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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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Sourav-Pattanayak/Data-Analysis-in-Hospitality-Domain

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Data-Analysis-in-Hospitality-Domain

🏨 AtliQ Grands: A Strategic Hospitality Analysis 🌐

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:

🧹 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.

🔄 Data Transformation: Turning raw data into actionable insights:

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

📊 Insights Generated:

  1. 🏢 Presidential rooms boast the highest occupancy rate.
  2. 🌆 Delhi leads in occupancy, followed closely by other cities.
  3. 📅 Weekends show higher occupancy (>70%) than weekdays (50.9%).
  4. 📉 Bangalore consistently has the lowest occupancy rate.
  5. 🗓️ August data may be incomplete; only available for Mumbai and Bangalore.

💰 Revenue Analysis:

  • 📈 Delhi has high occupancy but the least realized revenue.
  • 💵 Mumbai records the highest revenue.
  • 📊 Total revenue per month peaks in July.

🚀 Business Insights:

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

🌟 Service Quality and Ratings:

  • 🌐 Average ratings are uniform across all cities.
  • 🌟 None of the ratings are ≥4, highlighting the need for service quality enhancement.

🤔 Bookings Analysis: 🌐 40.9% bookings are from 'others'; strategic analysis recommended for market capture.

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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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