In this project, I have applied the k-means clustering unsupervised learning algorithm using scikit-learn and Python to build an image compression application with interactive controls.
Steps involved:
- Pre-processing high-resolution image data for k-means clustering
- Conducting basic exploratory data analysis (EDA)
- Data visualization
Utilized time-efficient implementation of the k-means algorithm, Mini-Batch K-Means, to compress images and leveraged 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.