Project Overview
This project delves into the intersection of linear algebra and machine learning, specifically focusing on the k-means clustering algorithm. It highlights how linear algebra forms the foundation for clustering data points into k groups based on similarity metrics.
Key Outcomes
- Explored the role of linear algebra in clustering algorithms.
- Developed a deeper understanding of matrix operations and their application in data science.
- Analyzed real-world applications such as recommendation systems and anomaly detection.
Tools and Techniques
- Linear Algebra (eigenvectors, matrix operations)
- Python (Numpy library)
- K-Means Algorithm
Applications
- Image segmentation for identifying regions based on color similarity.
- Anomaly detection in data by identifying outliers.
- Customer segmentation in marketing based on behavioral analysis.