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:

Streamlined data analysis process utilizing advanced AI technology and LLMs
Enhanced productivity and efficiency through intuitive language-based commands
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

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aadityaubhat OP t1_jdwkqy4 wrote on March 27, 2023 at 6:46 PM

#2,397,555

Demo - https://github.com/aadityaubhat/auto-analyst/blob/main/auto-analyst/auto-analyst-demo.gif

[deleted] t1_jdxeb6k wrote on March 27, 2023 at 9:57 PM

#2,401,768

[deleted]

addandsubtract t1_jdxeqdo wrote on March 27, 2023 at 10:00 PM

#2,401,823

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

[deleted] t1_jdxgyy0 wrote on March 27, 2023 at 10:16 PM

#2,402,117

Replying to addandsubtract (#2,401,823)

[deleted]

aadityaubhat OP t1_jdxhe79 wrote on March 27, 2023 at 10:19 PM

#2,402,185

Replying to addandsubtract (#2,401,823)

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:

⁠Parsing the data, description, and question: The tool takes your data and a plain English question as input, then parses and understands the context.
⁠Basic data cleaning: Before diving into the analysis, Auto-Analyst cleans the data to ensure it's ready for processing.
⁠Determining the answer type: Based on the input question, Auto-Analyst figures out if the answer can be provided through aggregation or visualization.
⁠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.
⁠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.