I am currently working on an use case where feature set contains numeric values such as amount, as well as a review feature which contains long winded english text. the english text will very well differ between train and test data. eg 'i have seen and its good' , 'nto ok','timepass',etc
how do i combine the text feature set with numerical data and feed it to a machine learning model?
i will nt be able to use encoding , these text variables are not categorical values . they are varying .
import pandas as panda
from sklearn.feature_extraction.text import TfidfVectorizer
words = ['i hv paid','i dont like','its good','yum yum']
a = panda.DataFrame({'amount':[10,20,30,40],'word':words})
tf = TfidfVectorizer()
csr = tf.fit_transform(words)
#how do i now use my csr to feed both amount and word to my machine learning model