Wikimedia Data Challenge

Introduction

This Project was put out as data challenge by the Wikimedia Foundation for a data analyst position. I thought it would be a great opportunity to brush up on some data analysis tools in Pandas and decided to complete the challenge.

The goal of the project was to analyze data from event logging (EL) to track a variety of performance and usage metrics to help the company make decisions. Specifically, they were interested in:

• clickthrough rate: the proportion of search sessions where the user clicked on one of the results displayed
• zero results rate: the proportion of searches that yielded 0 results

EL uses JavaScript to asynchronously send messages (events) to their servers when the user has performed specific actions.

Data

The dataset comes from a tracking schema that the Wikimedia Foundation uses for assessing user satisfaction. Desktop users are randomly sampled to be anonymously tracked by this schema which uses a "I'm alive" pinging system that we can use to estimate how long our users stay on the pages they visit. The dataset contains just a little more than a week of EL data.

Column	Value	Description
uuid	string	Universally unique identifier (UUID) for backend event handling.
timestamp	integer	The date and time (UTC) of the event, formatted as YYYYMMDDhhmmss.
session_id	string	A unique ID identifying individual sessions.
group	string	A label ("a" or "b").
action	string	Identifies in which the event was created. See below.
checkin	integer	How many seconds the page has been open for.
page_id	string	A unique identifier for correlating page visits and check-ins.
n_results	integer	Number of hits returned to the user. Only shown for searchResultPage events.
result_position	integer	The position of the visited page's link on the search engine results page (SERP).

The following are possible values for an event's action field:

• searchResultPage: when a new search is performed and the user is shown a SERP.
• visitPage: when the user clicks a link in the results.
• checkin: when the user has remained on the page for a pre-specified amount of time.

Example Session

uuid	timestamp	session_id	group	action	checkin	page_id	n_results	result_position
4f699f344515554a9371fe4ecb5b9ebc	20160305195246	001e61b5477f5efc	b	searchResultPage	NA	1b341d0ab80eb77e	7	NA
759d1dc9966353c2a36846a61125f286	20160305195302	001e61b5477f5efc	b	visitPage	NA	5a6a1f75124cbf03	NA	1
77efd5a00a5053c4a713fbe5a48dbac4	20160305195312	001e61b5477f5efc	b	checkin	10	5a6a1f75124cbf03	NA	1
42420284ad895ec4bcb1f000b949dd5e	20160305195322	001e61b5477f5efc	b	checkin	20	5a6a1f75124cbf03	NA	1
8ffd82c27a355a56882b5860993bd308	20160305195332	001e61b5477f5efc	b	checkin	30	5a6a1f75124cbf03	NA	1
2988d11968b25b29add3a851bec2fe02	20160305195342	001e61b5477f5efc	b	checkin	40	5a6a1f75124cbf03	NA	1

This user's search query returned 7 results, they clicked on the first result, and stayed on the page between 40 and 50 seconds. (The next check-in would have happened at 50s.)

Objectives

The objectives of this project are to find answers to the following questions:

1. What is their daily overall clickthrough rate? How does it vary between the groups?

2. Which results do people tend to try first? How does it change day-to-day?

3. What is their daily overall zero results rate? How does it vary between the groups?

4. Let session length be approximately the time between the first event and the last event in a session. Choose a variable from the dataset and describe its relationship to session length. Visualize the relationship.

Requirements

1. Python (3.x
2. Jupyter Notebook
3. NumPy
4. matplotlib
5. Pandas

To install the requirements with pip (except for Python), type in the main directory:

pip install -r requirements.txt

Or you can install the dependencies and access the notebook using Docker by building the Docker image with the following:

docker built -t wikimedia .

Followed by running the command container:

docker run -p 8888:8888 -t wikimedia