Our top algorithms for predictive modeling

When doing Predictive Models, we create ad hoc algorithms to help our client companies solve specific problems.

These algorithms may vary according to the problem that needs solved. In fact, selecting the wrong algorithm will not only result in poor performance, but it may also be a waste of resources.

The best way to choose an algorithm is by asking the right questions to the professionals in the industry to identify the exact problem that we are going to solve with the predictive model. That is why we will work in close collaboration with your company experts.

To provide an idea, the top-5 algorithm we use more often for predictive models are:

1. Statistical Models:
   These include traditional techniques such as linear and logistic regression, which are used to analyze and predict data based on its distribution and inherent relationships.
2. Machine Learning Algorithms:
   1. Supervised: Models such as decision trees, SVMs (Support Vector Machines) and logistic regression, which are trained on labeled datasets.
   2. Unsupervised: Techniques such as clustering (e.g., K-means) and principal component analysis (PCA), which find hidden patterns and structures in unlabeled data.
   3. Reinforcement Learning: Where algorithms learn through rewards and punishments, being useful in sequential decision-making situations.
3. Neural Networks:
   1. Artificial Neural Networks (ANN): Used to model complex relationships between inputs and outputs.
   2. Convolutional Neural Networks (CNNs): Specialized in processing data with a grid structure, such as images.
   3. Recurrent Neural Networks (RNN): Used for temporal data sequences or time series.
4. Deep Learning Techniques:
   Deep Learning is based on deep neural networks to learn hierarchical representations of data, being key in applications such as natural language processing and image recognition.
5. Explainable Artificial Intelligence (XAI) techniques:
   Methods that allow interpreting and understanding how complex models make decisions, essential for applications where transparency is crucial.

→ Check our Predictive Model solutions