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Related Questions
- Can dimensionality reduction techniques help in identifying patterns and correlations between user demographics and product ratings?
- How do PCA and t-SNE differ in their ability to reduce the dimensionality of user demographic and product rating data?
- Can PCA or t-SNE be used to visualize the relationship between age and product ratings, or would this require more specific techniques like clustering?
- Would applying dimensionality reduction to user demographic and product rating data lead to a loss of valuable information or meaningful patterns?
- How can PCA or t-SNE be modified to take into account missing data points in user demographic and product rating datasets?
- Can PCA or t-SNE be applied to binary product rating data, such as thumbs up or down, or are there better suited techniques?
- How can the interpretability of PCA and t-SNE results be increased, making it easier to understand the relationship between user demographics and product ratings?
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