Student Work(Assignment )|PA3-4930 ML Assignment : Unsupervised Learning Models Compared Project|UCF

Student | Client Work(Assignment )|PA3-4930 ML Assignment : Programming Application 3: From Raw Data to Cluster Labels | Clustering Algorithms in Python Output Video | University of Central Florida

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🎓 University: University of Central Florida (UCF), USA
📚 Course: PA3-4930 – Programming Application 3: Clustering Algorithms
📂 Project: Machine Learning Project 3 – Clustering Comparison
💻 Program: Data Science

This video presents my final clustering project where I explored and compared K-Means, DBSCAN, and Gaussian Mixture Models (GMM) for pattern discovery in unlabeled datasets.

📌 What’s Included in the Project:

Cluster separation using Euclidean distance
Visualization using PCA and 2D scatter plots
Evaluation using Silhouette Score, Davies-Bouldin Index, and Elbow Method
Parameter tuning for eps, min_samples, and k
Noise and outlier detection with DBSCAN
Probabilistic soft clustering via GMM

🛠️ Tools Used:

Python
scikit-learn, matplotlib, numpy, seaborn

📢 Comment below if you'd like the full code or a walk-through on clustering evaluation metrics!

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