Welcome to Task 2 of my internship at The Sparks Foundation! In this video, I dive into the Iris dataset to predict the optimal number of clusters using clustering techniques. Using R Programming Language, I explore hierarchical and K-means clustering methods and visualize the results to uncover natural groupings within the data.
🌟 Key Highlights:
Data Exploration: Understanding the Iris dataset structure and features.
Clustering Techniques: Applying hierarchical and K-means clustering algorithms.
Visualization: Creating visual representations like dendrograms and scatter plots.
Optimal Clusters: Determining the optimal number of clusters using the Elbow Method.
Evaluation: Assessing clustering performance with visual insights.
👉 Watch and learn how to apply these techniques to your data projects!
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