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EDA_hotel_booking_Samhitha_Template.ipynb
Hotel Bookings.csv

# Hotel Booking Analysis ## Table of Contents - [Overview](https://github.com/deepanshudagdi/EDA-Hotel-Bookings/blob/master/#overview) - [Objective](https://github.com/deepanshudagdi/EDA-Hotel-Bookings/blob/master/#objective) - [Data](https://github.com/deepanshudagdi/EDA-Hotel-Bookings/blob/master/#data) - [Analysis](https://github.com/deepanshudagdi/EDA-Hotel-Bookings/blob/master/#analysis) - [Insights](https://github.com/deepanshudagdi/EDA-Hotel-Bookings/blob/master/#insights) - [Conclusion](https://github.com/deepanshudagdi/EDA-Hotel-Bookings/blob/master/#conclusion) ## Overview This project centers on dissecting a hotel booking dataset to glean actionable insights and guide strategic decision-making for a hotel establishment. ## Objective The primary goal of this analysis is to decipher booking trends, cancellation behaviors, revenue patterns, and guest tendencies to facilitate data-driven business decisions. ## Data The dataset used for this analysis contains information about hotel bookings, including booking dates, guest details, and booking status. ## Analysis - Initial data exploration - Visualizations - Correlation analysis - Insights from the data ## Insights - **Booking Behavior**: - City Hotels had a higher proportion of bookings. - **Cancellation Rates**: - City Hotels experienced a higher cancellation rate, suggesting the need for improved cancellation management. - **Room Types**: - Certain room types were more popular, offering pricing optimization and targeted marketing potential. - **Geographic Insights**: - Portugal emerged as the leading source of guests, emphasizing the importance of regional targeting in marketing. - **Length of Stay**: - Shorter stays (1-4 days) were prevalent, with fewer longer stays. - **Seasonal Booking Patterns**: - Booking peaks in August and July indicated seasonality's impact on the hotel industry. - **Guest Types**: - Most guests were new, A smaller proportion were repeated guests. - **Revenue Analysis**: - Despite higher cancellation rates, City Hotels generated more revenue, indicating effective revenue generation strategies. - **Guest Loyalty**: - Resort Hotels had a higher percentage of repeated guests, suggesting advantages in loyalty programs or resort experiences. - **Cancellation Analysis**: - Cancellations were mainly made by new guests, emphasizing the need to understand and address their concerns. ## Conclusion The project concludes that informed decision-making based on data analytics can lead to improved business performance in the hotel industry. To achieve these improvements, the following recommendations were made: **Cancellation Management:** Implement strategies to reduce cancellation rates, especially in City Hotels, by offering flexible booking options or incentives for non-cancellation. **Loyalty Programs:** Strengthen loyalty programs to attract and retain repeated guests, potentially by offering exclusive benefits or discounts. **Pricing Optimization:** Optimize pricing strategies based on length of stay and seasonal demand, ensuring competitive rates and revenue maximization. **Marketing Focus:** Tailor marketing campaigns to target guest preferences, room types, and geographic regions for more effective outreach. **Guest Experience:** Enhance guest experience by addressing specific room type preferences and special requests, ultimately boosting satisfaction and loyalty. By leveraging these insights and recommendations, hotel management can make informed decisions, enhance guest satisfaction, and drive long-term business growth. ## Author: Deepanshu Dagdi

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