Hotel Analysis

This project focuses on analyzing hotel booking data, with a special interest in rejected/canceled bookings across different cities, market segments, and time periods

The project is divided into three main phases:

• Data Cleaning – preparing and standardizing the raw dataset.
• Exploratory Data Analysis (EDA) – identifying booking rejection patterns.
• Data Modeling (Star Schema) – building a Fact Table and Dimension Tables to enable deeper business insights.

📁 Hotel_Booking_Project

│── 📓 Clean_hotel_data.ipynb

     Data Cleaning
      - Imported raw dataset.
      - Handled missing values.
      - Standardized column formats (dates, cities, markets).
      - Removed duplicates.
      - Ensured data quality and readiness for analysis.

│── 📓 Analysis.pbix

      Data Modeling (Star Schema)
      - To structure the data for analysis, I designed a Star Schema with one Fact Table and several Dimension tables

      Data Analysis  
      - Calculated the percentage of rejected bookings relative to total bookings.
      - Compared rejection rates across cities.
      - Analyzed rejection rates by market segment.
      - Tracked seasonal trends in rejections across months.

│── Tools & Technologies

      - Python (Pandas)
      - Jupyter Notebook
      - Power BI
      - Canva (for infographics & visual design)
      - Git & GitHub (for documentation and version control)