A comprehensive Data Warehouse and Data Mining project designed to analyze hotel booking data and generate meaningful business insights. The system integrates booking information into a centralized data warehouse and applies data mining techniques to discover patterns, predict trends, and support strategic decision-making.
The Hotel Booking Data Warehouse project focuses on collecting, transforming, storing, and analyzing hotel booking data from various sources. By utilizing ETL processes and data mining algorithms, the system helps hotel managers understand customer behavior, identify booking trends, predict cancellations, and optimize occupancy rates.
- 🏨 Hotel Booking Data Management
- 📊 Data Warehouse Design
- 🔄 ETL (Extract, Transform, Load) Process
- 📈 Booking Trend Analysis
- ❌ Cancellation Prediction
- 👥 Customer Segmentation
- 💰 Revenue Analysis
- 📉 Occupancy Rate Monitoring
- 📋 Interactive Reports and Dashboards
| Technology | Purpose |
|---|---|
| Python | Data Processing |
| Pandas | Data Cleaning & Analysis |
| NumPy | Numerical Computation |
| Scikit-learn | Data Mining Algorithms |
| MySQL | Data Warehouse Storage |
Hotel-Booking-Data-Warehouse/
│
├── dataset/
│ └── hotel_bookings.csv
│
├── notebooks/
│ ├── ETL_Process.ipynb
│ ├── Data_Cleaning.ipynb
│ ├── Data_Mining.ipynb
│ └── Visualization.ipynb
│
├── sql/
│ └── warehouse_schema.sql
│
├── reports/
│ └── analysis_report.pdf
│
│
└── README.md
Fact_Bookings
-------------
Booking_ID
Customer_ID
Hotel_ID
Date_ID
Room_Type
Booking_Amount
Stay_Duration
Cancellation_StatusDim_Customer
-------------
Customer_ID
Customer_Name
Country
Customer_TypeDim_Hotel
----------
Hotel_ID
Hotel_Name
Location
CategoryDim_Date
---------
Date_ID
Day
Month
Year
QuarterJotiprova Mitra
🎓 Computer Science Engineering Student
💻 Data Warehousing | Data Mining | Python | SQL
🚀 Passionate about transforming data into actionable insights.