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https://github.com/hafidaso/analytical-insights-into-hotel-booking-dynamics

This project entails a detailed analysis of a hotel booking dataset, focusing on uncovering patterns and insights related to hotel pricing, cancellation rates, customer demographics, and market segment behaviors. The objective is to provide data-driven recommendations to optimize hotel operations
https://github.com/hafidaso/analytical-insights-into-hotel-booking-dynamics

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This project entails a detailed analysis of a hotel booking dataset, focusing on uncovering patterns and insights related to hotel pricing, cancellation rates, customer demographics, and market segment behaviors. The objective is to provide data-driven recommendations to optimize hotel operations

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# Analytical Insights into Hotel Booking Dynamics: A Comprehensive Study on Pricing, Cancellations, and Market Trends



## Project Overview

This project entails a detailed analysis of a hotel booking dataset, focusing on uncovering patterns and insights related to hotel pricing, cancellation rates, customer demographics, and market segment behaviors. The objective is to provide data-driven recommendations to optimize hotel operations, enhance customer satisfaction, and maximize revenue.



## Data Source

The dataset includes various attributes related to hotel bookings, such as hotel type, booking status, customer details, stay duration, and financials.

[Data](https://www.kaggle.com/datasets/khairullahhamsafar/hotels-booking-data-cleaned-version)



## Key Analytical Areas

1. **Cancellation Trends**: Examining cancellation rates over different time periods and across customer demographics.

2. **Pricing Analysis**: Assessing the average daily rates of different hotel types and comparing them across various criteria.

3. **Customer Demographics Impact**: Analyzing how guest origin and composition affect booking patterns.

4. **Market Segment Analysis**: Understanding the distribution and impact of different market segments on bookings and cancellations.

5. **Special Requests Correlation**: Investigating the relationship between special requests and customer satisfaction indicators.



## Methodology

- Data Preprocessing: Cleaning and preparing the data for analysis, including handling missing values and outliers.

- Exploratory Data Analysis: Utilizing statistical and visual techniques to explore various aspects of the dataset.

- Comparative Analysis: Drawing comparisons between different categories to identify significant patterns and trends.



## Tools and Technologies

- Python: For data processing and analysis.

- Libraries: Pandas for data manipulation, Matplotlib and Seaborn for visualization, and other supporting libraries.



## Key Findings

- **Cancellation Insights**: Identified key factors influencing cancellation rates and their variation over time and by customer nationality.

- **Pricing Strategy**: Uncovered pricing trends that highlight differences in pricing strategies between city and resort hotels.

- **Customer Behavior**: Analyzed how different guest demographics impact booking choices.

- **Market Segment Dynamics**: Explored the influence of various market segments on the hotel business.



## Recommendations for Business Strategy

Based on the analysis, several strategic recommendations are provided to optimize pricing, improve customer targeting, and reduce cancellation rates.



## Conclusion

This project offers valuable insights into the hotel booking process, aiding in making informed decisions to enhance business performance in the hospitality industry.



## Author Information

[Hafida Belayd](https://www.linkedin.com/in/hafida-belayd/)