HFR

ML Copilot Agent

Preprint Under Review Journal of Engineering AI

Your AI-Powered Machine Learning Assistant

ML Copilot is an interactive machine learning assistant that streamlines data preprocessing, model training, evaluation, visualization, and documentation—all through an intuitive interface powered by cutting-edge AI.

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ML Workflow Automation

AI Assistant

Automated Preprocessing

Handle missing values, encoding, scaling, and feature engineering

Model Selection

Recommends models based on problem type and dataset

Evaluation & Reporting

Generates metrics, visualizations, and ready-to-share reports

Powerful Applications Across Domains

Biomedical Research

Accelerate biomarker discovery, analyze genomic data, and predict clinical outcomes with automated ML pipelines.

TCGA Analysis Survival Prediction Drug Response

Business Analytics

Transform raw business data into actionable insights with predictive modeling and customer segmentation.

Churn Prediction Market Segmentation Sales Forecasting

Computer Vision

Develop image classification and object detection models without writing complex code from scratch.

Medical Imaging Quality Control Facial Recognition

Natural Language Processing

Implement text classification, sentiment analysis, and document summarization with guided workflows.

Sentiment Analysis Topic Modeling Chatbots

Industrial IoT

Predictive maintenance and anomaly detection for manufacturing equipment using sensor data.

Predictive Maintenance Quality Prediction Process Optimization

Education & Research

Teach machine learning concepts and accelerate academic research with interactive experimentation.

Teaching Tool Research Prototyping Data Exploration

How ML Copilot Works

Command Input

User provides natural language instructions for ML tasks

LLM Processing

GPT-4o/Gemini interprets request and generates Python code

Code Execution

Code interpreter safely executes generated Python code

Result Delivery

Outputs, visualizations, and files are returned to user

Key Features

  • Interactive command-line interface for ML workflows
  • Support for multiple LLM providers (OpenAI, Gemini)
  • Built-in code interpreter for safe Python execution
  • Automated data preprocessing and feature engineering
  • Multiple ML algorithms with hyperparameter tuning
  • Comprehensive model evaluation metrics
  • Automated visualization and reporting

Technical Specifications

  • Core Architecture: LLM Agent with Code Interpreter
  • Supported Data: CSV, Excel, JSON, Images (with extensions)
  • ML Libraries: scikit-learn, pandas, numpy, matplotlib, seaborn
  • Interface: CLI with Python API options
  • Security: Local code execution with sandboxing
  • Documentation: Auto-generated reports in Markdown/HTML

Technology Stack

Python
LlamaIndex
Gemini
GPT-4o
scikit-learn
pandas
numpy
matplotlib
seaborn
lifelines
GitHub
ReadTheDocs

Experience ML Copilot

Try Sample Commands

list files

Show files in current directory

preprocess data.csv target=outcome

Clean and prepare your dataset

train model=random_forest

Train a machine learning model

plot confusion_matrix

Visualize model performance

document

Generate analysis report

Output Preview

$ python -m ml_copilot_agent
ML Copilot Agent initialized with GPT-4o
$ preprocess data.csv target=outcome
Loading data.csv (1425 rows × 28 columns)
Handling missing values: 5 rows dropped
Encoding categorical variables: 3 features one-hot encoded
Normalizing numerical features
Saved preprocessed data to data_preprocessed.csv
$ train model=random_forest
Training Random Forest classifier...
Using 5-fold cross-validation
Best parameters: n_estimators=200, max_depth=10
Validation accuracy: 0.87 ± 0.03
Saved model to models/rf_model.pkl
$ plot confusion_matrix
Generating confusion matrix...
Saved plot to results/confusion_matrix.png
Confusion Matrix
TP: 245 | FP: 32
FN: 28 | TN: 267

Academic Research

Our preprint "ML Copilot: An LLM-Powered Agent for Orchestrating Complex Machine Learning Workflows" is currently under peer review at the Journal of Engineering Artificial Intelligence.

Read Preprint Documentation GitHub Repo

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