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Machine Learning Techniques for Mixed Data Sets – Part 1. Most of time when evaluating some data sets there is a mix of qualitative and quantitative data. Using traditional techniques like K-Means, K-modes, SOM, PCA, MCA, etc… don’t allow the full use of the data set. We can always descritize the data and do an analysis with techniques for categorical data, however we lose information. […]. January 24, 2016. Regression Models for Counts. May 5, 2015. Box-Cox Transformation for normalizing data. This is t...

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Machine Learning Techniques for Mixed Data Sets – Part 1. Most of time when evaluating some data sets there is a mix of qualitative and quantitative data. Using traditional techniques like K-Means, K-modes, SOM, PCA, MCA, etc… don’t allow the full use of the data set. We can always descritize the data and do an analysis with techniques for categorical data, however we lose information. […]. January 24, 2016. Regression Models for Counts. May 5, 2015. Box-Cox Transformation for normalizing data. This is t...

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GilDrops | gildrops.com Reviews

https://gildrops.com

Machine Learning Techniques for Mixed Data Sets – Part 1. Most of time when evaluating some data sets there is a mix of qualitative and quantitative data. Using traditional techniques like K-Means, K-modes, SOM, PCA, MCA, etc… don’t allow the full use of the data set. We can always descritize the data and do an analysis with techniques for categorical data, however we lose information. […]. January 24, 2016. Regression Models for Counts. May 5, 2015. Box-Cox Transformation for normalizing data. This is t...

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gildrops.com

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Hello world! – GilDrops

http://gildrops.com/hello-world

April 18, 2015. April 18, 2015. This is the first post in this blog. I will write about analytics and some cool coding algorithms. It’s mainly a repository of the things that I learn (a way to retain knowledge). Https:/ wordpress.org/ Mr WordPress. Hi, this is a comment. To delete a comment, just log in and view the post's comments. There you will have the option to edit or delete them.

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About Me – GilDrops

http://gildrops.com/about

2015-2016) Masters in Knowledge Management and Business Intelligence. 2013-2014) Masters in Economics. My elected courses included Forecasting Methodologies, and Econometrics. My thesis has about a new hybrid multivariate aproach between a Neural Network and an ARIMA model to forecast exchange rates, coded with MATLAB. 2010-2013) Bachelor in Economics. 2006-2009) Bachelor in Computer Science. Python SciKit Learn is a set of simple and efficient tools for data mining and data analysis. Drill supports a va...

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Box-Cox Transformation for normalizing data – GilDrops

http://gildrops.com/box-cox-transformation-for-normalizing-data

April 20, 2015. April 20, 2015. Box-Cox Transformation for normalizing data. Many social scientists deal with data that does not conform with the assumption of normality. This is a problem because many statistical tests and models are based on this assumption. This is not entirely true. It has been shown that parametric tests (multiple regression, ANOVA, etc…) can be robust to modest violations of the normality assumption and others assumptions like homoscedasticity. However we should be careful about vi...

4

Analytics – GilDrops

http://gildrops.com/category/analytics

There are 3 posts filed in Analytics. Machine Learning Techniques for Mixed Data Sets – Part 1. Most of time when evaluating some data sets there is a mix of qualitative and quantitative data. Using traditional techniques like K-Means, K-modes, SOM, PCA, MCA, etc… don’t allow the full use of the data set. We can always descritize the data and do an analysis with techniques for categorical data, however we lose information. […]. January 24, 2016. Regression Models for Counts. May 5, 2015. April 20, 2015.

5

Regression Models for Counts – GilDrops

http://gildrops.com/regression-models-for-counts

May 5, 2015. May 5, 2015. Regression Models for Counts. The most common models for Count Data. Count variables are many times modeled by a Linear Regression Model (LRM). Transformation to normalize the count data, so that the data meets the requirements of the LRM. However this can result in inefficient, inconsistent and biased estimates. The Poisson regression model (PRM). Real-life data are often characterized by over-dispersion that is, greater variance than might be expected in a Poisson distribution.

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Machine Learning Techniques for Mixed Data Sets – Part 1. Most of time when evaluating some data sets there is a mix of qualitative and quantitative data. Using traditional techniques like K-Means, K-modes, SOM, PCA, MCA, etc… don’t allow the full use of the data set. We can always descritize the data and do an analysis with techniques for categorical data, however we lose information. […]. January 24, 2016. Regression Models for Counts. May 5, 2015. Box-Cox Transformation for normalizing data. This is t...

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