This document presents an analysis of 90 days of conversational data from ExcelR to extract insights that can help improve the business. The objectives are to perform topic mining and exploratory analysis to help optimize resource allocation, modify content, and improve services. The analysis includes cleaning the data, performing exploratory data analysis, applying machine learning techniques, and providing insights. Various variables like timestamp, unread status, visitor email, country, age, and chat text are analyzed. Topic modeling using LDA identifies 4 topics: course inquiry, career transformation, assistance, and e-learning/discounts. Classification models like Naive Bayes, Logistic Regression, and Catboost are applied to the chat data tagged with time durations, achieving accuracies above 97%. The

1. ExcelR Convers
PRESENTED BY
01
SAYAN MONDAL
GUIDED BY
MR. BYOM

2. 02
Business Problem
Extracting all the actionable
insights from the chat transcript
which
will be helpful for:
• Improvement of business
• Easy Connectivity
• Reduced Human Dependency
• Effective Connectivity
• 24*7 Service

3. Objective
Topic Mining & Exploratory Analysis for
improving the Resource Allocation, Content
Modification & Service Improvement.
03
Data Set
•90 Days conversational Data
•Semi Structured Data

4. START WITH BUSINESS PROBLEM
CLEAN THE DATA
PERFORM EDA
APPLY MACHINE LEARNING TECHNIQUES
INSIGHTS
PROJECT ARCHITECTURE / PROJECT FLOW
04

5. DATA INSIGHT
FROM DIFFERENT
VARIABLE
TIMESTAMP
From the timestamp we can understand which
particular day of the week we are getting maximum call,
Which time slot we are getting that we can understand.
UNREAD
false/True : Depend upon this we also get the idea how
many people visited the website and how many of
people visited the site & how many are interested.
VISITOR EMAIL
Could be used for marketing purpose.
e.g. special occasion discount.
05

6. DATA INSIGHT
FROM DIFFERENT
VARIABLE
COUNTRY NAME/REGION/CITY
From this we could do a geographic segmentation.
AGE
If we add age as an input we could do the demographic
analysis
06

7. DATA INSIGHT
FROM DIFFERENT
VARIABLE
CHAT
From chat start and end time we get idea how the
prospect is engaged.
Which particular time chat volume are high we can
engage more executive. And give them break
alternately when chat vol. is less.
What are different things prospects are looking for
we got an idea. e.g digital marketing, deep
learning, Block chain. If any thing is not there but
The demand percentage is high we could think
about to put that in our curriculum.
If prospects going more technicalities then there
should be such kind of option shift them to a
technical person.
07

8. DATA INSIGHT
FROM DIFFERENT
VARIABLE
CHAT
we can also do sentimental analysis of the chat data
we can add one more feature to our data as rating.
after that we can say if the score is above 6, he/she
can be a potential customer who look more
interested in doing course in our institute.
08

9. DATA MERGE
We wanted to merge all the text data from all the
text file in directory.
up til now we are able to get data from all file into
one output file.
DATA PARSING
09

10. Chat Data Analysis & TopicChat Data Analysis & Topic
Modeling Using LDAModeling Using LDA
10

11. Data Pre-Processing
Converting all text into
Lowercase.
11
Removing punctuation
from text
Removing all stopwords
Lemmatization
Removing Special
Words.
eg - Id, Okay, etc

12. 12
Most Visited Visitors By Days

13. 13
Different Types Of Platforms Used Outside India

14. 14
Month Wise Visits

15. 15
No of People Actively reading Messages

16. 16
Most Visits By Region

17. 17
Platforms Used Worldwide

18. 18
Most Visitors Apart From India

19. 19
Word Cloud of Complete Corpus

20. 20
Negative Word Cloud

21. 21
Positive Word Cloud

22. [('get', 27293), ('data', 25215), ('science', 23305), ('end', 17841), ('location', 17710),
('support', 17066), ('number', 15972), ('information', 14559), ('quick', 14239), ('request',
11318), ('see', 11115), ('month', 11112), ('name', 11041), ('exploring', 11033),
('elearning', 10408), ('discount', 9830), ('exclusive', 9612), ('namecontact', 9606), ('like',
9551), ('time', 9505), ('access', 9502), ('detail', 9297), ('offer', 9151), ('enroll', 9029),
('money', 8979), ('love', 8971), ('special', 8966), ('save', 8962), ('life', 8805), ('contact',
8724), ('placement', 8620), ('region', 8333), ('project', 8284), ('please', 7989), ('city',
7772), ('group', 7772), ('clarification', 7743), ('whats', 7605), ('app', 7459), ('interview',
7260), ('doubt', 6925), ('live', 6722), ('call', 6493), ('student', 6453), ('preparation', 6444),
('25', 6316), ('forum', 6248), ('know', 5148), ('fee', 4187), ('pmp', 4148)]
22
Top 50 Frequent words

23. 23
Bigram

24. 24
Trigram

25. 25
Sentiment Analysis
Positive Negative

26. 26
Topic 1 - Course inquiry
Topic 2 - Career transformation
Topic 3 - Assistance
Topic 4 - E-learning and discount
Topic Modeling Using LDA
Model Purplexity : -5.46
coherence score : 0.59

27. 28
Unsupervised To Supervised Model
why we converted unsupervised to supervised ?
what benefits we will get from from business
prospective ?
How did we do it ?

28. 29
Chat CSV with time duration

29. Naive Bayes Classifier
30
Accuracy 97.79 %
Confusion matrix
Kappa score 0.8303

30. Logistic Regression Classifier
31
Accuracy 98.06 %
Confusion matrix
Kappa Score 0.8508

31. 0.8749
Catboost Classifier
32
Accuracy 98.35 %
Classification
report
Kappa score

32. 33
Challenges Faced and ways to improve.
unread was miss-classified
chat-bot should take user credentials before
staring the conversation.
Course fees should be mentioned according to
respective country.

33. 34
Model Deployment Using Flask

34. 27
Model Deployment Using Flask

35. 35
Thank
You