Follow these directions to run the code

1. Clone the repository:

   git clone https://github.com/dinopapaya/Cheapest-Flight-Finder---Project2---66.git
   cd Cheapest-Flight-Finder---Project2---66

2. Create and activate a virtual environment:

   py -m venv .venv
   .venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

4. Command-line usage:

   You can inspect the cheapest fare between two airports directly from the terminal:

   python main.py ABE PIE

5. Streamlit dashboard:

   Launch the interactive dashboard to explore routes visually:

   streamlit run app.py

Team Name: Aviation

Team Members: Siddharth Bandaru and Anish Gude

Project Title: Cheapest Flight Finder

Problem: Finding the cheapest flight itinerary to a given destination can be time-consuming and confusing due to the large number of airlines, fluctuating prices, and the variety of direct and multi-leg options available. This project automates the process by computing the most cost-effective flight route between two airports in the United States using real flight route data.

Motivation: Travelers often spend hours comparing prices across booking platforms. Our project simplifies this process by modeling flights as a weighted graph and applying shortest-path algorithms to find the optimal (minimum-cost) route. The goal is to provide an interactive, visual tool that helps users quickly understand the cheapest way to travel across the U.S.

Data: All Airline Flight Routes in the US (Kaggle).

Tools:

Python: Core implementation of Dijkstra’s and Bellman-Ford algorithms for efficient shortest path computation. Used for visualization, data handling, and integration between backend logic and front-end interface.

Streamlit: Builds an interactive web application interface.

Folium: Generates interactive maps to visualize the shortest flight path.

Pandas: Used for efficient data loading and preprocessing of the flight dataset.

Algorithm implementation: Python

Visualization: Python(using Streamlist and Folium for interactive map displays)

Visuals: The system will visualize the algorithm’s pathfinding process and final results using Streamlit and Folium.

Distribution:

Anish: Implementing the Dijkstra algorithm.

Siddharth: Designed and implemented the Python-based visualization interface using Streamlit and Folium. Also contributed to algorithm testing and integration between backend and frontend.

Anish: Focused on developing and debugging the graph algorithms (Dijkstra and Bellman-Ford) in Python, ensuring efficiency and correctness. Also assisted in data preprocessing and system testing.

Reference:

All Airline Flight Routes in the US (Kaggle): https://www.kaggle.com/datasets/oleksiimartusiuk/all-airline-fight-routes-in-the-us