Defining objective and key metrics. At this stage, our programmers identify the main goals that predictive analytics solutions should accomplish. They find out key metrics that align with company goals and will help run accurate predictive analytics for businesses and its managers.

Data gathering and integration. Next, Springs engineers collect information from internal and external sources, ensuring its quality and consistency. They gather data in comprehensive datasets for the solution to work with later on.

Data exploration and cleaning. Once this step is over, our programmers clean and reprocess information to address missing values and outliers. They explore the datasets to understand their traits and patterns.

Feature engineering. When there’s enough information to work with, Springs experts establish and develop relevant features to train the relevant model.

Model selection and training. Next, they select the right predictive modeling techniques based on the issues that must be addressed. Springs programmers experiment with several algorithms and model architectures until they find the most effective ones.

Model evaluation. At this stage, our experts check the performance of the selected model, its accuracy, recall, and precision. This process is repeated several times until Springs engineers are sure that the solution will bring the most predictive analytics benefits to the client.

Deployment and monitoring. Once the model works properly, our programmers send it to the production environment. During this stage, they monitor model performance to find any potential issues with its work.

Maintenance and updates. After releasing the solution, our developers create a maintenance schedule for updates. This is crucial to keep the product working as intended and maintaining its accuracy.