There are 34 repositories under data-exploration topic.
1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.
Visualize and compare datasets, target values and associations, with one line of code.
First open-source data discovery and observability platform. We make a life for data practitioners easy so you can focus on your business.
Automatically find issues in image datasets and practice data-centric computer vision.
Feature exploration for supervised learning
Automate Data Exploration and Treatment
Desbordante is a high-performance data profiler that is capable of discovering many different patterns in data using various algorithms. It also allows to run data cleaning scenarios using these algorithms. Desbordante has a console version and an easy-to-use web application.
Grep through all Grafana entities in the spirit of git-wtf.
😎 A curated list of software and resources for exploring and visualizing (browsing) expression data 😎
light and fast implementation of web pivot table / pivot chart components.
Enjoy your transcriptomic data and analysis responsibly - like sipping a cocktail
A library for detecting problematic data segments in structured and unstructured data with few lines of code.
A collection of Jupyter notebooks exploring different datasets.
Multidimensional data explorer and visualization tool.
bamboolib - template for creating your own binder notebook
Light, personalized, interactive dashboards for urban data exploration.
Using R and machine learning to build a classifier that can detect credit card fraudulent transactions.
Routines for exploratory data analysis.
A collection of handy ML and data visualization and validation tools. Go ahead and train, evaluate and validate your ML models and data with minimal effort.
Understand the relationships between various features in relation with the sale price of a house using exploratory data analysis and statistical analysis. Applied ML algorithms such as Multiple Linear Regression, Ridge Regression and Lasso Regression in combination with cross validation. Performed parameter tuning, compared the test scores and suggested a best model to predict the final sale price of a house. Seaborn is used to plot graphs and scikit learn package is used for statistical analysis.