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README.md

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+# Hotel Booking Cancellation Analysis
+
+## Overview
+This project explores hotel booking data in order to understand the main factors that influence booking cancellations.
+
+The goal is to identify patterns in customer behavior and provide insights into what makes a booking more likely to be canceled.
+
+
+
+## Dataset
+The dataset is based on the "Hotel Booking Demand" dataset from Kaggle.
+
+It includes over 100,000 observations and features such as:
+- Lead time
+- Hotel type
+- Customer type
+- Average daily rate (ADR)
+- Cancellation status
+
+The dataset was cleaned and prepared before analysis.
+
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-{}
+
+## Question 1: Do cancellation rates differ between hotel types?
+
+![Hotel Type](hotel_type.png)
+
+The results show that city hotels have higher cancellation rates compared to resort hotels.
+
+This suggests that bookings in city hotels may be less stable, possibly due to differences in customer behavior or booking flexibility.
+
+---
+
+## Question 2: Are bookings with longer lead time more likely to be canceled?
+
+![Lead Time](lead_time.png)
+
+The plot shows that canceled bookings tend to have a higher average lead time.
+
+This indicates that bookings made further in advance are less certain and more likely to be canceled.
+
+---
+
+## Additional Insight: Cancellation rates across lead time ranges
+
+![Lead Time Groups](lead_time_groups.png)
+
+By grouping lead time into ranges, we can clearly see that cancellation rates increase as lead time increases.
+
+This confirms that the relationship is consistent and not driven only by extreme values.
+
+---
+
+## Question 3: Do repeated guests cancel less?
+
+![Repeated Guests](repeated.png)
+
+Repeated guests show lower cancellation rates compared to first-time guests.
+
+This suggests that returning customers are more committed and confident in their bookings.
+
+---
+
+## Question 4: How does price (ADR) relate to cancellation behavior?
+
+![ADR](adr.png)
+
+The distribution shows that higher-priced bookings tend to have greater variability and are slightly more likely to be canceled.
+
+Outliers were kept in the dataset but handled carefully in the visualization.
+
+---
+
+## Data Cleaning & Decisions
+- Duplicate records were identified and removed.
+- Missing values were examined but not all were removed, as some represent meaningful absence of information.
+- Outliers were identified but kept to preserve real-world behavior.
+
+---
+
+## Conclusion
+The analysis shows that booking cancellations are influenced by several factors, including lead time, hotel type, customer type, and pricing.
+
+These insights can help businesses better understand customer behavior and improve decision-making.
+
+---
+
+## Data Source
+The dataset used in this project is based on the "Hotel Booking Demand" dataset, originally available on Kaggle.
+
+The data was cleaned and processed as part of this analysis.
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