Impact of Big Data on Transport for London (TFL)

Impact of Big Data on Transport for London (TFL)

The transportation sector is undergoing a significant transformation fueled by the ever-growing power of big data. As depicted in Figure 1, interest in big data has surged in recent years, reflecting its vast potential for improving efficiency, safety, and passenger experience. Transport for London (TfL), responsible for managing London's complex network of buses, trains, and cycling infrastructure, has emerged as a frontrunner in adopting big data solutions.

Change in Medicine prescription patterns pre-COVID and post-COVID period

Change in Medicine prescription patterns pre-COVID and post-COVID period

This project analyzes the impact of the COVID-19 pandemic on medication prescription patterns in England by comparing pre-COVID data from 2018 with post-COVID data from 2023. Using NHS Open Data, the study highlights changes in prescribed medications, focusing on biological systems, medication types, and specific drug categories.

Ola Electric Scooter Sentiment Analysis Using Reddit Data

Ola Electric Scooter Sentiment Analysis Using Reddit Data

This project conducts a comprehensive sentiment analysis of Ola Electric's first scooter model using Reddit comments. The analysis explores customer experiences, common discussion themes, and overall sentiment towards Ola’s electric scooters, providing valuable insights into user satisfaction, concerns, and key product features.

Business Analytics: Solving Real-World Problems with Data

Business Analytics: Solving Real-World Problems with Data

In the dynamic world of business, decision-making is crucial. From logistics management to staffing optimization, businesses rely on data-driven insights to stay competitive. This report explores four real-world business problems and how data analytics can provide actionable solutions.

Operational Research implementation on Luggage Packing Optimization

Operational Research implementation on Luggage Packing Optimization

This project explores the use of optimization techniques to maximize luggage capacity while considering weight restrictions, trip specifics, and item importance. By leveraging the Knapsack Problem and the Dual Simplex Algorithm, the model provides efficient packing plans that help travelers minimize excess weight and maximize space utilization. The study integrates operational research methodologies and business optimization strategies, offering a practical solution to travel-related packing challenges.

MSc. Data Science Disertation: About AI-Powered Chatbot for University Websites

MSc. Data Science Disertation: About AI-Powered Chatbot for University Websites