Student Work(Project)|PA3-4930 ML Project 3:Cluster Detection & Analysis |Clustering Algorithms|UCF

Student | Client Work(Project)|PA3-4930 ML Project 3 : Clustering Comparison: K-Means vs DBSCAN vs GMM with Python Output Video | University of Central Florida

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🎓 University: University of Central Florida (UCF), USA
💼 Program: Data Science
📚 Course: PA3-4930 – Programming Application 3: Clustering Algorithms
📂 Project: ML Project 3 – Comparative Analysis of Clustering Techniques

This reel showcases the results of my third ML assignment focused on unsupervised learning and clustering. I evaluated the performance of:

✔️ K-Means Clustering
✔️ DBSCAN (Density-Based Spatial Clustering)
✔️ Gaussian Mixture Models (GMM)

📌 Evaluation Techniques Used:

Silhouette Score
Davies–Bouldin Index
Elbow Method for K selection
PCA for dimensionality reduction
Outlier detection via DBSCAN

🧠 Clustering insights revealed different model behaviors on various datasets, particularly in handling noise and overlapping clusters.

🛠️ Tools Used: Python, scikit-learn, NumPy, Seaborn, Matplotlib

💬 Drop a comment if you want a code walkthrough or visualization breakdown!

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