[P] 🎉 Announcing Auto-Analyst: An open-source AI tool for data analytics! 🎉

Submitted by aadityaubhat t3_123w6sv in MachineLearning

Auto-Analyst leverages power of cutting-edge Large Language Models (LLMs) to revolutionize data analytics. This powerful UI tool simplifies the data analysis process, eliminating the need for complex coding.

🔎 Key Features of Auto-Analyst:

  1. Streamlined data analysis process utilizing advanced AI technology and LLMs
  2. Enhanced productivity and efficiency through intuitive language-based commands
  3. Increased accessibility to data analysis for professionals across industries

🔗 Want to explore and contribute to the project? Head over to the GitHub repo: https://github.com/aadityaubhat/auto-analyst

22

Comments

You must log in or register to comment.

ZestyData t1_jdyli0a wrote

Mods can we crack down on students posting basic tutorial-tier side projects to this sub, its becoming common lately.

16

Flag_Red t1_jdyz0vk wrote

Alternatively, we could encourage people to make interesting stuff and share it with the community.

9

zoontechnicon t1_je10zfj wrote

> Auto-Analyst leverages the OpenAI API

I feel like frontends for OpenAI/ChatGPT do not belong here

6

addandsubtract t1_jdxeqdo wrote

Can you go into more details as to what this does and how it works?

1

aadityaubhat OP t1_jdxhe79 wrote

Sure, currently it supports aggregation and visualization, I am working to add more functionality to it.
The core process of Auto-Analyst consists of several steps:

  1. ⁠Parsing the data, description, and question: The tool takes your data and a plain English question as input, then parses and understands the context.
  2. ⁠Basic data cleaning: Before diving into the analysis, Auto-Analyst cleans the data to ensure it's ready for processing.
  3. ⁠Determining the answer type: Based on the input question, Auto-Analyst figures out if the answer can be provided through aggregation or visualization.
  4. ⁠Aggregation: If the question requires an aggregated answer, Auto-Analyst leverages the OpenAI API to generate an SQL query. It then tries running the query on the data. If it fails, the OpenAI API is used to correct the query. This process continues until a working query is obtained or the user-defined maximum number of tries is reached. The aggregation results are then returned to the user.
  5. ⁠Visualization: If the question calls for a plot, Auto-Analyst first identifies the aggregated data needed for the visualization. It uses the aggregation steps described above to obtain this data. Next, it employs the OpenAI API to generate Python code for the plot and returns the visualization to the user.
7