This open-source algorithm is designed to enable dynamic web scraping for monitoring flight prices on United Airlines' website. By leveraging the power of Python's pandas and selenium libraries, users can automate the process of checking for fluctuations in flight prices, saving valuable time and potentially reducing travel expenses. The algorithm offers adaptability, allowing users to explore data from various airlines or gather information for specific dates.
Furthermore, the project extends beyond web scraping to encompass data analysis and visualization using Plotly and Dash. Through the integration of these powerful tools, users can create interactive dashboards that transform flight information into dynamic visuals and insightful graphs. This algorithm/platform unlocks the potential to uncover hidden patterns and gain deeper insights into the airline industry. Ultimately, it provides a flexible and user-friendly flight analytics solution, empowering users to make informed decisions about their travel plans and optimize their flight choices to save money and time.
Prototype Blueprints
Actual Dashboard
- Data Processing & Manipulation: Numpy, Pandas
- Static and Dynamic Webscrapping: BeautifulSoup, selenium
- Dashboard Infrastructure: Dash
- Visualizations: Plotly
The Dashboard generated is daily updated with the flight prices webscrapped from different airlines. The visuals provided allow the user to derive insights and implement his own analyis that would potentially save a significant amount of money! The dashboard allows daily tracking and monitoring of prices and thus flight dates demand,allowing us to choose the most adequate departure and return dates for our planned trips.