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Python vs R for Data Analytics Final
- 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
- 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
- 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
- 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 18 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 21
Editor's Notes
- Lines of code - we set up standard criteria for this measurement so if it was between 1-10 lines it got a 10, 11-20 lines it got a 9, and so forth Development sources - how strong is the online support community, how many helpful sources are out there for us to help us complete the use case and problem solve if issues arise Functionality - is it able to do what we want it to & how well is it able to accompish that
- TEXTBLOB struggled to identify positive/neutral tweets Explain how we got accuracy - retrieved 100 tweets and compared them (as a team) to the package results and see if we agreed with the outcome Neutral = 10/53 Negative = 11/21 Positive = 7/26
- Syuzhet- Used for sentiment analysis - what is reading the tweets T M - Works with Snowball C and TwitteR to mine text TwitteR - Interacts with Twitter API to get tweet for analysis Snowball C - Makes words more concise so that they are easier for other packages to read Explain how we got accuracy - retrieved 100 tweets and compared them (as a team) to the package results and see if we agreed with the outcome
- Load packages, Import Twitter API, Scrape, Cleaning, Analyze, Apply
- 50% overall. 77% negative (30/39). 27% positive (9/33). 39% neutral (11/28). Accuracy MENTION: Functionality USE FOR LESSONS LEARNED Found out that it is more accurate with negative tweets Not perfect, picks out certain words to decide whether it is positive or negative. Sarcasm is difficult. Shouldn’t trust positivity tweet analyses
- Language is built for predictive analytics, ready to run predictive analytics where as python needs to be molded into running the linear regression The packages we ran for R were much more accurate than the Python packages for running sentiment analysis More functionality available when running image analytics than Python and very simple to change, it was a matter of changing only 2 lines of code to switch between face detection, landmark detection, logo detection, object detection