CruiseInsight is a comprehensive data analytics initiative designed to delve into the intricacies of web-based cruise bookings. Our primary objective is to extract meaningful insights about passenger behavior, preferences, and booking patterns through the application of advanced statistical and machine learning methodologies.

• Data Preprocessing: In-depth cleansing and preparation of transactional data, making it suitable for analytical endeavors.
• Exploratory Data Analysis (EDA): Detailed visualization of passenger data to uncover trends, patterns, and insights.
• Predictive Modeling: Employment of Logistic Regression to accurately forecast passenger behavior and preferences.
• Feature Importance: Identification and analysis of key factors that significantly influence passenger decisions.

To set up the project locally, follow these steps:

1. Clone the repository:

   git clone https://github.com/your-username/CruiseInsight.git

Ensure you have the following installed:

• Python 3.x
• Pandas
• NumPy
• Matplotlib
• Seaborn
• Scikit-learn
• Statsmodels

Install the required packages:

pip install pandas numpy matplotlib seaborn scikit-learn statsmodels

• Data Preprocessing: Load and clean your dataset, performing all necessary preprocessing steps.
• Exploratory Data Analysis: Use various plotting functions to explore and visualize different aspects of the data.
• Model Building: Develop and train the Logistic Regression model to predict passenger behavior.
• Evaluation: Measure model performance with metrics such as accuracy, confusion matrix, and ROC curve.

• passenger_behavior_analysis.py: The main script containing all analysis and modeling code.
• data/: Directory for datasets (replace placeholder paths with actual data paths).
• plots/: Directory for saving generated plots.

We welcome contributions to CruiseInsight! For guidelines on contributing, please refer to CONTRIBUTING.md. This document includes our code of conduct and the process for submitting pull requests.

CruiseInsight is licensed under the MIT License. For more details, see the LICENSE.md file.

• Heartfelt thanks to all team members who have contributed to this project.
• Recognition of external datasets and resources utilized in this project.