Capstone Final Presentation

1. PYTHON VS R
BY: KENNAN DUFFY, DARIA GBOR, CHRIS LUKENS,
JOHN SAVIELLO, & JAMES SCHEUREN
http://project.mis.temple.edu/pythonvsranalytics/final-deliverables/

2. AGENDA
1. Our Process
2. Use Cases
3. Sentiment Analysis - Python
4. Sentiment Analysis - R
5. Scorecard
6. Recommendation
7. Q & A
2

3. OUR PROCESS
3
RESEARCH
Conduct web research on the use
case and the language
PYTHON
Complete the use case in Python
ANALYZE
Review and analyze the results of
Python & R as a team
R CODE
Complete the use case in R
DEFINE
Define the business purpose of the
use case and completion plan
SCORE
Fill out the scorecard based on previously
defined scoring criteria

4. SCORECARD
4
Criteria Weight (%)
Package Requirement 10%
Lines of Code 5%
Simplicity 10%
Popularity 5%
Development Sources 10%
Data Visualization 15%
Functionality 45%
Total 100%

5. USE CASE #1 - PREDICTIVE ANALYTICS
What
➔ NFL franchise wants to ensure that the player they are selecting
from the draft will be a high performer
How
➔ Linear Regression using the NFL combine dataset from 1985-2015

6. USE CASE #2 - TEXT MINING
What
➔ Justin Trudeau’s campaign team wants to stay updated on
what the public opinion is on him
How
➔ Sentiment analysis using Twitter feed as our dataset

7. USE CASE #3 - IMAGE ANALYTICS
What
➔ England wants to keep track of what is going on in the
busy streets for security purposes
How
➔ Object detection using a picture of a busy street in England

8. SENTIMENT ANALYSIS - PYTHON
8
csv
Allows us to write output to
csv file for analysis
Tweepy
Python library that allows
access twitter API and use
different functions
TextBlob
Natural language processor
to get subjectivity and
polarity of tweets
01
03 02

9. DEMONSTRATION - PYTHON
9

10. PERFORMANCE - PYTHON
10
Overall Accuracy: 28%
▰ Negative Accuracy: 52% (11/21)
▰ Positive Accuracy: 27% (7/26)
▰ Neutral Accuracy: 19% (10/53)

11. SENTIMENT ANALYSIS - R
11
04
03
02
01Syuzhet
Sentiment Analysis
TwitteR
Twitter API
Snowball C
Concision
TM
Text Mining

12. DEMONSTRATION - R
12

13. PERFORMANCE - R
13
Overall Accuracy: 50%
▰ Negative Accuracy: 77% (30/39)
▰ Positive Accuracy: 27% (9/33)
▰ Neutral Accuracy: 39% (11/28)

14. SCORECARD
14

15. Our Recommendation
15
- Built for Data Analytics
- Package Accuracy
- Usability

16. 16
THANK YOU!
Any questions?

17. APPENDIX

18. GRADING CRITERIA
1. Package Requirement:
0 packages = 10 points
1 package = 9 points
2 packages = 8 points
3 packages = 7 points
4 packages = 6 points
5 packages = 5 points
6 packages = 4 points
7 packages = 3 points
8 packages = 2 points
9 packages = 1 point
10 packages = 0 points
3. Simplicity:
Quick, really simple to write, really simple to read = 10
Took a while to complete, but pretty simple, easy to understand = 7
Took so long to complete, not very simple, hard to understand = 4
Hard to write, almost impossible, not able to read = 1
4. Popularity:
Very Popular among the industry = 10
A lot of people use this language = 7
Some people use this language = 4
No one uses it = 1
5. Development Sources:
A lot of help in the online community = 10
Some resources available, decently helpful sources = 7
Not many resources available = 4
No help available online = 1
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6. Data Visualization
Easy to manipulate, cleanliness, visually appealing = 10
Harder to manipulate, messy, not exciting = 7
Harder to manipulate, difficult to read = 4
Unable to manipulate, unreadable = 1
7. Functionality
Accurate data, does everything it needs to do = 10
Mostly accurate data, does most of what it needs to do = 7
Inaccurate data, barely does what it needs to do = 4
Is not able to complete the task = 1
2. Lines of Code:
0-10 lines = 10 points
11-20 = 9 points
21-30 = 8 points
31-40 = 7 points
41-50 = 6 points
51-60 = 5 points
61-70 = 4 points
71-80 = 3 points
81-90 = 2 points
91-100 = 1 point
101 + = 0 points

19. 19
USE CASE 1 - Python

20. 20
Use CASE 1 - R

21. USE CASE 3 – IMAGE ANALYTICS
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