Combining text (NLP), numeric and categorical data for a regression problem I have a dataset 

data = { 
    points: 3.765, 
    review: `Food was great, staff was friendly`, 
    country: 'Chile', 
    designation: 'random', 
    age: 20

}


I am looking for a way to use these features to build a model to predict points.

Description seems to hold alot of information about points.

How do i feed this data into the model and also which model?

Note I dont want to use word2vec (embeddings)
To predict the points variable, we have four features namely review, country, designation and age. 

For the review feature,

You can use Doc2Vec to transform the review into a fixed size vector. Similar reviews will lie in a close vicinity.

For the country and designation features,

Assuming these variables have finite values, so you can treat the variable as categorical. We can use one-hot encoding.

For the age feature,

You can treat this variable as numeric.