Hotel Booking Cancellation Analysis Using Data Science

Hotel Booking Data Analysis

A comprehensive data analysis project focused on understanding hotel booking patterns and identifying the key factors that influence booking cancellations.

The project involved working with a dataset of more than 119,000 hotel bookings and covered the complete data analysis workflow, including:

- Data cleaning and preprocessing

- Handling missing values and duplicate records

- Outlier detection and treatment

- Feature engineering and creation of meaningful features

- Exploratory Data Analysis (EDA)

- Data visualization using multiple charts

- Analysis of booking patterns, customer behavior, and cancellation trends

- Extracting actionable insights from the data

Several features were analyzed and engineered, including Total Guests, Total Stay Nights, Is Family, Room Changed, Previous Bookings, and Company-related information.

The analysis also explored how factors such as hotel type, lead time, length of stay, customer type, deposit type, and seasonality affect booking cancellation behavior.

Tools & Technologies: Python, Pandas, NumPy, Matplotlib, Seaborn

Project Type: Team Project