Project 1: Penguin Data Clustering with K-Means, Silhouette Scores, and t-SNE | Machine Learning
Welcome to Project 1 in our Machine Learning Projects series! In this video, we dive into the world of unsupervised learning by clustering penguin data using K-Means clustering, Silhouette Scores, and t-SNE.
📊 What You'll Learn:
How to preprocess and clean data for clustering
How to determine the optimal number of clusters using Silhouette Scores
How to apply K-Means clustering to identify groups in the penguin dataset
How to visualize clusters using t-SNE for better interpretation
📝 Dataset Overview:
The dataset includes measurements such as culmen length, culmen depth, flipper length, body mass, and the sex of the penguins. These data were collected and made available by Dr. Kristen Gorman and the Palmer Station, Antarctica LTER.
🔧 Tools and Libraries Used:
Python
Pandas
NumPy
Scikit-Learn
Matplotlib
Seaborn
📚 Chapters:
0:00 - Introduction
1:15 - Loading and Preprocessing Data
4:39 - what is K-Means Algorithm
5:55 - Determining Optimal Clusters with Silhouette Scores
7:35 - Applying K-Means Clustering
8:25 - Visualizing Clusters with t-SNE
9:22 - Conclusion
🔗 Useful Links:
Penguin dataset: https://github.com/mouabbi/Penguin-Cl...
Code Repository: https://github.com/mouabbi/Penguin-Cl...
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