Awesome
π Do data science and data analysis in plain english π
<p align=""> <a href="https://datahorse.ai/"> <img src="image.png" height=""> </a> <h1 align="center"> <a href="https://github.com/DeDolphins/DataHorse">βοΈ Star DataHorse</a> </h1> </p> <p align="center"> <a href="https://www.linkedin.com/showcase/data-horse"> <img src="https://img.shields.io/badge/LINKEDIN-blue.svg?style=for-the-badge&logo=read-the-docs&logoColor=white&labelColor=000000&logoWidth=20"> </a> </p>π DataHorse is an open-source tool and Python library that simplifies data science for everyone. It lets users interact with data in plain English π, without needing technical skills or watching tutorials π₯ to learn how to use it. With DataHorse, you can create graphs π, modify data π οΈ, and even create smart systems called machine learning models π€ to get answers or make predictions. Itβs designed to help businesses and individuals πΌ regardless of knowledge background to quickly understand their data and make smart, data-driven decisions, all with ease. β¨
Quick Installation
pip install datahorse
Examples
Weβre using the Iris flower dataset as an example to demonstrate how DataHorse simplifies data analysis. This example showcases how our tool can handle real-world data, making it easier to work with and understand.
Setup and usage examples are available in this Google Colab notebook.
import datahorse
df = datahorse.read('https://raw.githubusercontent.com/plotly/datasets/master/iris-data.csv')
df = df.chat('convert species names to numeric codes')
seed=int
: Ensures that the generated function is reproducible across different runs.cache_req=True
: Enables caching for the API request, ensuring that identical prompts won't trigger unnecessary API calls.
df = df.chat('convert species names to numeric codes', seed=int, cache_req=True)
Model training
df.chat('train a classification model and save the model')
Model testing
datahorse.test("path of the saved model",[["list of testing features"]])
Library Demo
<img src="demo/DatahorseLibrary.gif">Guide for running the DataHorse WebUI
Clone the repository
git clone https://github.com/DeDolphins/DataHorse.git
Go to the directory
cd DataHorseUI
Install the requirements
pip install -r requirements.text
Run DataHorse WebUI
streamlit run app.py
WebUI Demo
<img src="demo/datahorseUI.gif"> Please support the work by giving the repository a star, contributing to it, orStar History
βοΈ Star DataHorse to increase our visibility
Contribute
Found a bug or have an improvement in mind? Fantastic!
Got a solution ready? That's even better!
Ready to share it with us? We're all ears!
Start at the contributing guide!