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Human interpretation of data is inherently susceptible to cognitive biases. While Large Language Models (LLMs) act as automated data analysts, they often mirror user biases or training artifacts. This project introduces a "Bias-Contrastive" Agentic Framework that goes beyond simple text analysis.
The main features of bididi-badidi/fyp-data-analysis-with-llm are: Data Analysis.
Projects with overlapping indexed features include: aws/aws-sdk-pandas — aws-sdk-pandas is a Python library that integrates pandas dataframes with AWS services, acting as a cloud data ETL… cdslaborg/paramonte — ParaMonte: Parallel Monte Carlo and Machine Learning Library for Python, MATLAB, Fortran, C++, C. data-centric-ai-community/fg-data-profiling — This project is a data profiling and exploratory data analysis tool designed to generate automated quality reports for… desbordante/desbordante-core — Desbordante is a high-performance data profiler that is capable of discovering many different patterns in data using… generateme/fastmath — Fast primitive based math library. alanmarazzi/panthera — Data-frames & arrays on Clojure.
aws-sdk-pandas is a Python library that integrates pandas dataframes with AWS services, acting as a cloud data ETL tool and data lake connector. It provides a unified interface to move and transform data between in-memory dataframes and cloud storage, databases, and data warehouses. The project distinguishes itself as a distributed compute orchestrator capable of submitting pandas-based workloads to EMR clusters and serverless processing environments. It further specializes in coordinating distributed data processing via Ray cluster initialization to handle datasets that exceed the memory of
ParaMonte: Parallel Monte Carlo and Machine Learning Library for Python, MATLAB, Fortran, C++, C.
This project is a data profiling and exploratory data analysis tool designed to generate automated quality reports for Pandas and Spark dataframes. It serves as a system for computing descriptive statistics, identifying correlations, and analyzing univariate and multivariate data patterns. The tool provides specialized capabilities for comparing different versions of datasets to identify changes in data quality and distributions. It includes a dedicated profiler for time-dependent data to extract statistical information such as seasonality and auto-correlation. The software covers a broad an
