🌐 Hotel Booking Data Analysis with Pandas

Explore a real-world hotel booking dataset using pandas and NumPy.
Run interactive tasks, answer business questions, and extend the notebook with your own analysis.

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✨ Features at a Glance

• 📊 End-to-end exploratory data analysis (EDA) on hotel bookings.
• 🧹 Missing data detection and cleanup (e.g., dropping sparse company column).
• 💵 Revenue-focused metrics: ADR, total stay cost, average spend.
• 🧍 Guest behavior insights: repeat guests, length of stay, children/babies.
• 🌍 Demographics: country distributions and most common last names.
• 📅 Arrival patterns by day of month and day of week.

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🚀 Getting Started

1. Clone the repo -git clone https://github.com/rivu-intel45/hotel-booking-analytics-pandas.git cd hotel-booking-data-analysis
2. (Optional) Create a virtual environment -python -m venv venv
3. Windows -venv\Scripts\activate 5.macOS / Linux -source venv/bin/activate 6.Install dependencies -pip install -r requirements.txt 7.Launch Jupyter

Then open hotel-data-pandas.ipynb to start exploring.

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🎯 What You’ll Do Inside the Notebook

1. Understand the Data

• Inspect shape, column types, and memory usage of the dataset.
• Detect missing values and identify the column with the most missing data (company).

2. Clean and Prepare

• Drop or handle columns with excessive missing data.
• Create helper columns like total stay nights (weeknights + weekend nights).

3. Answer Business Questions

• What percentage of bookings are from repeat guests (isrepeatedguest)?
• What is the mean ADR (average daily rate) across all stays?
• What is the average stay length and total revenue per booking?
• Who paid the highest ADR and how much was it?

4. Explore Patterns

• Top 5 most common guest last names.
• Top 5 guest country codes.
• How many arrivals happened between the 1st and 15th of each month?
• Arrivals by weekday (Monday, Tuesday, etc.).

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🧪 Try These Interactive Tasks

Use these as prompts while editing or running the notebook:

• Change filters to analyze:

  • Only resort hotels vs city hotels.
  • Specific years (e.g., 2015 vs 2017).
  • Bookings from a specific country.

• Create your own metrics:

  • Revenue per night segment (short vs long stays).
  • Average ADR per month or season.
  • Impact of special requests on total cost.

• Extend the analysis:

  • Visualize distributions (histograms, bar charts, boxplots).
  • Compare repeat vs non-repeat guests on key metrics.

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✅ Requirements

-numpy -pandas -matplotlib -seaborn -jupyter

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💡 Ideas for Further Improvement

• Add interactive widgets with ipywidgets to filter by year, country, or hotel type.
• Turn key tasks into functions so others can easily reuse the analysis.