How to Deal with Big Data of WikiPedia Dump(found at https://dumps.wikimedia.org/backup-index.html)? Hadoop , good option but could not run the Human name recognition algorithm written in python via Hadoop Streaming API. So did what hadoop does - convert the big data file into chuncks of data(extra care while diving since data is in XML format) using splitXML.py And runing the algorithm on each data chunck and integrating the results

How the extraction works:

• The code uses regex and find to get elements related to people from the page.
• If element is found then people tag is updated to true
• If the people tag is true then we extract the title to a file.

How the Visualization works: (We have plotted using sample data)

• The code gets value from the file as a tuple.
• The common names are then grouped.
• The code uses matplotlib library to plot the data.

Reference for visualization: https://github.com/norvig/pytudes/blob/master/ipynb/xkcd-Name-Dominoes.ipynb Precision Recall and F-Measure: We took a sample of 500 records to calculate precision: Precision : 493/500 = 0.986 Recall: Step 1: No of names/No of titles = Step 2: Total titles in file (including redirect pages) = 12162695 Total names in file = 1014959 Expected Names in file = 2432539 Recall = 0.4172

[Wikipedia pages about living people : ~15% (Wikipedia living people pages: https://en.wikipedia.org/wiki/Category:Living_people) (Wikipedia total pages: https://en.wikipedia.org/wiki/Wikipedia:Size_of_Wikipedia) Roughly 20-25% of Wikipedia articles are related to people (https://www.quora.com/How-many-people-have-a-Wikipedia-page-about-them-regardless-if-they-are-living-or-dead) ] F-measure: 2PR/(P+R) = 0.8177/1.3972 = 0.5852