Conclusion
Detection and correction of data quality problems with machine learning

Data cleaning and preparation aimed at improving data quality are widely regarded as an essential prerequisite for the application of machine learning techniques, as errors in the data directly impact the performance of predictive models and the validity of their results. Traditionally, research-based solutions for data cleansing have focused on correcting quality problems "in the abstract", sometimes independently of the application using the data. In an industrial context, the application of data cleansing techniques is often determined by the need to reduce the costs associated with poor-quality data, or to gain in performance, based on measurable indicators (KPIs - Key Performance Indicators). Since the rise of artificial intelligence and the democratization of the application of learning methods, data cleansing is now associated with the stage of transforming and preparing data so that...