Artificial Intelligence & Machine Learning
In Lucentia Lab, we create self-learning systems using complex patterns in millions of data.
Advantages of Artificial Intelligence
Anticipation of problems or failure
Custom price calculation
Fraud identification scenarios
Moment / optimal quantity
Client or candidate scoring
What does machine learning provide us?
Increase human capacity
Help companies get further
What is Machine Learning?
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.
Automation and iterative processes.
Methodology and Phases
In Lucentia Lab we have developed a methodology oriented to the development of customized solutions:
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.
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.