Missing Map Area Prediction on Italy Map

This project aims to predict missing map areas on an Italy map using the K-Nearest Neighbors (KNN) algorithm. The input consists of 5 images labeled from 1 to 5, with missing maps ranging from 10% to 50%. The KNN algorithm is implemented in Python, with varying values of K (1, 3, 5, 7, and 9) used for prediction.

Project Overview

• Objective: Predict missing map areas on an Italy map using KNN.
• Input: 5 images labeled 1 to 5 with varying percentages of missing maps (10% to 50%).
• Algorithm: K-Nearest Neighbors (KNN) with K values of 1, 3, 5, 7, and 9.
• Language: Python

Project Structure

• images/: Contains the input images labeled 1 to 5.
• src/: Source code for the KNN algorithm and prediction.
  • knn.py: KNN implementation and prediction logic.
  • main.py: Main script to execute the prediction.
• results/: Directory to store the prediction results.
• README.md: Project documentation (this file).

Usage

1. Place the input images (labeled 1 to 5) in the images/ directory.
2. Run the main.py script to execute the prediction.

python src/main.py

3. The prediction results will be stored in the results/ directory.

Requirements

• Python 3.x
• Libraries: scikit-learn, numpy

Future Improvements

• Explore different distance metrics for KNN.
• Experiment with different image preprocessing techniques.
• Implement more advanced machine learning algorithms for comparison.

Note

This project does not include actual map data and is for educational and illustrative purposes only.

Feel free to modify and expand upon this project to suit your needs!

Happy coding!