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Thank you for your kind words

I don't understand your question very well. But it seems you have a dataset with 1000 samples (or more) and 79 features. If so, then many clustering techniques can be applied to find clusters of 1000+ samples. From very simple clustering techniques like the k-means algorithm to hierarchical clustering can be used. EM based clustering methods can also be used. Recenly, many clustering methods are developed in the field of single-cell analysis such as FEATS (pubmed.ncbi.nlm.nih.gov/33285568/) SC3, Seurat etc. These techniques can be used as well. If you know the number of clusters then your clustering problem would be easier otherwise some techniques like FEATS, Seurat etc also can find the best number of clusters.

Thanks for your query. I will try to give an illustration that may help you understand the difference between classification and prediction. Classification is a process when you want to identify between two or more objects, putting 'apples' and 'oranges' in two distinct baskets; i.e., separating objects into separate categories. This is a classification task. However, prediction can happen just with one object. For e.g. you want to predict what is the weather tomorrow (forecasting). Basically, based on the information at hand, you want to estimate an object, event, disease/pathogens and so on.

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