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Hotel Booking Cancellation Prediction
The Hotel Booking Cancellation Prediction project is based on a clear and scalable architecture, designed to transform hotel booking data into actionable insights. Data is collected and cleaned with Pandas, then enriched through interactive visualizations (Matplotlib, Seaborn, Plotly) to highlight key trends: booking channels, customer profiles, seasonality, and the financial impact of cancellations. The business logic is handled by machine learning models trained with Scikit-learn, while MLflow ensures experiment tracking and reproducibility of results.
Architecture
- Python: main language for data collection and processing.
- Pandas: manipulation and cleaning of booking data.
- Scikit-learn: training and evaluation of machine learning models.
- Matplotlib & Seaborn: visualization of trends and metrics.
- Jupyter Notebook: interactive environment for exploration and documentation of analyses.
- MLflow: experiment tracking and model management.
- PySpark: even though the dataset is relatively small, using PySpark allows manipulation of this distributed library and demonstrates proficiency for future large-scale use cases.
Hotel Booking Cancellation Prediction is a Python application designed to analyze hotel booking data and predict the probability of cancellation. The goal is to help establishments better manage their resources and optimize their occupancy rate.
Hotels face a high rate of booking cancellations, which complicates resource management and leads to financial losses. Booking data is often heterogeneous (dates, length of stay, room type, customer origin), making it difficult to identify the factors that truly influence the probability of cancellation. The challenge is therefore to transform this raw data into actionable insights to anticipate customer behavior and reduce the impact of cancellations.
The Hotel Booking Cancellation Prediction project goes beyond building a predictive model. It provides key indicators to help hotels understand and anticipate customer behavior. Results are presented through dashboards and visualizations that make it possible to identify:
- Cancellation rates by booking channel (direct website, OTA, agencies, etc.).
- Customer profiles most likely to cancel (geographic origin, type of stay, length of stay).
- The impact of seasons and periods on cancellations.
- Average financial indicators related to cancellations (estimated loss, occupancy rate).
Even though the dataset is relatively small, the integration of PySpark allows manipulation of a distributed library and demonstrates the ability to handle large-scale data in real-world contexts. This business-oriented approach transforms data into actionable insights, enabling hotels to:
- Adjust their pricing and overbooking strategies.
- Optimize resource management (staff, rooms).
- Improve customer relations by targeting at-risk segments.
Key Features
- Interactive dashboard
- Booking channel analysis
- Key financial indicators
- Predictive models (ML)
- Experiment tracking (MLflow)
- Distributed data handling with PySpark