Ship Performance Clustering Analysis

🚢 Project Title: Ship Performance Clustering Analysis

📌 Project Overview

Maritime operations are growing in complexity, demanding data-driven strategies for improved efficiency and cost reduction. This project applies clustering analysis to simulated ship performance data, revealing operational patterns and offering actionable insights for maritime stakeholders, particularly in the Gulf of Guinea region.

🎯 Objectives

• Identify distinct operational patterns among ships.

• Uncover trends in speed, cargo weight, and fuel efficiency.

• Provide data-backed recommendations for optimizing fleet operations.
  
  

📂 Data Description

The dataset consists of simulated yet realistic performance metrics for various ship types operating in the Gulf of Guinea.

Numerical Features:

• • Speed (knots)

  • Engine power (kW)

  • Operational cost (USD)

  • Fuel efficiency
    
    

Categorical Features:

• • Ship type

  • Route type

  • Maintenance status

  • Weather condition
    
    

🛠️ Tools Used

• Python

• Pandas

• Matplotlib

• Seaborn

• Scikit-learn
  
  

🧠 Skills Demonstrated

• Data Simulation

• Data Cleaning & Preprocessing

• Exploratory Data Analysis (EDA)

• PCA (Dimensionality Reduction)

• Clustering (KMeans)

• Data Visualization
  
  

⚙️ Methodology

Data Preprocessing

• Imputed missing values

• Converted date columns to appropriate datetime formats

• Investigated and flagged categorical anomalies (e.g., similar distributions across clusters)
  
  

Clustering Approach

• Applied KMeans Clustering to group ships with similar performance characteristics

• Used Principal Component Analysis (PCA) for dimensionality reduction and enhanced visualization

Cluster Profiling

• Cluster 0: High efficiency, moderate speed, and cost — suitable for cost-conscious operations

• Cluster 1: High revenue potential but with elevated operational costs

• Cluster 2: Specialized/niche-operating ships with unique characteristics

• Anomaly: Uniformity in categorical features across clusters suggests need for further feature engineering or alternative clustering techniques

🔍 Key Findings

• Clear differentiation in operational profiles among clusters

• Categorical feature uniformity may indicate data simulation limits or model insensitivity

• PCA helped clarify cluster separability, but more nuanced clustering methods could improve insights

✅ Conclusion & Future Work

This analysis demonstrates how clustering can uncover hidden patterns in ship performance data, supporting smarter maritime decisions.

Next Steps:

• Improve categorical feature differentiation through enhanced simulation or encoding techniques

• Test advanced clustering methods like DBSCAN or Hierarchical Clustering

• Integrate real-time performance data for dynamic clustering and monitoring
  
  

🔗 Explore More

• 💻 GitHub Repository: Ship Performance Clustering Analysis

• 📥 Download Dataset: Kaggle Link
  
  

💬 Connect With Me

Got questions, feedback, or ideas?
Let’s collaborate or discuss more on maritime data analytics. Feel free to connect or reach out via  Email.

Visualization