35 results for “topic:ydata-profiling”
This ETL (Extract, Transform, Load) project employs several Python libraries, including Airflow, Soda, Polars, YData Profiling, DuckDB, Requests, Loguru, and Google Cloud to streamline the extraction, transformation, and loading of CSV datasets from the U.S. government's data repository at https://catalog.data.gov.
The model predicts household energy usage using historical data and weather factors to optimize consumption and promote sustainability.
A python project using Streamlit, Pycaret and Pandas to demo an automated data modelling🤖 workflow.
The model predicts household energy usage using historical data and weather factors to optimize consumption and promote sustainability.
Repositório para geração de relatórios exploratórios a partir de arquivos CSV utilizando a biblioteca ydata-profiling.
YData Profiling
Comparison between several Python data profile libraries.
Pancreatic disease prediction from biomarker tabular data (Debernardi et al., 2020) — EDA, classical ML (CatBoost/LightGBM/XGBoost), PyTorch MLP, LightAutoML, Optuna HPO, and rigorous evaluation
This repository contains the code snippets, short and long scripts for EDA, and some useful libraries to save time.
An interactive data cleaning, profiling, and prediction platform built with Streamlit.
End-to-end BI pipeline on IMDb datasets using Azure Data Factory, Snowflake, and Tableau to deliver insights through scalable modeling and visualization.
A modern, web-based Exploratory Data Analysis (EDA) tool built with Streamlit and ydata-profiling. Transform your CSV data into comprehensive insights with just a few clicks!
Generate reports with ydata_profiling
Exploratory Data Analysis on Titanic Dataset.
Automated data profiling and quality gates for ETL pipelines using ydata-profiling.
Multi-environment CSV data analysis orchestrator that resolves dependency conflicts between profiling engines through isolated conda environments while providing a unified interface.
This repository showcases my learning process of automating EDA using 'ydata-profiling'
Analyzed food safety inspection records across Chicago and Dallas to identify violation patterns, risk factors, and operational insights for public health departments.
BI analytics project analyzing traffic collision data across Austin, Chicago, and NYC to identify high-risk patterns and inform public safety interventions using ETL pipelines and interactive dashboards.
Get instant EDA reports on every DataFrame in your Metaflow steps — zero code changes
Creating quick visualizations and summary statistics using python
Read, Visualize, Automate and..
The Exploratory Data Analysis (EDA) App is a Streamlit-based web application that allows users to perform comprehensive exploratory data analysis on their datasets. This app provides an intuitive and user-friendly interface for uploading CSV files, visualizing the input data, and generating an interactive profiling report.
ensoML is a beginner-friendly, no-code AutoML platform, providing a modern, intuitive, and responsive interface that empowers users to upload datasets, generate rich EDA reports, and train optimized ML models—all without writing a single line of code.
Data Sweeper Pro+ is an advanced data cleaning and transformation platform built with Streamlit. It allows users to upload datasets, clean them, analyze them with interactive profiling reports, and export the cleaned data in multiple formats. The app is designed for both technical and non-technical users.
Intrusion Detection in Military Networks ⚠️🚀🔍
Data Profiler is a Streamlit app designed to provide insightful data analysis and visualization. Users can upload their datasets in '.csv' or '.xlsx' format, and the app generates a comprehensive profiling report using the YData Profiling library.
End-to-end exploratory data analysis of the Titanic dataset to uncover key factors influencing passenger survival using data cleaning, visualization, and feature engineering.
Data profiling y-data profile, Data staging (Staging tables), Talend for ETL jobs, MySQL validations Dimensional model (Target tables), Facts and Dimensions, Mapping document explaining the source column name and where it finally maps to target column, Stage to Target, Document all transformations if any
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