https://github.com/blastre/mlcli

automated ML pipeline with LLM powered data analysis

https://github.com/blastre/mlcli

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Repository

automated ML pipeline with LLM powered data analysis

Basic Info
  • Host: GitHub
  • Owner: blastre
  • Language: Python
  • Default Branch: main
  • Size: 88.9 KB
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  • Stars: 0
  • Watchers: 1
  • Forks: 1
  • Open Issues: 0
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Created 11 months ago · Last pushed 10 months ago
Metadata Files
Readme

README.md

🚀 MLCLI – Build ML Pipelines. Talk to Your Data.

MLCLI is a modular toolkit that lets you automate full machine learning workflows and interact with your data effortlessly — via either a command-line interface or a Streamlit web app.

Just provide a CSV and enjoy:

🔍 Smart Task Detection (classification/regression)
📊 Fast Data Handling with Dask
🧠 LLM-Assisted Pipeline Planning via LLaMA 3.3 70B
⚙️ Auto Preprocessing & Feature Engineering
🤖 AutoML Training powered by PyCaret
📉 Comprehensive Metric Reporting (Accuracy, R², F1...)
💾 Joblib Model + Pipeline Saving
💬 Interactive Data Chat Mode — Ask natural language questions about your dataset (powered by Groq LLM)
📂 Metadata Export for reproducibility
🌐 Streamlit Web App — Use MLCLI through a simple, intuitive browser UI

Demo Usage

CLI tool

type "python click.py start" to start 1 2 3 4 5 6

Streamlit Webapp

type "streamlit run app.py" to start s1 s2 s3 s4 s5 s6

🧰 Tech Stack

Frameworks & Libraries: PyCaret, Scikit-learn, Dask, Click, Streamlit
AI & Automation: (LLaMA 3.3 70B)
Languages: Python
Data Processing: Pandas, CountVectorizer, datetime
CLI & Scripting: Click (modular command-line interface)
Model Handling: Joblib (pipeline & model saving)

🔁 Workflow

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Owner

  • Login: blastre
  • Kind: user

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