momentarek1/hotel_booking_data

is_canceled
Values
0 → Booking was not canceled
1 → Booking was canceled

This makes the dataset suitable for a binary classification problem.

🧠 Machine Learning Use Cases

The dataset can be used to develop several machine learning applications.

1. Booking Cancellation Prediction

Predict whether a reservation will be canceled.

Input:
Booking + Customer + Reservation Features

                ↓

        Machine Learning Model

                ↓

       Cancellation Prediction

        0 → Not Canceled
        1 → Canceled

Possible algorithms include:

Logistic Regression
Decision Tree
Random Forest
XGBoost
LightGBM
CatBoost
Neural Networks
2. Customer Behavior Analysis

The dataset can be used to understand customer booking behavior based on:

Lead time
Previous cancellations
Repeated guest status
Customer type
Market segment
Distribution channel
Special requests
3. Revenue Analysis

The adr feature can be used to analyze:

Average daily room rates
Pricing patterns
Market segments
Seasonal variations
Hotel type and pricing relationships
4. Booking Pattern Analysis

The dataset can help analyze:

Peak booking periods
Length of stay
Weekend vs weekday stays
Arrival patterns
Booking lead times
🔍 Data Categories

The features can broadly be grouped into several categories.

🏨 Hotel Information
hotel
reserved_room_type
assigned_room_type
📅 Arrival & Stay Information
arrival_date_year
arrival_date_month
arrival_date_week_number
arrival_date_day_of_month
stays_in_weekend_nights
stays_in_week_nights
👥 Guest Information
adults
children
babies
country
is_repeated_guest
customer_type
📖 Booking Information
lead_time
booking_changes
deposit_type
agent
company
days_in_waiting_list
💰 Pricing
adr
🍽️ Services & Requests
meal
required_car_parking_spaces
total_of_special_requests
📢 Marketing & Distribution
market_segment
distribution_channel
🔄 Reservation Outcome
is_canceled
reservation_status
reservation_status_date
🔐 Personal / Sensitive Information
name
email
phone-number
credit_card

Privacy Note: Personally identifiable or sensitive booking information such as names, email addresses, phone numbers, and credit card information should be removed, anonymized, or securely handled before using the dataset for public machine learning experiments or publishing it online.

📈 Recommended Exploratory Data Analysis

Before training a machine learning model, the following analysis can be performed:

Cancellation Analysis

Analyze the distribution of:

is_canceled

and compare cancellation rates between:

City Hotels
Resort Hotels
Market segments
Customer types
Deposit types
Lead Time Analysis

Investigate the relationship between:

lead_time

and:

is_canceled
Pricing Analysis

Analyze:

adr

across:

Hotel types
Months
Market segments
Customer types
Stay Duration

Calculate total stay duration:

total_stay_nights =
stays_in_weekend_nights + stays_in_week_nights

This can be used to investigate whether longer stays have different cancellation behavior.

🧹 Recommended Data Preprocessing

Before using the dataset for machine learning, several preprocessing steps may be required.

Missing Values

Some columns may contain missing values, particularly:

children
country
agent
company

Missing values should be handled according to the intended analysis.

Possible approaches include:

Imputation
Replacing missing categorical values with "Unknown"
Removing records when appropriate
Categorical Encoding

Categorical features such as:

hotel
meal
market_segment
distribution_channel
deposit_type
customer_type

can be encoded using:

One-Hot Encoding
Label Encoding
Target Encoding
CatBoost Encoding

depending on the machine learning model.

Date Processing

The reservation date information can be transformed into useful temporal features.

For example:

arrival_date_year
arrival_date_month
arrival_date_week_number
arrival_date_day_of_month

can be combined or transformed into:

Season
Quarter
Day of week
Arrival date
Peak/off-peak period
⚠️ Potential Data Leakage

Special attention should be given to columns that may contain information unavailable at the time a cancellation prediction would actually be made.

For example:

reservation_status
reservation_status_date

may reveal the final outcome of a reservation.

Therefore, these columns should generally not be used as input features when predicting is_canceled, because they can introduce target leakage.

Similarly, personally identifiable information such as:

name
email
phone-number
credit_card

should not be used as predictive features.

🏗️ Example Machine Learning Pipeline

A typical cancellation prediction workflow could be:

Hotel Booking Dataset
        │
        ▼
Data Cleaning
        │
        ▼
Missing Value Handling
        │
        ▼
Feature Engineering
        │
        ▼
Categorical Encoding
        │
        ▼
Train / Test Split
        │
        ▼
Feature Scaling
        │
        ▼
Machine Learning Model
        │
        ▼
Cancellation Prediction
        │
        ▼
Model Evaluation
📏 Recommended Evaluation Metrics

Because is_canceled is a classification target, suitable evaluation metrics include:

Accuracy

Measures the percentage of correctly classified bookings.

Precision

Measures how many predicted cancellations were actually cancellations.

Recall

Measures how many actual cancellations were successfully identified.

F1-Score

Provides a balance between precision and recall.

ROC-AUC

Measures the model's ability to distinguish between canceled and non-canceled reservations.

A confusion matrix is also recommended for detailed classification analysis.

💡 Possible Projects Using This Dataset

This dataset can be used for projects such as:

🏨 Hotel Cancellation Prediction

Predict whether a customer will cancel their reservation.

📊 Hotel Business Intelligence

Analyze booking trends and customer behavior.

💰 Revenue Optimization

Study pricing and revenue patterns using ADR and booking characteristics.

👥 Customer Segmentation

Group customers according to booking behavior.

📅 Demand Analysis

Analyze hotel demand across months, seasons, and market segments.

🤖 Predictive Hospitality Analytics

Build an end-to-end machine learning system for hotel decision support.

🛠️ Technologies

Recommended technologies for working with this dataset:

Python
Pandas
NumPy
Matplotlib
Seaborn
Scikit-learn
XGBoost
CatBoost
Jupyter Notebook
📁 Suggested Project Structure
hotel-booking-data/
│
├── data/
│   └── hotel_booking_data.csv
│
├── notebooks/
│   ├── 01_data_exploration.ipynb
│   ├── 02_data_preprocessing.ipynb
│   └── 03_cancellation_prediction.ipynb
│
├── models/
│   └── hotel_cancellation_model.pkl
│
├── README.md
└── requirements.txt
📌 Dataset Summary
Property	Description
Dataset Name	Hotel Booking Data
Domain	Hospitality / Hotels
Data Type	Structured / Tabular
Main Task	Binary Classification
Target	is_canceled
Main Use Case	Booking Cancellation Prediction
Additional Tasks	EDA, Customer Analysis, Revenue Analysis
Data Format	CSV / Tabular
🎯 Key Objectives

The dataset provides a foundation for studying:

Hotel reservation behavior
Booking cancellation patterns
Customer characteristics
Hotel demand
Pricing behavior
Market segmentation
Reservation management
Predictive analytics in hospitality
👨‍💻 Author

Momen

This dataset can be used as part of machine learning and data science projects focused on hospitality analytics, customer behavior, and hotel booking cancellation prediction.