Marine Habitat Clustering Using Hierarchical Analysis

Contained in this repo is a project that demonstrates how to generate synthetic marine ecological data and apply unsupervised machine learning (hierarchical clustering) to explore patterns in policy coverage across marine zones.

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Project Overview

• 🔧 Data Generation: Simulates 20 marine zones with binary presence/absence data for 6 ecological policies.
• 🧠 Distance Metric: Jaccard distance — ideal for binary attributes.
• 🌳 Clustering Method: Hierarchical clustering with complete linkage.
• 🌿 Visualization: Dendrogram to reveal how zones group based on shared protections.
• 📊 Output:
  • generated_marine_zones.csv — synthetic raw data
  • clustered_marine_zones.csv — same data with cluster labels
  • dendrogram_marine_zones.png — dendrogram image

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Ecological Policies Simulated

Each marine zone is evaluated for the presence (1) or absence (0) of the following protections:

• Coral Reef Protection
• Fishing Ban
• Turtle Nesting Zone
• Oil Drilling Ban
• Marine Sanctuary Status
• Mangrove Forest Protection

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🛠️ How to Run

1. Clone or download this repo
2. Install dependencies:

   pip install numpy pandas scipy matplotlib