matanask/hotelscancel

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.

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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.

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Question 1: Do cancellation rates differ between city hotels and resort hotels?

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.

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Question 2: Are bookings with a longer lead time more likely to be canceled?

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.

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Additional Insight: How does cancellation rate change across lead time ranges?

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.

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Question 3: Do repeated guests cancel less than first-time guests?

Repeated guests have a noticeably lower cancellation rate.
This suggests that returning customers are more confident and committed to their bookings.

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Question 4: How does average daily rate (ADR) relate to cancellation behavior?

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.

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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.

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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.

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Dataset Source

The original dataset was taken from Kaggle:
"Hotel Booking Demand Dataset"

The data was modified, cleaned, and analyzed for educational purposes.

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