Photo by Conny Schneider on Unsplash Synthetic data is a cornerstone of Data Centric-AI, an approach that focuses primarily on data quality rather than models. For the past few years, synthetic data gained attention because of a wide range...
Generating synthetic data lays a crucial role in addressing the problematic aspects of data in Data Science, such as balancing classes, expanding small datasets, and securely sharing sensitive information like bank transactions while...
In today's data-driven world, privacy concerns have become paramount. The use of personal data in various applications raises ethical and legal questions, prompting the need for privacy-preserving techniques. Differential privacy has...
Photo by Roman Synkevych on Unsplash A probabilistic approach to fast synthetic data generation with ydata-synthetic To find synthetic data generation within the same sentence as Gaussian Mixture Models (GMMs) sounds odd, but it makes a...
I improved a model to save a hypothetical auto insurance company almost $200 per claim! One of the biggest mistakes that junior data scientists make is focusing too much on model performance while remaining naive about the model’s impact...
Cover Photo by Avery Evans on Unsplash The value of data and the adoption of data-driven strategies have proven valuable for many organizations, particularly the financial services sector, with cases ranging from fraud detection to...
Cover Photo by Nicholas Doherty on Unsplash The adoption of a data-driven strategy for Predictive Maintenance empowers energy and utility industries. A successful Predictive Maintenance implementation can reduce maintenance costs by...
Cover Original Photo by Alina Grubnyak on Unsplash Smart meters are crucial for the energy sector's increase of service efficiency - as they allow new service development and build better customer relationships. At the heart of EDP smart...
The creation of new business models and the need to find the right competitive edge is the drive for the growing adoption of AI initiatives. Digital transformation is a heavy legacy transformation and not a 6 months project, nevertheless,...
Advanced Data Visualisation with Pandas Profiling Visualisation is the cornerstone of EDA. When facing a new, unknown dataset, visual inspection allows us to get a feel of the available information, draw some patterns regarding the data,...
ydata-profiling time-series exploratory analysis One of the early steps in the data science development cycle is to understand and explore the data for the problem you’re solving. EDA is a crucial step for a better data science workflow,...
Leverage EDA to beat GANs challenges Machine Learning has mainly evolved as a model-centric practice where achieving quality results is left pending small data processing efforts and instead concentrated on experimenting with families of...
