Advanced Analytics Blog

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Retail store sales forecasting

Sales forecasting is an essential task for the management of a store. During this article we are going to use the information about the sales of a drug store from the last two years in order to predict the amount of sales that it is going to have one week in advance.
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Principal components analysis

In this article, we explain principal components analysis. It is a statistical technique that allows to identify underlying linear patterns in a data set so it can be expressed in terms of other data set of significatively lower dimension without much loss of information.
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Market Basket Analysis using R and Neural Designer

In this article, our objective is to analyze a dataset from a grocery store in order to create a recommendation system. This system will be capable of generating accurate recommendations about products that the user may have an interest in.
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electricity demand forecasting

Electricity demand forecasting

Forecasting of power demand plays an essential role in the electric industry. It provides the basis for making decisions in power system planning and operation. The objective of this article is to explain all the factors that lead to demand change and to determine the underlying causes.
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6 testing methods for binary classification

Depending on the type of problem that we are analyzing, there are some specific methods that may help us to study in depth the performance of the predictive model. In this article, we will focus on the 6 most used testing analysis methods for binary classification problems.
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Perceptron: the main component of neural networks

There are many different types of neural networks, from which the multilayer perceptron is the most important one. The characteristic neuron model in the multilayer perceptron is the so called perceptron. In this article we will explain the mathematics on this neuron model.
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Improving risk assessment using Advanced Analytics

Nowadays, the risk assessment process carried out by insurance companies has became obsolete. By analyzing the available information on the customers stored by the company, we can develop risk models that evaluate new customers in a faster and more accurate fashion.
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Genetic algorithms for feature selection in Data Analytics

The genetic algorithm is a stochastic method for function optimization based on the mechanics of natural genetics and biological evolution. In this article we show how genetic algorithms can be applied to optimize the performance of a predictive model, by selecting the most relevant features.
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What is Advanced Analytics?

Advanced Analytics is the set of techniques used to discover intricate relationships, recognize complex patterns or predict current trends in your data. Its objective is to model data from internal and external variables in order to obtain useful insights that results in smarter decisions and better business results.
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3 methods to deal with outliers

An outlier is a data point that is distant from other similar points. They may be due to variability in the measurement or may indicate experimental errors. Along this article, we are going to talk about 3 different methods of dealing with outliers.
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Customer segmentation using Advanced Analytics

Telemarketing is a form of direct marketing that is widely used by all types of companies. This technique can be extremely powerful at generating sales, but it requires a strict selection of potential clients. Advanced Analytics allows us to select individual targets, which results in increased profitability.
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5 algorithms to train a neural network

The procedure used to carry out the learning process in a neural network is called training algorithm. The learning problem in neural networks is formulated in terms of minimization of a loss function, F. For that, there are many different algorithms in order to get the best possible results.
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Learning about the Predictive Model Markup Language (PMML)

PMML is an XML-based format that provides portability of predictive models among various software tools. It allows data scientists to design and deploy models in a more flexible way, so more software tools can make use of them.
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6 Applications of predictive analytics in business intelligence

Predictive analytics extracts information from data sets in order to discover complex relationships, recognize unknown patterns, forecasting actual trends, find associations, etc. This allows us to anticipate the future and make the right decisions.
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Model selection algorithms in predictive analytics

Our tool Neural Designer provides innovative algorithms to automate the model selection process. In this post, we will explain some basic ideas about model selection and the algorithms implemented in this software.
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Higgs Boson machine learning challenge

The data used for this challenge are simulated data provided by the ATLAS experiment at CERN. Physicists use them to optimize the analysis of the Higgs boson. The database has been downloaded from Kaggle.
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Modeling the performance of an engine

Nowadays, engine manufacturers are trying to implement strategies that provide the best efficiency and are as clean as possible. Deep learning can be used to model the behavior of an engine, based on the most important operational variables. That model can be used for different purposes, which include performance optimization or predictive maintenance.
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Telemonitoring of patients with Parkinson’s disease

One of the biggest difficulties for those patiens with Parkinson’s disease is the requisite physical visit to the clinic in order to monitoring and treatment. The main goal of this study is to predict the unified Parkinson’s disease rating scale (UPDRS) for classifying the stage of the disease, by remote telemonitoring of the patients.
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