This project applies K-means clustering to both a simple 2D dataset and the more complex MNIST dataset of handwritten digits. For the MNIST dataset, PCA (Principal Component Analysis) is utilized to reduce the dimensionality to two principal components before clustering.
- Implementation of K-means clustering algorithm.
- Use of the elbow method to determine the optimal number of clusters.
- Dimensionality reduction using PCA for the MNIST dataset.
- Data visualization for both the 2D and PCA-reduced MNIST datasets.
Ensure that you have Python installed on your system, along with the following packages:
- pandas
- matplotlib
- scikit-learn
Clone the repository and navigate to the project directory:
git clone https://github.com/UkrainianEagleOw/M2_H06.git
cd M2_H06
Run Jupyter Notebook or JupyterLab, and open the .ipynb
file:
jupyter notebook
Follow the instructions within the notebook to run the analyses.
The analysis includes an elbow plot to determine the optimal number of clusters and scatter plots to visualize the results of the K-means clustering.
This project is licensed under the terms of the MIT license.
For any queries or discussions, reach out to linkedin.com/in/dmytro-filin
.