Search Results for "analytics"

pandas-profiling generates profile reports from a pandas DataFrame. The pandas df.describe() function is handy yet a little basic for exploratory data analysis. pandas-profiling extends pandas DataFrame with df.profile_report(), which automatically generates a standardized univariate and multivariate report for data understanding. High correlation warnings, based on different correlation metrics (Spearman, Pearson, Kendall, Cramér’s V, Phik). Most common categories (uppercase, lowercase,...

MentDB is an open-source platform driving research into next-generation AI and universal data exchange. Our architecture is built around the revolutionary Mentalese Query Language (MQL). MentDB Weak (Generalized Interoperability): A unified data layer enabling seamless data exchange and application integration (SOA, ETL, Data Quality). We eliminate data silos through a single, generalized data language. MentDB Strong (Strong AI / AGI): The framework for exploring and building Machine...

EZStacking is Jupyter notebook generator for supervised learning problems using Scikit-Learn pipelines and stacked generalization. EZStacking handles classification and regression problems for structured data. It can also be viewed as a development tool, because a notebook generated with EZStacking contains: -an exploratory data analysis (EDA) used to assess data quality - a modelling producing a reduced-size stacked estimator - a server returning a prediction, a measure of the quality...

This project is dedicated to open source data quality and data preparation solutions. Data Quality includes profiling, filtering, governance, similarity check, data enrichment alteration, real time alerting, basket analysis, bubble chart Warehouse validation, single customer view etc. defined by Strategy. This tool is developing high performance integrated data management platform which will seamlessly do Data Integration, Data Profiling, Data Quality, Data Preparation, Dummy Data...

...WhyLogs is an open source statistical logging library that allows data science and ML teams to effortlessly profile ML/AI pipelines and applications, producing log files that can be used for monitoring, alerts, analytics, and error analysis. WhyLogs calculates approximate statistics for datasets of any size up to TB-scale, making it easy for users to identify changes in the statistical properties of a model's inputs or outputs. Using approximate statistics allows the package to run on minimal infrastructure and monitor an entire dataset, rather than miss outliers and other anomalies by only using a sample of the data to calculate statistics.