Building data products with Python
The following is a repository containing the code for a wine reviews and recommendations web application, in different stages as git tags. The idea is that you can follow the tutorials through the tags listed below, and learn the different concepts explained in them. We will use Python technologies such as Django, Pandas, or Scikit-learn. The tutorials also include instructions on how to deploy the web using a Koding account.
An OnLine Spectral Search ENgine using Python with Spark, Flask, and AngularJS
Our engine provides a RESTful-like API to perform on-line spectral search for proteomics spectral data. It is based on the SpectraST algorithm for spectral search and uses PRIDE Cluster spectral libraries. It also features an AngularJS web user interface.
World differences in infectious tuberculosis prevalence 1990-2007
In this first approach to the world situation regarding infectious tuberculosis we want to have a look at how different countries have been affected by the disease in the period from 1990 to 2007. By doing so we want to better understand different trends in the prevalence of this important disease. Which countries are getting better and worse? Are there more or less clear groups of countries based on how much are the affected and how their situation is changeing?
A web-based Sentiment Classifier using R and Shiny
The purpose of many data science projects is to end up with a model that can be used within an organisation to solve a particular problem. If this is our case, we need to determine the right representation of that model so it can be shared in the easiest, cheapest, and most effective way. Web data products are an ideal vehicle for delivering machine learning models. The Web can be accessed almost everywhere and by multiple users. Moreover, the typical web application deployment cycle allows us to do easy updates.