The name, Humalytica, was intended to demonstrate a blend of Analytics and the Human Mind. As data science became a popular field, I began seeing people relying on software and black-box algorithms entirely too much. Analysts were picking their favorite tool or algorithm and forcing their data into it. For instance, an analyst might have learned and liked Artificial Neural Networks (ANN) and uses it to model every problem that comes their way, rather than picking the right algorithm and right tool for the job at hand.
HumalyticaTM Analytics attempt demonstrate that business problems can be solved with multiple methods/algorithms using multiple tools, and that there is an optimal solution among the larger solution set. We do this by blogging on multiple websites, and by providing training at conferences, workshops, and to companies directly.
Our tool expertise includes:
·       SAS Studio
·       SAS Enterprise Miner
·       SPSS Modeler
·       SPSS Stats
·       R Studio
·       Python
·       Python using Jupiter
·       MATLAB
·       Octave
·       SciLab
We also have expertise with the following algorithms:
·       Linear Regression
·       Logistic Regression
·       Generalized Linear Models
·       Random Forest
·       Empirical Bayes
·       Naïve Bayes
·       Artificial Neural Networks
·       Classification Trees
·       K-Means Clustering
·       And more…

1. Time Series Analysis using iPython

Time Series Analysis using iPython
In this example, we will examine ARMA and ARIMA models with Python using the Statsmodels package. This package can be downloaded at http://statsmodels.sourceforge.net/stable/index.html. Autogressive Moving-Average Processes (ARMA) and Auto-Regressive Integrated Moving Average (ARIMA) can be called from the tsa (Time Series) module from the Statamodels package.
Note: I am not as expert in time-series analysis as I am in other areas of Analytics, so if you find errors I would be happy to know about them and correct them. Introduction ARIMA models are, in theory, the most general class of models for forecasting a time series, which can be made to be “stationary” by differencing (if necessary), perhaps in conjunction with nonlinear transformations such as logging or deflating (if necessary). A random variable that is a time series is stationary if its statistical properties are all constant over time. A stationary series has no trend, its variations aro…

Neural Networks using R

Neural Networks using RByonMay 13, 2015
The intent of this article is not to tell you everything you wanted to know about artificial neural networks (ANN) and were afraid to ask. For that you’ll have to ask someone else. Here I only intend to tell you how you might use R to implement an ANN model. One thing I will say is that I rarely use an ANN. I have found them to work best in an ensemble model (using averaging) with logistics regression models. Using neuralnetneuralnet depends on two other packages: grid and MASS (Venables and Ripley, 2002). It is used is primarily with functions dealing with regression analyses like linear models (lm) and general linear models (glm). As essential arguments, we must specify a formula in terms of response variables ~ sum of covariates and a data set containing covariates and response variables. Default values are defined for all other parameters (see next subsection). We use the data set infert that is provided by the package dat…