A look at the world wine market using Python, Pandas, and Seaborn
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.
Exploring geographical data with SparkR and ggplot2
The present analysis will make use of SparkR’s power to analyse large datasets in order to explore the 2013 American Community Survey dataset, more concretely its geographical features. For that purpose, we will aggregate data using the different tools introduced in the SparkR documentation and our series of notebooks, and then use ggplot2 mapping capabilities to put the different aggregations into a geographical context.
Linear Models with SparkR 1.5: uses and present limitations
In this analysis we will use SparkR machine learning capabilities in order to try to predict property value in relation to other variables in the 2013 American Community Survey dataset. You can also check the associated Jupyter notebook. By doing so we will show the current limitations of SparkR’s MLlib and also those of linear methods as a predictive method, no matter how much data we have.
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.
A scalable on-line movie recommender using Spark and Flask
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.
Data Science Engineering, your way
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.
Spark & Python Notebooks VI: SQL & Dataframes
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.
Spark & Python Notebooks V: Decision Trees & Model Selection
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.
Spark & Python Notebooks IV: Logistic Regression & 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.
Spark & Python Notebooks III: Statistics and EDA
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.
Spark & Python Notebooks II: key/value RDDs
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.