<p>I don't think this classify as an NLP problem, there is almost no semantic analysis needed, it is more like a classification problem using categorical features. </p>

<p>NLTK is surely valuable if you want to perform some text 'cleaning' or preprocessing before encoding the variables. The only NLP application that I think you could apply here is some sentiment analysis on the comments to extract extra features (like a number expressing the negativeness or positiveness of each comment). Nevertheless you might want to do that using some pre-trained models cause your dataset is pretty small. </p>

CC BY-SA 4.0
2020-03-18T06:13:26.447Z
Hello Eodardo,  Thanks for your reply, I was able to do this in python using dataframe itself.  I am currently adding some more enhancements to it.  I will post the completed code sometime soon.
Mani
CC BY-SA 4.0
2020-03-13T02:08:26.097Z
2020-03-13T02:08:26.097Z
Edoardo Guerriero
<p>I am newbie to NLP.
I have a excel sheet with following columns: 
    Server_SNo, 
    Owner, 
    Hosting Dept, 
    Bus owner, 
    Applications hosted, 
    Functionality,
    comments</p>

<p>a. Except the Server_SNo, other columns may or may not have data.<br>
b. For some records there is no data except Server_SNo which is the first column.
c. One business owner can own more than 1 Server.</p>

<p>So, out of 4000 records, about 50% of data contain direct mapping for a server with owner.  Remaining 50% of data have combination of other columns (Owner, Hosting Dept, Bus owner, Applications hosted, Functionality and comments)</p>

<p>Here is my problem, I need to find the owner for the given Server_Sno for 50% of data which have combination of other columns (Owner, Hosting Dept, Bus owner, Applications hosted, Functionality and comments).</p>

<p>I have just started to build the code using Python and NLTK.</p>

<p>Is this an NLP problem? Am I going in right direction using Python and NLTK for NLP?</p>

<p>Any insights is appreciated.</p>

<p>-Mani</p>

<machine-learning><natural-language-processing><python>
Owner Search for given Server SNO
Mani
# Owner Search for given Server SNO

(Asked by: username_0 on 2020-03-10)

I am newbie to NLP.
I have a excel sheet with following columns:
Server\_SNo,
Owner,
Hosting Dept,
Bus owner,
Applications hosted,
Functionality,
comments

a. Except the Server\_SNo, other columns may or may not have data.  
b. For some records there is no data except Server\_SNo which is the first column.
c. One business owner can own more than 1 Server.

So, out of 4000 records, about 50% of data contain direct mapping for a server with owner. Remaining 50% of data have combination of other columns (Owner, Hosting Dept, Bus owner, Applications hosted, Functionality and comments)

Here is my problem, I need to find the owner for the given Server\_Sno for 50% of data which have combination of other columns (Owner, Hosting Dept, Bus owner, Applications hosted, Functionality and comments).

I have just started to build the code using Python and NLTK.

Is this an NLP problem? Am I going in right direction using Python and NLTK for NLP?

Any insights is appreciated.

-username_0

**Tags:** machine-learning, natural-language-processing, python


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## 1 answers

### Answer by username_1 on 2020-03-13. Score: 1

I don't think this classify as an NLP problem, there is almost no semantic analysis needed, it is more like a classification problem using categorical features.

NLTK is surely valuable if you want to perform some text 'cleaning' or preprocessing before encoding the variables. The only NLP application that I think you could apply here is some sentiment analysis on the comments to extract extra features (like a number expressing the negativeness or positiveness of each comment). Nevertheless you might want to do that using some pre-trained models cause your dataset is pretty small.

> **Comments:**
> **username_0**: Hello Eodardo,  Thanks for your reply, I was able to do this in python using dataframe itself.  I am currently adding some more enhancements to it.  I will post the completed code sometime soon.  

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