Flight Data Analysis from Priceline

Project Overview

This project involves exploratory data analysis (EDA) on flight data from the Priceline website, sourced from Kaggle. The aim is to understand trends, patterns, and general insights from the dataset, such as price distributions, common routes, and flight duration. The analysis is performed using NumPy, scikit-learn, and matplotlib.

Table of Contents

• Project Overview
• Dataset
• Installation
• Data Analysis and Methodology
• Results
• Contributing
• License

Dataset

The dataset used for this project is available on Kaggle, containing information about flights listed on Priceline. Key features include:

• Airlines: The carrier operating each flight.
• Route: The origin and destination of each flight.
• Price: The listed ticket price.
• Flight Duration: Time taken for each route.
• Additional Fields: Departure and arrival times, layovers, etc.

Installation

To run this project locally:

1. Clone the repository:

   https://github.com/slytechiefrommagentashore/Flight-Data-Analysis-from-Priceline
   cd flight-data-analysis

Data Analysis and Methodology

The analysis uses Python and includes the following libraries:

• NumPy: For numerical computations and data handling.
• scikit-learn: For preprocessing and data cleaning (no modeling was performed).
• Matplotlib: For visualizations, including graphs and plots to reveal data trends.

Key Steps

Data Cleaning:

• Checked for missing values and handled them appropriately.
• Removed any obvious outliers to ensure a clean dataset for analysis.

Exploratory Data Analysis (EDA):

• Conducted descriptive statistical analysis to understand distributions in flight prices, durations, and airline frequencies.
• Created various graphs (within the Jupyter notebook) to visualize trends, such as:
  • Price distribution across different flights.
  • Common routes and their average prices.
  • Airline comparisons for average ticket prices.

Results

Key insights from the analysis include:

• Price Patterns: Identification of high-cost and low-cost routes and the distribution of ticket prices.
• Airline Insights: Comparative analysis of different airlines based on their average prices.
• Duration Trends: Overview of how flight duration may influence ticket prices.

These findings can aid travelers in identifying cost-effective flights or provide insights for airline market analysis.

Contributing

Contributions are welcome! If you’d like to contribute:

1. Fork the repository.
2. Create a new branch (git checkout -b feature-branch).
3. Make your changes.
4. Submit a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for more details.

Dataset License

The dataset used in this project is made available under the CC0: Public Domain license. This means you are free to copy, modify, and distribute the data without any restrictions. For more information, please see the Kaggle dataset page.