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Goal: Create a tool that recommends similar players based on their statistical profile

Process: (1) Standardize data (2) UMAP to reduce dimensionality (c. 50 features) (3) First-Stage Clustering: GMM to create macro clusters of players (4) Second-Stage Clustering: GMM to create micro clusters of each macro cluster with different features based on their position (e.g. only 10/50 that are relevant) (5) Calculate Euclidean Distance using PCA (UMAP led to weird results)

Question: How good/reasoanble is this approach on a scale from 1-10 (10=best)? Are there any downsides to my approach that I'm not considering?

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  • $\begingroup$ I am not sure about your approach, since we can attack each ML problem in many ways. All we need to care about is the accuracy of the model. One thing i could see is your model looks little complex. You are going to do recommendation, possible try this. 1. Standardize the data. 2. Fit the data using NearestNeigbor with Cosine Similarity 3. Pick a new/random data and hit the model you fit with some k value to get the similar players. $\endgroup$ Apr 23 at 19:55

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