machine learning in Python "We use scikit-learn to support leading-edge basic research [...]" "I think it's the most well-designed ML package I've seen so far." "scikit-learn's ease-of-use, performance and overall variety of algorithms implemented has proved invaluable [...]." "For these tasks, we relied on the excellent scikit-learn package for Python." "The great benefit of scikit-learn is its fast learning curve [...]" "It allows us to do AWesome stuff we would not otherwise accomplish" "scikit-learn makes doing advanced analysis in Python accessible to anyone." Healthcare Reform May Not Improve Medical Bills A healthcare reform study in Massachusetts reported more people covered under insurance did not improve medical debts for them. Tuesday the American Journal of Medicine published these findings which looked at Massachusetts healthcare reform,modeled after President Barack Obamas national plan that was passed last year. Advocators for the national healthcare reform claimed it would reduce medical bankruptcy. "These data suggest that reducing medical bankruptcy rates in the United States will require substantially improved -- not just expanded -- insurance," authors wrote. Researchers took a random sample of Massachusetts bankruptcy filers in July 2009 and sent surveys to 500 households. The Massachusetts healthcare reform was implemented in 2008,so they compared their data to 2007 information. Medical bills were still 52.9 percent of all bankruptcies in the state, although the percent was slightly down. "We need to reduce limits on deductibles and out-of-pocket costs," said Dr.
The R Project for Statistical Computing Getting Started with RapidMiner Studio - RapidMiner Documentation So here you are, a business analyst or developer or jack-of-all-trades. Things are going well, but it's time to fine-tune your business. You've probably collected a myriad of data points about your customer base and would like to determine which customers will remain loyal and which ones will likely churn. Attracting new customers comes at a price, so you'd really like to maintain your current customer base. How do you predict who may leave? What target audiences do you spend marketing dollars on to entice them to stay? Using RapidMiner's modern enterprise platform, you can quickly and easily create analytic workflows called processes to determine who to target. This series of five Getting Started tutorials will help familiarize you with some basic features and functionality of RapidMiner Studio. Below are some additional resources available to help you get up and running quickly with RapidMiner Studio.
The microhydro plant My little paradise has a stream that provides enough water flow and head to run a small turbine, to provide electricity to my home. While writing this, the microhydro plant is being implemented, and here are some photos of the process. Since I usually like to start at the end, the first thing I built is the controller: It is an implementation of Jan Portegijs' "Humming Bird", with some changes and adaptations. The largest cost of the plant, by far, is in the piping for the rather long penstock. When the purchase was made and the truck arrived, we unloaded the pipes at different places, to get them as close to the installation area as possible. A smaller number of pipes were stored closer to the turbine site. Only for the last part of the run, where the pressure exceeds 2 bar, I will use blue class 4 PVC pipe. To change from the low slope run of the white pipe to the much steeper run of the blue one, a change of direction is required. The flat area is where the forebay will be built. News!
COC131 Data Mining, Tuotorials Weka "The overall goal of our project is to build a state-of-the-art facility for developing machine learning (ML) techniques and to apply them to real-world data mining problems. Our team has incorporated several standard ML techniques into a software "workbench" called WEKA, for Waikato Environment for Knowledge Analysis. Tutorial 01 (13/02/09) Get the old faithful data-set (.csv) here Get the tutorial 01 exercises here Get the tutorial 01 solutions here Statistics revision for Tutorial 01 here Tutorial 02 (20/02/09) Get the iris data-set (.arff) here Get the tutorial 02 exercises here Tutorial 03 (27/02/09) Get the tutorial 03 exercises here Tutorial 04 (06/03/09) Tutorial 03 exercises and clarification of any issues from earlier tutorials Tutorial 05 (13/03/09) Get the tutorial 04 exercises here Tutorial 06 (20/03/09) Get the flags data-set (.arff) here Get the whole euro data-set (.arff) here Get the tutorial 05 exercises here Tutorial 07 (27/03/09) Tutorial 08 (24/04/09) Coursework
Home - SCaVis Freedom to choose a programming language. Freedom to choose an operating system. Freedom to share your code. Supported programming languages SCaVis can be used with several scripting languages for the Java platform, such as BeanShell, Jython (the Python programming language), Groovy and JRuby (Ruby programming language). Supported platforms SCaVis runs on Windows, Linux, Mac and Android operating systems. SCaVis is a successor of the popular jHepWork package which has been under intensive development since 2005.
Vowpal Wabbit (Fast Learning) Vowpal Wabbit (Fast Learning) This is a project started at Yahoo! Research and continuing at Microsoft Research to design a fast, scalable, useful learning algorithm. VW is the essence of speed in machine learning, able to learn from terafeature datasets with ease. Via parallel learning, it can exceed the throughput of any single machine network interface when doing linear learning, a first amongst learning algorithms. We primarily use the wiki off github. DownloadCommand lineTutorialExamplesInput ValidatorDiscussionsMailing list Octave GNU Octave is a high-level interpreted language, primarily intended for numerical computations. It provides capabilities for the numerical solution of linear and nonlinear problems, and for performing other numerical experiments. It also provides extensive graphics capabilities for data visualization and manipulation. Octave is distributed under the terms of the GNU General Public License. Version 4.0.0 has been released and is now available for download. An official Windows binary installer is also available from A list of important user-visible changes is availble at by selecting the Release Notes item in the News menu of the GUI, or by typing news at the Octave command prompt. Thanks to the many people who contributed to this release!
GGobi data visualization system. Tutorial · JohnLangford/vowpal_wabbit Wiki We did a new version 7.8 tutorial which includes: New tutorials associated with Version 7.4. This includes: A new version 7.0 tutorial is available. It covers the basics and most common options, how to use VW and the data format for different types of problems, such as Binary Classification, Regression, Multiclass Classification, Cost-Sensitive Multiclass Classification, "Offline" Contextual Bandit and Sequence Predictions. Many more advanced options in terms of flags and the data format are not covered. The version 6.1 tutorial and various pieces below covers some topics not covered in the version 7 tutorial, as most of these haven't change in the latest version: Older stuff The version 5.1 tutorial with a video. Version 5.0 Videolecture. The main piece (v5.0)The importance weight invariant update rule. A Step by step introduction The first step is downloading a version of VW. git clone Now we compile: cd vowpal_wabbit . Note: make vw make test