# Finding gender affinity for businesses

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

• 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? May 19 '20 at 12:39
• just in the dataset May 19 '20 at 12:44

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 $$Pr(Female) = \frac{females}{total}$$