I came across a wonderful open source project recently — Project OSRM (link) — A modern C++ routing engine for shortest paths in road networks. You can imagine it as a free version of Google Maps API, without live traffic of course. It is very valuable for my work because my current company has large shipping and logistic services. Being able to calculate the distance and directions between locations in a timely fashion will enable us to research and modeling on route optimization, leads generation, etc.
Intuition I was working with an Elasticsearch project on AWS using Python and the requests_aws4auth package worked like a charm for me. Never had any issue regarding the authentication (AWS V4 could be hard to work with sometimes). However, when I trying to create a Shiny app for my project, the problem emerged. I just couldn’t get the V4 auth to work with httr in R. I tried aws.signature package on Github but keep getting request header issues.
Motivation Well, I think it all start with one of my favorite tweets from 2013:
In Data Science, 80% of time spent prepare data, 20% of time spent complain about need for prepare data.
— Big Data Borat (@BigDataBorat) February 27, 2013
When building NLP models, pre-processing your data is extremely important. For example, different stopwords removal, stemming and lemmization might have huge impact on the accuracy of your models.