This project aims to implement the “Improving Neural-Network Classifiers Using Nearest Neighbor Partitioning” paper, exploring a novel approach to enhance the performance of neural networks through the integration of Evolutionary Computing and KNN Partitioning techniques. The key features and implementations include:

• Neural Network Training: Utilizing Evolutionary Computing algorithms to train neural network models, optimizing their architecture and weights for improved classification performance.
• KNN Partitioning: Incorporating the KNN Partitioning technique to partition the input space, allowing for more flexible decision boundaries and improved generalization capabilities.
• Interactive Visualization: Leveraging the Streamlit library to create an interactive web-based application, enabling users to visualize the neural network’s decision boundaries and observe the impact of KNN Partitioning.

Through this project, you will gain insights into advanced machine learning techniques, such as Evolutionary Computing and KNN Partitioning, and their application in enhancing neural network classifiers. The interactive Streamlit integration facilitates a deeper understanding of the model’s behavior and decision-making process.

Skills Utilized:

• Neural Networks
• KNN Partitioning
• Evolutionary Computing
• Machine Learning
• Streamlit
• Data Visualization
• Interactive Web Applications

This ongoing project demonstrates expertise in implementing cutting-edge machine learning techniques, exploring novel approaches to enhance neural network performance, and developing interactive visualizations to facilitate understanding and analysis.

TO DO:

• Create a README file for explaining the project.