In this project, I have used K-Means clustering unsupervised learning algorithm using scikit-learn and Python to build an image compression application with interactive controls.
The project consists of the following steps:
- Pre-processing high-resolution image data for k-means clustering.
- Conducting basic exploratory data analysis (EDA) and data visualization.
- Applying a computationally time-efficient implementation of the k-means algorithm, Mini-Batch K-Means, to compress images.
- Leveraging the Jupyter widgets library to build interactive GUI components to select images from a drop-down list and pick values of k using a slider.
Please have a look at the notebook here, or open it on Colab.