analysts have come up with these numbers and have asked to estimate the probability of ending up with a capital amount of less than £30000 at the end of the year. Carsafe also wants an estimate of the capital at the end of 12 months, along with a 95% confidence interval of the estimate.
