/Data-Analysis-in-Hospitality-Domain

AtliQ Grands, a make-believe hospitality company operating in four cities, took on a strategic analysis using Python and the Pandas library, along with visualization tools like Seaborn and Matplotlib, to tackle market competition. The goal was to use data-driven insights to overcome challenges and boost business growth.

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Data-Analysis-in-Hospitality-Domain

๐Ÿจ AtliQ Grands: A Strategic Hospitality Analysis ๐ŸŒ

In response to market competition, AtliQ Grands, an imaginary hospitality company with hotels in four cities, embarked on a comprehensive data-driven analysis to overcome challenges and drive business growth. The project is divided into three major steps:

๐Ÿงน Data Cleaning: To ensure accurate insights, I meticulously cleaned the data:

  • Fixed negative values in the Number of Guests.
  • Removed outliers in Revenue Generated & Realized.
  • Handled NaN values in Ratings Given.

๐Ÿ”„ Data Transformation: Turning raw data into actionable insights:

  • Introduced 'Occupancy Percentage' using successful bookings and capacity.
  • Explored insights based on the transformed data.

๐Ÿ“Š Insights Generated:

  1. ๐Ÿข Presidential rooms boast the highest occupancy rate.
  2. ๐ŸŒ† Delhi leads in occupancy, followed closely by other cities.
  3. ๐Ÿ“… Weekends show higher occupancy (>70%) than weekdays (50.9%).
  4. ๐Ÿ“‰ Bangalore consistently has the lowest occupancy rate.
  5. ๐Ÿ—“๏ธ August data may be incomplete; only available for Mumbai and Bangalore.

๐Ÿ’ฐ Revenue Analysis:

  • ๐Ÿ“ˆ Delhi has high occupancy but the least realized revenue.
  • ๐Ÿ’ต Mumbai records the highest revenue.
  • ๐Ÿ“Š Total revenue per month peaks in July.

๐Ÿš€ Business Insights:

  1. Bangalore sees a sharp drop in average successful bookings compared to Mumbai.
  2. ๐Ÿ’ก Strategic insights on revenue from cancellations for AtliQ Industries hotels.
  3. ๐ŸŒŸ AtliQ Seasons excels in low cancellation rates due to competitive pricing and strategic locations.

๐ŸŒŸ Service Quality and Ratings:

  • ๐ŸŒ Average ratings are uniform across all cities.
  • ๐ŸŒŸ None of the ratings are โ‰ฅ4, highlighting the need for service quality enhancement.

๐Ÿค” Bookings Analysis: ๐ŸŒ 40.9% bookings are from 'others'; strategic analysis recommended for market capture.