String Values in a data frame in Pandas Suppose I have a data frame like this :
Hospital_name    State    Employees    ......
Fortis           Delhi    5000         ......
AIIMS            Delhi    1000000      ......
SuperSpeciality  Chennai  1000         ......

Now I want to use this data frame to build a machine learning model for predictive analysis. For that, I must convert the strings to float values. Also, some of these columns in Hospital_name and State contains 'NAN' values. In such a case, how should I prepare my data for building a model in Keras?
To convert from string to float in pandas (assuming you want to convert Employees and you loaded the data frame with df), you can use:
df['Employees'].apply(lambda x:float(x))

You have not given enough information about your input and expected output. So let us assume that hospital name or anything for that matter which is the input for your model is nan. You would like to remove it from the dataset because extracting features from 'nan' wouldn't make sense. Apart from that, if they are just other peripheral features, then it might be alright. In that case, if you wish to convert them into blank, then use:
df.replace(np.nan,' ', regex=True)`

Else, if you wish to remove that frame, you can check for nan using this.