Cruise Vessel Performance Analysis

This repository contains the analysis of two cruise vessels' performance of Cruise Company, using Python code written in a Quarto (.qmd) file. The project focuses on evaluating operational metrics such as fuel efficiency, power generation, and other Key Performance Indicators (KPIs) to optimize maritime resource usage and improve sustainability.

Introduction

This project analyzes the performance of two cruise vessels of Cruise Company by looking at trends over time, and calculating key operational metrics and KPIs. By understanding how vessels convert fuel into power and how they operate under different conditions, the analysis aims to optimize future operations while reducing the environmental impact.

Data Overview

The dataset includes several operational metrics like fuel consumption, energy generation, vessel speed, and operational hours. These metrics were used to calculate KPIs, which serve as benchmarks for assessing vessel performance.

Key Performance Indicators (KPIs)

The KPIs cover various dimensions of vessel performance, such as energy efficiency, fuel efficiency, and operational output. These metrics are essential for improving the overall sustainability and economic efficiency of the vessels.

Performance Analysis

This section evaluates how well each vessel performs based on the KPIs, identifying areas where improvements can be made. The report generated provides insights into optimizing fuel usage, power generation, and operational practices for future voyages.

Conclusion

The analysis highlights significant insights into the operational efficiencies of cruise vessels, with a focus on improving fuel usage and reducing environmental impact.

Limitations and Outlook

While the current dataset provides valuable insights, the absence of historical data limits the ability to track improvements over time. If historical data spanning at least the last ten years were available, we could analyze trends in fuel efficiency, energy consumption, and carbon emissions, providing a clearer picture of operational progress. Additionally, incorporating carbon emissions data would offer a more comprehensive view of the vessels' environmental impact. Future work could integrate these elements for a more robust analysis.

Repository Structure

Key Components:

1. task_data Folder: Contains the raw dataset, the imputed dataset, and the description of the variables used for the KPI analysis.
2. Scripts Folder: The Quarto file (.qmd) which contains the Python code for KPI calculations and visualizations.
3. Output Folder: The final report (ProjectTask.html) generated from the Quarto file.
4. Instructions.txt: Detailed setup and running instructions, including required libraries.

Instructions for Navigation

1. Data: Go to the task_data/ folder to find the dataset used for analysis.
2. Scripts: The Quarto file containing the analysis code can be found in the folder. You can run this file to generate the analysis report.
3. Output: The HTML report (ProjectTask.html) can be found in the folder and is viewable in any web browser. This file contains all the visualizations, KPI metrics, and interpretations of the data.
4. Instructions: Detailed setup instructions, including the libraries required to run the Quarto file, are provided in the instructions.txt file.

Running the Analysis

1. Download the repository.
2. Follow the setup steps in the instructions.txt file to install the necessary libraries and dependencies.
3. Open the Quarto (.qmd) file to modify or rerun the analysis.
4. Run the analysis to generate the report by executing the Quarto file