Hotel Booking Prediction Using ANN
Download Dataset: Hotel Booking Dataset
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Problem Statement
The hospitality industry faces significant financial losses due to hotel booking cancellations. By analyzing historical booking data such as booking lead time, customer type, room type, meal plan, market segment, and previous cancellations, an Artificial Neural Network (ANN) can be developed to predict whether a booking is likely to be cancelled or not. This enables hotels to improve occupancy planning, revenue management, and customer service.
Dataset Information
| Attribute | Description |
|---|---|
| Dataset Name | Hotel Booking Demand Dataset |
| Problem Type | Binary Classification |
| Target Variable | is_canceled |
| Total Records | 119,390 |
| Total Attributes | 32 |
| Missing Values | Present in a few attributes (e.g., Company, Agent, Country, Children) |
| Output Classes | 2 (Cancelled / Not Cancelled) |
Input Features
| Feature | Description |
|---|---|
| hotel | Type of hotel (Resort Hotel or City Hotel) |
| lead_time | Number of days between booking and arrival |
| arrival_date_year | Year of arrival |
| arrival_date_month | Month of arrival |
| arrival_date_week_number | Week number of arrival |
| arrival_date_day_of_month | Day of arrival |
| stays_in_weekend_nights | Number of weekend nights booked |
| stays_in_week_nights | Number of weekday nights booked |
| adults | Number of adults |
| children | Number of children |
| babies | Number of babies |
| meal | Type of meal booked |
| country | Customer's country of origin |
| market_segment | Booking market segment |
| distribution_channel | Booking distribution channel |
| is_repeated_guest | Whether the customer is a repeated guest (0 = No, 1 = Yes) |
| previous_cancellations | Number of previous cancelled bookings |
| previous_bookings_not_canceled | Number of previous successful bookings |
| reserved_room_type | Room type reserved by the customer |
| assigned_room_type | Room type assigned at check-in |
| booking_changes | Number of booking modifications |
| deposit_type | Type of deposit paid |
| agent | Travel agent ID responsible for the booking |
| company | Company ID associated with the booking |
| days_in_waiting_list | Number of days the booking remained on the waiting list |
| customer_type | Customer category (Transient, Contract, Group, etc.) |
| adr | Average Daily Rate (average room price per night) |
| required_car_parking_spaces | Number of parking spaces requested |
| total_of_special_requests | Number of special requests made by the customer |
| reservation_status | Final booking status |
| reservation_status_date | Date when the reservation status was updated |
Target Variable
| Feature | Description |
|---|---|
| is_canceled | 0 = Booking Not Cancelled, 1 = Booking Cancelled |
Research Questions
RQ1: Dataset Loading and Understanding
How can the hotel booking dataset be loaded and analyzed to understand its structure, attributes, and target variable?
RQ2: Data Preprocessing
How can missing values, duplicate records, and irrelevant attributes be identified and handled before training the Artificial Neural Network?
RQ3: Data Transformation
How can categorical variables such as Hotel Type, Meal Plan, Market Segment, Customer Type, Reserved Room Type, Assigned Room Type, Deposit Type, and Country be converted into numerical values using appropriate encoding techniques?
RQ4: Feature Scaling
How can feature scaling (Normalization or Standardization) improve the learning performance and convergence of the Artificial Neural Network?
RQ5: Feature Selection
Which booking-related attributes have the greatest influence on predicting whether a hotel booking will be cancelled?
RQ6: Input and Output Preparation
How can the dataset be separated into input features (X) and the target variable (Y) for hotel booking cancellation prediction?
RQ7: Data Splitting
How should the hotel booking dataset be divided into training and testing datasets to ensure reliable model development and evaluation?
RQ8: ANN Model Design
How can an Artificial Neural Network architecture be designed with suitable input, hidden, and output layers for hotel booking cancellation prediction?
RQ9: ANN Model Initialization
Which Python libraries, Deep Learning framework, activation functions, optimizer, and loss function should be selected to initialize the Artificial Neural Network model?
RQ10: Model Compilation
How can the Artificial Neural Network be compiled using an appropriate optimizer, loss function, and evaluation metrics?
RQ11: Model Training
How can the Artificial Neural Network be trained using the training dataset by selecting suitable epochs, batch size, and validation strategy?
RQ12: Model Performance Evaluation
How can the performance of the Artificial Neural Network be evaluated using Accuracy, Precision, Recall, F1-Score, Confusion Matrix, ROC Curve, and AUC Score?
RQ13: Model Selection
How can the trained Artificial Neural Network be validated and selected as the best model for predicting hotel booking cancellations?
RQ14: Prediction on New Booking Data
How can the trained Artificial Neural Network be used to predict whether a new hotel booking will be Cancelled or Not Cancelled?
Easy Story to Remember ANN
Think of ANN as a student preparing for an exam:
- Dataset → Textbook 📘
- Features → Chapters
- Input Layer → Student reads the chapters
- Hidden Layers → Student understands and connects concepts
- Weights → Importance given to each chapter
- Bias → Extra adjustment based on experience
- Activation Function → Decides what information is useful
- Prediction → Student answers the question
- Loss → Marks lost due to wrong answers
- Optimizer (Adam) → Teacher explains mistakes
- Backpropagation → Student corrects understanding
- Epoch → One complete revision of the entire textbook
- Batch → Reading a few pages at a time
- Accuracy → Final exam score
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