LinearLeap: Towards More Intelligent Machine Learning Tools

Upload data, run regression, get recommendations - powered by LLMs and built with analysts in mind.

LLMs are doing a fantastic job in automating repetitive and mundane day-to-day tasks. However, where they can truly add value is in performing "intelligent" tasks.

With this in mind, I started exploring how I could create "intelligent" ML models. When I say intelligent, I mean models that can provide crisp, actionable recommendations to analysts and stakeholders - not just stir through piles of data, try 10 different models, and then say "here's everything, select whatever you like."

To address this need, I developed an intelligent Linear Regression assistant. It's currently hosted on Streamlit Cloud and accessible via this link: LinearLeap

This web application leverages multimodal LLMs (currently configured to use Gemini). While I'm using a free API for demonstration purposes, users can enter their own API keys to use the tool as extensively as they wish.

The Multiple Linear Regression model is still a work in progress, which I plan to enhance later. Nevertheless, I'm moderately satisfied with the Linear Regression tool's current capabilities.

If you're an analyst, data scientist, or stakeholder with some ML knowledge, I invite you to try it out and share your feedback. Your input will be valuable as I continue to improve the application.

Features:

• Upload and analyze your datasets with ease

• Perform linear and multilinear regression analysis

• Visualize relationships between variables

• Get detailed statistical insights and predictions

• Receive tailored recommendations based on your data (GenAI generated)

Resources:

• GitHub repository: LinearLeap - Github

• Demo video:

  (Please excuse my presentation - recording yourself is a humbling experience!!)

Future enhancements planned:

• Fully integrating Multiple Linear Regression

• Better support for categorical variables

• Enhanced visualizations and export options

• More robust handling of multicollinearity

• Even smarter GenAI-generated recommendations

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