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How to group products that are similar and bought together over a short time window?

Why not use dimensional reduction algorithms? UMAP or t-SNE are quite simple to implement, they are non-linear (contrary to PCA) and they make meaningful clusters. Then, you can apply a KMeans. Here ...
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Given a set of options where one option is selected prior to an outcome, how to model optimal selection that will increase likelihood of (+) outcome

The first step is calculating descriptive statistics. Starting with survival rate by group which can be ordered from highest rate to lowest rate. The logical follow-up question is - Are the group ...
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Handling IP addresses as features when creating machine learning model

Did you have a look at IP2Vec? Probably have some limitations when new IP addresses appear frequently, but maybe it is worth a try.
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2 votes

How can a log transformation decrease performance?

If the target is skewed, you could try oversampling, under sampling or SMOTE (synthetic minority oversampling technique) Since 75% of data is 0 you could bin them into two groups ones that are equal ...
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2 votes

How can a log transformation decrease performance?

Most of the online discussions, that I looked up, seemed to talk about and recommend log-transformation of the target variable for better results for tree based regression algorithms. It's indeed ...
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  • 149
2 votes

Method of calculating sample size for a machine learning model

As Noe mentioned, no. But you can study how much data is needed to certain algorithm converge. For example assuming that one has 10 iid normal distributed IV and the DV is a linear combination of them,...
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  • 111
2 votes

Method of calculating sample size for a machine learning model

In general? No. Take into account that the amount of data need for an ML model to achieve a certain level of performance in a task is totally dependent on the task, the specific dataset, and the model ...
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What is the best machine learning technique to fuse two spatial data sets?

In terms of navigation, one of the most reliable algorithms is the Kalman Filter because it predicts directions according to previous points. In your case, if you have 2 points measurement at each ...
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