https://github.com/praveendecode/airways-booking-analysis
Analyzed airline booking data to optimize routes, pricing, and passenger experience for improved airline operations and customer satisfaction
https://github.com/praveendecode/airways-booking-analysis
classification machine-learning-algorithms macine-learning python random-forest-classifier
Last synced: 19 days ago
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Analyzed airline booking data to optimize routes, pricing, and passenger experience for improved airline operations and customer satisfaction
- Host: GitHub
- URL: https://github.com/praveendecode/airways-booking-analysis
- Owner: praveendecode
- Created: 2023-11-07T13:12:13.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-11-07T13:20:28.000Z (over 2 years ago)
- Last Synced: 2025-08-31T23:46:37.407Z (9 months ago)
- Topics: classification, machine-learning-algorithms, macine-learning, python, random-forest-classifier
- Language: Jupyter Notebook
- Homepage:
- Size: 1.28 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Airways Customer Booking Analysis
- Web Scraping and Data Analysis for Predicting Customer Buying Behavior
# Overview:
- This project involves web scraping, data preprocessing, and data analysis using Python, including NLP tasks such as topic modeling, sentiment analysis, and word clouds. It also includes a machine learning project to predict customer buying behavior, encompassing model building and feature importance analysis.
# Main Features of Project:
- Web Scraping and Data Analysis: Collecting online data through web scraping, preprocessing, and employing Python for NLP tasks to extract valuable insights.
- Predicting Customer Buying Behavior: Leading a machine learning project to predict customer buying behavior and sharing detailed findings and recommendations.
# Process Steps:
## Web Scraping and Data Analysis:
- Collected online data through web scraping.
- Conducted data preprocessing for analysis.
- Employed Python for NLP task including topic modeling, sentiment analysis, and word clouds.
- Extracted valuable insights from the data.
## Predicting Customer Buying Behavior:
- Led a machine learning project to predict customer buying behavior.
- Conducted model building and feature importance analysis.
- Prepared and shared a detailed report with findings and recommendations.
# Conclusion:
- This project showcases the power of web scraping and data analysis to extract valuable insights. Additionally, the machine learning aspect provides a foundation for predicting customer buying behavior, which can be invaluable for making informed business decisions and recommendations based on the findings.