Hotel Bookings Exploratory Data Analysis

• Tech Stack: Python, Pandas, NumPy, Matplotlib, Seaborn
• Project Focus: Analyzing booking trends and patterns to uncover insights
• GitHub Repository: Project Link

This project involved a detailed exploratory data analysis (EDA) of hotel booking data to uncover trends and patterns that influence booking behaviors. Key components of the project include:

• Data Cleaning: Handled missing values, duplicates, and inconsistent data entries for a clean dataset ready for analysis.
• Descriptive Analysis: Examined features such as booking cancellations, lead times, customer types, and room preferences to identify impactful trends.
• Visualization: Created interactive and static visualizations using Matplotlib and Seaborn to communicate findings effectively, such as heatmaps for seasonality and bar plots for customer segmentation.
• Key Insights:
  • Identified peak booking periods and their correlation with cancellations.
  • Analyzed the impact of lead times and special requests on booking confirmation rates.
  • Provided actionable insights for hotel managers to optimize room allocation and marketing strategies.

This comprehensive analysis provides valuable insights to improve operational efficiency, enhance customer satisfaction, and maximize revenue in the hospitality sector.