In this article we want to have a look at present wine market prices by region and appellation from the point of view of the Wine.com website catalog. We will use Python-based libraries such as Pandas and Seaborn.

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.

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.

This Apache Spark tutorial will guide you step-by-step into how to use the MovieLens dataset to build a movie recommendations web service using collaborative filtering with Spark’s Alternating Least Saqures implementation and Python/Flask.

Today we just made public a series of tutorials on Data Science Engineering. In them we will try to compare how different concepts in the discipline can be implemented in the two dominant ecosystems nowadays: R and Python.

The fifth episode in our Spark series introduced Decision Trees with MLlib. This new notebook moves away from MLlib for a while in order to introduce SparkSQL and the concept of Dataframe, that will speed up our analysis and make it easier to communicate.

The fourth episode in our Spark series introduced Logistic Regression with MLlib. This new notebook explains how to use the library to build a classifier using Decision Trees on a large dataset. It also shows how powerful trees are in order to understand our data and even perform model selection.

The third episode in our Spark series introduced the MLlib library and its Statistics and Exploratory Data Analysis capabilities. This fourth notebook explains how to use the library to build a classifier using Logistic Regression on a large dataset. It also describes two different approaches to model selection.

Episodes one and two in our Spark series introduced how to work with RDDs. The third one introduces Spark’s MLlib library for machine learning, starting with its Statistics and Exploratory Data Analysis capabilities.

Previously, we introduced the basics of working with Spark RDDs in Python. In this new notebook, we deal with data aggregations and key/value pair RDDs.

Previously we discussed tf-idf as a way to calculate how relevant a search term is given a set of indexed documents. When having multiple terms, we used overlap score measure consisting in the sum of the tf-idf for each term in the given input. A more general and flexible way of scoring multi-term searches is using the vector space model.

Let us expose here a basic and beautiful Information Retrieval concept such as tf-idf. In order to do so, we will use Python to define a basic in-memory “search engine” that will allow us to add documents and search for them. The search results will contain the relevant documents together with the tf-idf value.