UMAP — When t-SNE Hits Its Limits

1. 1Import the UMAP library in Python using pip install umap-learn
2. 2Load your dataset and preprocess it for dimensionality reduction
3. 3Apply UMAP to your data using the UMAP function with desired parameters
4. 4Visualize the reduced data using a scatter plot or other visualization tools
5. 5Compare the results with t-SNE to see the advantages of UMAP

Data scientists and analysts can benefit from using UMAP for dimensionality reduction, especially when dealing with large datasets where t-SNE is limited

💡 UMAP is a better choice than t-SNE for large datasets due to its ability to handle high-dimensional data and preserve global structure

🔍 UMAP vs t-SNE: when to use each for dimensionality reduction in Python #dataviz #machinelearning