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Using K-Means Clustering Algorithm to analyse and find optimised locations where Anti-Poaching Devices can be placed, in poaching hotspots.

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AngadDogra/Tiger-Wildlife-Corridor-Detection

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Tiger-Wildlife-Corridor-Detection

Project Overview

This project leverages the K-Means clustering algorithm to analyze and identify optimal locations for deploying anti-poaching devices in poaching hotspots across India. Using latitude and longitude data from a shapefile, we visualize poaching trends and provide actionable insights for wildlife conservation.

Objectives

  • Utilize K-Means clustering to analyze poaching hotspot data.
  • Identify strategic locations for anti-poaching device placement.
  • Visualize results on a map of India generated using geographic data.

Features

  • Data Processing: Clean and format input data for analysis.
  • Clustering Algorithm: Implement K-Means to group data points by geographic location.
  • Visualization: Generate maps that display clusters and recommended placement areas.
  • Interactive Analysis: Provide visual outputs to guide conservation strategies.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contact

For any questions or collaboration opportunities, feel free to reach out to [email protected] and [email protected].

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Using K-Means Clustering Algorithm to analyse and find optimised locations where Anti-Poaching Devices can be placed, in poaching hotspots.

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