Create README.md

README.md

@@ -0,0 +1,124 @@
+---
+language:
+- en
+pretty_name: "Hotel Booking Cancellation Analysis"
+tags:
+- tabular
+- classification
+- exploratory-data-analysis
+- pandas
+license: "cc-by-4.0"
+task_categories:
+- classification
+---
+
+# Hotel Booking Cancellation Analysis
+
+## Overview
+
+This project explores hotel booking data in order to better understand customer behavior, with a focus on cancellations.  
+The goal is to identify patterns that may help hotels predict and reduce cancellation rates.
+
+The dataset was taken from Kaggle and then cleaned, processed, and analyzed using Python.
+
+---
+
+## Data Cleaning
+
+Before starting the analysis, several cleaning steps were performed:
+
+- Checked for missing values across all columns  
+- Verified and removed duplicate rows  
+- Reviewed data types and ensured consistency  
+- Explored summary statistics to understand distributions  
+
+Outliers were identified in variables such as lead time and average daily rate (ADR).  
+Instead of removing them completely, I chose to keep most of them, since they may represent real customer behavior (e.g., very early bookings or luxury pricing).  
+However, in some visualizations, extreme values were limited to improve readability.
+
+---
+
+## Question 1: Do cancellation rates differ between city hotels and resort hotels?
+
+![Hotel Type](hotel_type.png)
+
+City hotels show a higher cancellation rate compared to resort hotels.  
+This suggests that bookings in city hotels may be more flexible or less certain.
+
+In contrast, resort bookings are typically planned further in advance and are more stable.
+
+---
+
+## Question 2: Are bookings with a longer lead time more likely to be canceled?
+
+![Lead Time](lead_time.png)
+
+Bookings that were eventually canceled tend to have a higher average lead time.  
+This indicates that customers who book far in advance are more likely to change their plans.
+
+However, this is a general trend and not a strict rule.
+
+---
+
+## Additional Insight: How does cancellation rate change across lead time ranges?
+
+![Lead Time Groups](lead_time_groups.png)
+
+When dividing lead time into ranges, a clear pattern appears:  
+The longer the lead time, the higher the cancellation rate.
+
+This adds depth to the previous analysis by showing that the relationship is gradual, not just based on averages.
+
+---
+
+## Question 3: Do repeated guests cancel less than first-time guests?
+
+![Repeated Guests](repeated_guests.png)
+
+Repeated guests have a noticeably lower cancellation rate.  
+This suggests that returning customers are more confident and committed to their bookings.
+
+---
+
+## Question 4: How does average daily rate (ADR) relate to cancellation behavior?
+
+![ADR](adr.png)
+
+Higher-priced bookings tend to have slightly higher cancellation rates.  
+This may indicate that customers are more sensitive when prices are higher, or that these bookings are compared more carefully with alternatives.
+
+---
+
+## Key Insights
+
+- Cancellation is strongly influenced by lead time  
+- Returning customers are more reliable than new ones  
+- City hotels experience more cancellations than resort hotels  
+- Price may play a role in customer decision-making  
+
+Overall, customer behavior is not random.  
+It is influenced by timing, experience, and pricing factors.
+
+---
+
+## Business Recommendations
+
+Based on the analysis:
+
+- Encourage shorter lead time bookings to reduce cancellations  
+- Offer incentives for returning customers  
+- Apply stricter cancellation policies for long-term bookings  
+- Monitor pricing strategies for high-value bookings  
+
+These actions can help hotels improve planning and reduce uncertainty.
+
+---
+
+## Dataset Source
+
+The original dataset was taken from Kaggle:  
+"Hotel Booking Demand Dataset"
+
+The data was modified, cleaned, and analyzed for educational purposes.
+
+---