Dingo: A Comprehensive Data Quality Evaluation Tool

In this project, a RFM model is implemented to relate to customers in each segment. Assessed the Data Quality, performed EDA using Python and created Dashboard using Tableau.

Official implementation of our paper "Finetuned Multimodal Language Models are High-Quality Image-Text Data Filters".

This repository contains solutions to the 3 different tasks that must be performed during the data analytics virtual internship provided by KPMG via Forage.

Step-by-step exploratory movement data analysis protocol in a Jupyter notebook

Data-IQ: Characterizing subgroups with heterogeneous outcomes in tabular data (NeurIPS 2022)

🔍Your Data Quality Detector / Gain insight into your data and get it ready for use before you start working with it 💡📊🛠💎

Health Data Metrics (HDM) a Data Quality assessment Application.

🧼🔎 SelfClean revised versions of benchmark datasets for more reliable performance estimation.

A highly-configurable, real-time data quality monitoring tool designed for streaming data

Addressing Data Quality Challenges in Ambulatory Wrist-worn Wearable Monitoring Through Analytical and Practical Approaches

Data quality, maturity and utility labelling tool for the EHDS (HealthData@EU)

SDQCPy is a comprehensive Python package designed for synthetic data management, quality control, and validation.

Collection of R scripts to test packages in conducting data quality assessments

A function that automatically generates a Data Quality Report for your data

To provide Sales trend visibility on monthly, Quarterly and yearly basis.

A signal quality assessment pipeline and dashboard for ambulatory cardiovascular data

KGHeartBeat is a community-shared open-source knowledge graph quality assessment tool to perform quality analysis on a wide range of freely available knowledge graphs registered on the LOD cloud and DataHub. Web-App: http://www.isislab.it:12280/kgheartbeat/

Sumeh — Unified Data Quality Framework Sumeh is a unified data quality validation framework supporting multiple backends (PySpark, Dask, Polars, DuckDB, Pandas) with centralized rule configuration.

This project involves analyzing Sprocket Central Pty Ltd Data to help the marketing department unveil useful insights that could help them optimize resources allocation for targeted marketing