https://github.com/anmolian/hotel_booking_demand_prediction

Big Data Analytics
https://github.com/anmolian/hotel_booking_demand_prediction

apache-spark docker fpgrowth gcp kmeans-clustering linear-regression random-forest

Last synced: 2 months ago
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Big Data Analytics

• Host: GitHub
• URL: https://github.com/anmolian/hotel_booking_demand_prediction
• Owner: Anmolian
• License: mit
• Created: 2025-02-10T20:01:34.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2025-02-10T23:12:07.000Z (over 1 year ago)
• Last Synced: 2025-08-05T02:38:43.471Z (11 months ago)
• Topics: apache-spark, docker, fpgrowth, gcp, kmeans-clustering, linear-regression, random-forest
• Language: Jupyter Notebook
• Homepage:
• Size: 7.6 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
![Project Banner](https://raw.githubusercontent.com/Anmolian/Hotel_Booking_Demand_Prediction/main/Cloud_Computing.jpg)

# Hotel Booking Demand Prediction

## Technologies Used: GCP, Apache Spark, Docker, Hadoop

- Leveraged Google Cloud Platform with Hadoop, Docker, and Apache Spark to process vast datasets, expedite data handling, and build machine learning models. Developed a Random Forest classifier attaining a 78% AUC-ROC, predicting booking cancellations.

- Applied unsupervised learning techniques such as K-means clustering to segment the most profitable customers based on lead time and ADR (Average Daily Rate), influencing hotel profitability strategies.

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