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What are the different models I can use to find the gender affinity of businesses using yelp dataset-- https://www.kaggle.com/yelp-dataset/yelp-dataset . I need to find Probablity (Male buying from a merchant) and Probablity (Female buying from a merchant) where both probablities add to 1 . There is no information about gender so I can use the Genderize api to find gender using names of users. This is an unsupervised problem

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  • $\begingroup$ Do you just want to find the gender affinity of the businesses in the dataset? Or are you hoping to build a model to predict gender affinity for new businesses? $\endgroup$ – zachdj May 19 at 12:39
  • $\begingroup$ just in the dataset $\endgroup$ – Math_Zombie May 19 at 12:44
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Since you're only interested in the gender affinity of businesses in your dataset, no model is needed. You can compute gender affinity directly: For each business, find the total number of customers, the total number of male customers, and the total number of female customers. Then $Pr(Male) = \frac{males}{total}$ and $Pr(Female) = \frac{females}{total}$

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