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.

YData Profiling

Repositório para geração de relatórios exploratórios a partir de arquivos CSV utilizando a biblioteca 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

Inspired by ydata-profiling. Automated exploratory data analysis web app — upload any CSV and instantly get stats, correlations, missing value insights and smart alerts.

This repository contains the code snippets, short and long scripts for EDA, and some useful libraries to save time.

Exploratory Data Analysis on Titanic Dataset.

Generate reports with ydata_profiling

An interactive data cleaning, profiling, and prediction platform built with Streamlit.

Automated data profiling and quality gates for ETL pipelines using ydata-profiling.

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!

End-to-end BI pipeline on IMDb datasets using Azure Data Factory, Snowflake, and Tableau to deliver insights through scalable modeling and visualization.

Multi-environment CSV data analysis orchestrator that resolves dependency conflicts between profiling engines through isolated conda environments while providing a unified interface.

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.

This repository showcases my learning process of automating EDA using 'ydata-profiling'