✈️ Airline Reviews Scraping

📝 Table of Contents

• 📚 Overview
• 🔍 Project Details
  • Objective
  • Data Source
  • Methodology
• ✨ Key Features
• ⚙️ Requirements
• 🚀 Getting Started
• 📈 Results and Insights
• 📄 License
• 📧 Contact

📚 Overview

Welcome to the Airline Reviews Scraping project! This repository provides a comprehensive tool for collecting and organizing customer feedback from the airline industry, specifically by scraping reviews from the Skytrax website. This project is ideal for data enthusiasts interested in gaining insights from user reviews and identifying key areas for airline service improvement.

🔍 Project Details

Objective

To scrape, organize, and analyze customer reviews for airlines to understand customer satisfaction trends, common complaints, and service strengths.

Data Source

• Website: Skytrax
• Data: Customer reviews, ratings, and feedback across multiple airlines

Methodology

1. Web Scraping

   • Airline Names: Gather a comprehensive list of airlines reviewed on Skytrax.
   • Review Data: Collect review details including ratings, review titles, review content, and service categories.

2. Data Processing

   • Structuring: Organize scraped data into a structured format (Pandas DataFrame) for easy analysis.
   • Data Cleaning: Remove inconsistencies and format text data for analysis.

3. Exploratory Data Analysis (EDA)

   • Visualization: Identify trends in customer feedback, highlighting areas like customer service and value for money.

✨ Key Features

• Comprehensive Data Collection: Scrapes detailed airline review data, including various service aspects.
• Flexibility: Easily scalable to include other review platforms or additional data points.
• Foundation for NLP: Processed data can be extended for sentiment analysis or predictive modeling.

⚙️ Requirements

• Python: Version 3.8 or higher
• Libraries:
  • requests
  • BeautifulSoup
  • pandas
  • lxml

All dependencies are listed in requirements.txt.

📈 Results and Insights

This project organizes airline review data in a format ideal for further analysis. Key insights include customer satisfaction trends and common areas of concern, which can support decision-making and improvements in airline services.

📄 License

This project is licensed under the MIT License.

📧 Contact

If you have questions or feedback, feel free to reach out:

• Email: fatimaliyva@gmail.com
• LinkedIn: Fatima Aliyeva
• GitHub: FatimaAliyeva01

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