KNN and Kmeans Algorithms on MNIST in C++ from Scratch

This project demonstrates the implementation of the K Nearest Neighbors (KNN) and K-means algorithms from scratch in C++. It was developed as part of the second-semester coursework for the Object-Oriented Programming subject. This was made to have a lower-level understanding of how these machine learning algorithms work and to appreciate how elegantly pandas handles data and how much we should appreciate the library. This is a pandas appreciation repository :D

Project Features

• Implementation of the K Nearest Neighbors algorithm
• Implementation of the K-means clustering algorithm
• Support for custom distance metrics in KNN
• Ability to specify the number of clusters in K-means
• Interactive command-line interface for user interaction
• Efficient data structures and algorithms for optimal performance

Usage

To use this project, follow these steps:

1. Clone the repository: git clone https://github.com/ahmedHanzala/knn-kmeans-mnist-cpp-implementation.git
2. Compile the source code using a C++ compiler
3. Run the executable file
4. Follow the on-screen instructions to interact with the KNN and K-means algorithms

If you want any help regarding this project or have any ideas feel free to contact me :D