Archive for ‘Data Science’ Category

16
Aug
Anticipating Customer Churn through Levers of Retention
  • Rubén Granados
  • 518 Views
  • 0 Comment
  • Big Data . Churn Prediction . Data Science . Fuga de Clientes . Machine Learning . Palancas de Retencion .

Identification and analysis of signs of customer churn Knowing which customers might not continue as such has been the objective of classic customer churn models, which final output consisted of the probability of loss associated with each customer over a specific temporary time range. However, once we know the customer is going to leave, we must ask ourselves: What can we do to keep them? And taking it even further: What can we do to keep the customer from even considering leaving? Preventive Measures for Customer Churn in the Banking Sector At Synergic Partners, we have carried out a project for one of the largest banking entities in Spain responding to these questions with a methodology based on the combination…

30
May
Improving Effectiveness at a Call Center with Machine Learning
  • Víctor Gonzalez
  • 1618 Views
  • 0 Comment
  • Algorithms . Big Data . Data Science . Machine Learning . Procesamiento de Lenguaje Natural .

Understanding our clients and giving the best response to meeting their needs in the shortest amount of time possible is key to improving satisfaction and engagement with the company. The problem begins when the number of clients is high and we are receiving hundreds or thousands of messages per day. In this situation, we have two problems to solve. First, we have to prioritize which messages we are going to respond first and, second, we have to understand what it is that they are saying. It is clear that some messages will be more important or more urgent than others and that it will not always be easy to prioritize. To make things easier, we can use Machine Learning techniques…

18
May
Predicting crime: fact or fiction?
  • Santiago González
  • 1795 Views
  • 0 Comment
  • Big Data . Crime Predicction . Innovation . Predicción de crímenes .

According to the OMS, one of the main worries of global society today is violence and crime, making avoiding crime and creating centers of intelligence to do so the main objective for police and judicial systems. In this sense, as director of innovation and influenced by science fiction, I set out to analyze this situation. Using the traditional scientific method, the first step was to ask questions and hypothesize: Is it possible to predict the place, date and time of a crime? And if so, to what extent, in what kind of detail? Do we have experience with this? Who can offer us information? Has anyone done this before and, if so, what were the results? Thanks to our previous…

17
Sep
Warnings about normalizing data
  • Santiago Morante
  • 3517 Views
  • 2 Comments
  • No tags

For many machine learning algorithms, normalizing the data of analysis is a must. A supervised example would be neural networks. It is known that normalizing the input data to the networks improve the results. If you don't believe me it's ok (no offense taken), but you may prefer to believe Yann Le Cunn (Director of AI Research in Facebook and founding father of convolutional networks) by checking section 4.3 of this paper. You can catch up the idea with the first sentence of the section: Convergence [of backprop] is usually faster if the average of each input variable over the training set is close to zero. Among other things, one reason is that when the neural network tries to correct…

08
Apr
An architecture to tweet them all
  • Carla Martínez
  • 3793 Views
  • 2 Comments
  • No tags

The definition of the project was simple: find out what it is that people think of our client on Twitter, both the good and the bad, and be able to visualize it all on a dashboard. Or at least as much as possible. As soon as we find out the range, we try to get as close as we can. Read from Twitter, analyze what is said and process it. Three modules. The architecture didn’t seem complicated, but we had no idea what was ahead. I have always been taught that a good engineer finds a problem, suggests a solution and chooses a series of tools to carry it out. Problem, solution and tools. Always. Well… not always. In this…

29
Feb
The hottest trends in Big Data and Data Science
  • Synergic Partners
  • 4259 Views
  • 0 Comment
  • Big Data . Big Data Diet . Data . Data outsourcing . data plumbing . Data Science . Deep Learning . Hadoop . HPC . light analytics . small data . Transformación digital .

Based on client surveys – vendors of products and data processing platforms – as well as trends on popular blogs, LinkedIn and similar posts, here are the most in-demand big data and data science topics: The importance of data plumbing (as the cleaning and preparation of data has come to be known in general terms) in optimizing big data tools, making them more precise, safer, more reliable and faster through “data pipes” (internet, intranet, in-memory, local servers, the cloud, Hadoop clusters, etc.), optimizing such aspects as redundancy, load balance, and the intake, storage, compression and summary of data, among other things. The rise of the data plumber, an architect of systems and systems analysis (a new figure in the ranks…

22
Apr
Moving towards the creation of “the web for machines”
  • Synergic Partners
  • 8186 Views
  • 1 Comment
  • No tags

The internet has now long been established as the global standard of communication and the main point of human interaction. Mobile phones and the connectivity of mobile networks, as well as the existence of standards eliminating any and all barriers to entry, have largely made this possible. Big data and new analytical capabilities emerging from this new paradigm have appeared as a part of this environment, and data itself has become the main asset of today’s top companies. With the first transformation over, the technology market is today attempting to meet a new challenge: that of the Internet of Things and the resulting analysis of data. However, in this case, the market is coming up against various barriers to entry…

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