This is part 2 of 2 about using Elasticsearch and Kibana to track patterns in baby activity. Part 1 here covers the hardware and setup for tracking baby kicks.

Machine learning

Having collected about 6 weeks of baby kicking data, it’s time to test the new toy in the Elasticsearch stack: Machine learning.

Installing this was a straightforward case of following the instructions, and from the ‘Machine Learning’ new menu item in Kibana, I chose ‘Create new job’, and ‘Create a single metric job’:

This is part 1 of 2 about using Elasticsearch and Kibana to track patterns in baby activity.

Part 2 is here.

Kicking things off

According to countthekicks.org “Counting baby kicks is important because changes in your baby’s movement pattern may indicate potential problems with your pregnancy”. Counting and patterns sounds like a technological problem for Elasticsearch and Kibana!

Sentiment analysis, Elasticsearch and Kibana

The idea

From the Twitter streaming API grab tweets for a live TV show whilst it’s showing, classify by contestant, analyze sentiment and provide a real-time dashboard into the outlook.

Try to predict good bets on the winners. Optional: Gamble. :-)

The tools

• Grabbing the data: Scala and the twitter4j Java library
• Sentiment analysis: SentiStrength
• Search engine: Elasticsearch
• Dashboard: Kibana (also from the Elasticsearch guys)

The show

I picked the final of the BBC’s “Strictly Come Dancing” in the UK. My wife is an avid viewer, myself less, so I’m generally consigned to the laptop whilst it’s on. This was a welcome distraction.