This resource is designed primarily for beginning data scientists or analysts who are interested in identifying and applying machine learning algorithms to address the problems of their interest.
This is a simple post to demonstrate how python can be used to do preliminary data analysis. I am using Python version 3.5.1 coming with Anaconda Python version 4.0 64 bit and my operating system is windows. To start with you can download Anaconda Python and install it in your machine. Once the installation is over, open the Anaconda Prompt which will appear in the start menu if you have installed in windows machine.
In this special guest feature, Ty Tucker, CEO of REV, discusses predictive and prescriptive analytics, and how they can be used in tandem to achieve the best results for a business. Ty provides unmatched business services to a wide variety of clients. With experience in growing organizations from two employees to 340,000, he believes that blending active management, business intelligence and advanced analytics is the key to increased productivity.
Dstl’s Satellite Imagery competition, which ran on Kaggle from December 2016 to March 2017, challenged Kagglers to identify and label significant features like waterways, buildings, and vehicles from multi-spectral overhead imagery. In this interview, first place winner Kyle Lee gives a detailed overview of his approach in this image segmentation competition. Patience and persistence were key as he developed unique processing techniques, sampling strategies, and UNET architectures for the different classes.
In our industry, much focus is placed on developing analytical models to answer key business questions and predict customer behavior. However, what happens when data scientists are done developing their model and need to deploy it so that it can be used by the larger organization? Deploying a model without a rigorous process in place has consequences—take a look at the following example in financial services.
In this tutorial I’ll explain how to build a simple working Recurrent Neural Network in TensorFlow. This is the first in a series of seven parts where various aspects and techniques of building Recurrent Neural Networks in TensorFlow are covered. A short introduction to TensorFlow is available here. For now, let’s get started with the RNN!
In the second part of our series we will build another small shiny app but use another UI. More specifically we will present the example of a UI with a plot at the top and columns at the bottom that contain the inputs that drive the plot. For our case we are going to use the diamonds dataset to create a Diamonds Analyzer App.
I guess we all use it, the good old histogram. One of the first things we are taught in Introduction to Statistics and routinely applied whenever coming across a new continuous variable. However, it easily gets messed up by outliers. Putting most of the data into a single bin or a few bins, and scattering the outliers barely visible over the x-axis. This distribution might look familiar …
With roots dating back to at least 1662 when John Graunt, a London merchant, published an extensive set of inferences based on mortality records, Survival Analysis is one of the oldest subfields of Statistics . Basic life-table methods, including techniques for dealing with censored data, were known before 1700 . In the early eighteenth century, the old masters, de Moivre working on annuities and Daniel Bernoulli studying competing risks for his work on smallpox inoculation, developed the foundations of time-to-event modeling . Today, survival analysis models are important in Engineering, Insurance, Marketing and Medicine and many more application areas. So, it is not surprising that the R Task View on Survival Analysis, a curated, organized and annotated list of relevant R packages and functions, is formidable.