Artificial Intelligence and Machine Learning

At Lucentia Lab we create systems that learn automatically using complex patterns in millions of data

 

Artificial Intelligence

Anticipation of problems or failure

Custom price calculation

Customer churn

Fraud identification scenarios

Predictive maintenance

Moment / optimal quantity

Client or candidate scoring

Optimal feeding

What does machine learning provide us?

Increase human capacity

+

Higher speed

More efficiency

Help companies get further

 

Machine Learning

Machine Learning or Machine Learning is a subset of artificial intelligence. Where computers have the ability to learn without being programmed. That is, it does not require a person to program such instructions.

Data preparation resources.

Algorithms - basic and advanced.

Modeled together.

Automation and iterative processes.

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Scalability.

Methodology and Phases

In Lucentia Lab we have developed a methodology oriented to the development of customized solutions:

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PHASE 1: Identification and specification of the problem framework

Understand the business need and clearly define the problem. Clearly define the business questions we want to answer. It is important to know what an answer or result would be like if we considered a solution.

PHASE 2: Understanding the data

 Know if you have the necessary data to answer the questions that define the problem to be solved and, therefore, the objective of the project. It is a preliminary analysis of the available data: its size, its quality, whether they are complete or partial.

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PHASE 3: Feature extraction

An exploratory analysis, observe them, probably graph them from different perspectives to transform these data into tabular formats suitable for their treatment.

PHASE 4: Implementation, analysis and validation

The most appropriate algorithm is chosen in relation to the problem we wish to solve. A first approximation of the solution is made and the results are analyzed. We will return to this stage until the model fits well and we reach the objectives.

PHASE 5: Presentation of results

We will meet with you to show you how the solution benefits your business although there will be a constant communication throughout the process of this to make joint decisions.

PHASE 6: Prediction

Once our model has been implemented overcoming the problem posed, the next step is to make the prediction. Which we can start by entering new data to our model.

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