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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