hotel boooking data analysis

Project Overview This project analyzes hotel booking behavior to uncover insights around booking trends, cancellations, revenue, guest demographics, and customer value. The pipeline takes raw booking data through cleaning and preprocessing, engineers new features (including RFM-based customer segmentation), models the data, and visualizes the results in an interactive Power BI dashboard. 🎯 Objectives Explore booking patterns across cities, hotels, room types, and booking channels Clean and prepare a large-scale (600K row) real-world dataset Engineer meaningful features (stay length, weekend bookings, domestic vs. international, booking counts, etc.) Segment guests using RFM (Recency, Frequency, Monetary) analysis into loyalty tiers (Bronze, Silver, Gold, Premium, Titanium) Build a relational data model and an interactive dashboard for business insights 🛠 Tools & Technologies Python (Pandas, NumPy, Matplotlib) — EDA, cleaning, preprocessing, feature engineering, RFM segmentation Power BI — data modeling and dashboard/visualization Jupyter Notebook — analysis workflow 📊 Dataset Size: ~600,000 rows Key columns: booking_id, hotel_id, hotel_name, city, country, hotel_stars, room_type, guest_name, guest_email, adults, children, checkin_date, checkout_date, nights, booking_date, booking_channel, payment_method, price_per_night_usd, total_price_usd, booking_status, guest_rating, special_requests