Identify Problem and Collect Data
Our team of consultants and data scientists take on the preliminary work of evaluating your business objectives and determining the relevant solutions to the problems that are posed. Based on the outlined goals, qualitative and quantitative data is extracted for analysis.
Prepare Data for Analysis
Raw data requires a lot of preprocessing to make it usable and efficient. We clean, normalize, label, classify the collected data and eliminate the unusable parts. Pertinent visualizations are prepared to examine its scope and uncover hidden connections.
Transform the Data
This is the consolidation stage of data processing, where the data is transformed into forms appropriate for mining and getting intelligent insights. The data is simplified by normalization, attribute decomposition and aggregated into understandable categories to make it uniform.
Data splitting focuses on 3 main subsets: training, testing, and validation. Training data is a learning sample for the model, test data ensures performance improvement, and validation data equips the model for unforeseen tasks. This process builds a robust and reliable model.
At this stage, the transformed training data is used to create multiple algorithm models. Depending on the desired outcomes of the task at hand, supervised or unsupervised learning method is applied for experimentative analysis using set parameters.
Test and Validate Models
The created models are now put to the test to check for the best results. Cross-validation and ensembling techniques are used to scale speed, accuracy, efficiency, and performance. The goal is to tune the algorithm and develop a successfully optimized model.
By this stage, we have a production-grade model ready for deployment. For optimum performance and smooth integration, A/B testing and modifications are implemented. The model is now ready to make inferences.