Recent Ingenuity

Machine Learning

Machine Learning: Charity Donor Analysis

Machine Learning:  Charity Donor Analysis Introduction A charitable organization wishes to develop a machine learning model to improve the cost effectiveness of their direct marketing campaigns to previous donors. The recent mailing records reflect an overall 10% response rate with an average donation of $14.50. The cost to produce and send each mail is $2.
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Text Analytics Sentiment Analysis

Text Analytics in R – Internet of Things (IoT)

Internet of Things (IoT) Text Analytics in R A small corpus of ten articles related to the Internet of Things (IoT) were collected for the purpose of text analytics.  Using R, each article was cleaned for unusual characters, changed to lower case,  removed numbers, punctuation, stop words, white space along with any additional terms that
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Factor Analysis Path Diagram

Factor Analysis to Identify Sectors

Factor Analysis Introduction Utilizing a stock portfolio data set and a factor analysis to identify sectors in the stock market, we will transform the variables into log values to explain the variation in the log-returns of the stocks and market index.  We will begin the factor analysis by performing a Principal Factor Analysis without a
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Principal Components Analysis - Grouped by Sector

Principal Components Analysis

Principal Components Analysis Utilizing a stock portfolio data set and the Principal Components Analysis as a method in reducing dimension and as a remedial measure for multicollinearity in Ordinary Least Squares regression.  Beginning with the data, we will transform the variables into log values to explain the variation in the log-returns of the stocks and
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Automated Variable Selection

Automated Variable Selection The Amex, Iowa housing data set build has been utilized to develop various iterative regression models to determine the mean sales price of a house based on numerous variables. The variables range correlated, continuous variables to categorical variables. In this installment, we continue building the model using raw categories and later, the
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Data Variables and Analytical Models

Data Variables and Analytical Models

Data Variables and Analytical Models Before diving in to a statistical analysis of any dataset, spending the requisite time to understand the data, checking the quality and taking a look ‘under the dash’ is essential.  Below, we will examine the data variables and analytical models on a housing prices as a first step in predicting
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Regression Models Using Numerous Variables

Assessing Regression Models Using Numerous Variables Regression model on the Amex, Iowa housing data set builds regression models for the house sale price with numerous variables.  Some of which are highly correlated, continuous variables along to the other side of the continuum by evaluating categorical, low correlated variables.  An assessment of each model will be
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Python Word Analytics

Text Analytics on News Article

For a little fun, this is a text analysis based on a CBC News article which is available at http://www.cbc.ca/news/technology/trump-climate-change-executive-order-1.4043650[raw]. Below you will see the Python code along with the various word analytics on the text (which was downloaded and put into a text file named “cbcnewstrump.txt”. Enjoy! [/raw]

Variable Transformations

Variable Transformations: Continuous & Categorical

Variable Transformations The Amex, Iowa housing data set build has been utilized to develop various regression models to determine the sales price of a house based on numerous variables. The variables range from highly correlated, continuous variables to categorical variables with low correlations. In this assessment, variable transformations and comparisons of Y versus Log(Y) will
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Word Counter in Jupyter Notebooks

Word Counter in Jupyter Notebooks

Simple utilities can make things so much easier at times.  This Jupyter Notebook take a document or in this case, ‘Alice in Wonderland’ by Lewis Carroll and provides the top 10 words.  Naturally, words can be eliminated but for ease of reference, only the word ‘the’ has been removed. For websites, a deeper cleaner is needed
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