AutoDS Copilot

An AI data-science assistant. Point it at a spreadsheet and it walks the whole machine-learning workflow for you, exploring the data, cleaning it, comparing dozens of models, training the best one, and explaining the result in plain language. It runs locally, and the same engine is exposed three ways: a web console, an autonomous agent, and a set of tools any AI client can call.

No notebooks, no setup. Upload a CSV and you get a trained model, an interactive dashboard, a written analysis, and the ability to score new data.

Screenshots

The web console walks the whole workflow, from an uploaded CSV to a trained model.

The interactive dashboard, with Overview, Data analysis, Model, and Insights tabs.

The cross-validated model leaderboard, every candidate ranked on your data.

The self-writing analysis report, the signature output.

Related MCP server: MCP Data Science

What it does

It runs the full lifecycle and pauses for your approval at the decisions that matter.

1. Load a CSV, Excel, TSV, or JSON file.

2. Pre-clean, a light mechanical pass that fixes representation only, trims whitespace, turns placeholder tokens into real missing values, parses dates stored as text, converts numeric text, and drops duplicate rows. Every change is logged. It never imputes or removes outliers, so exploration stays honest.

3. Explore with a varied set of seaborn charts, each with the code behind it and a plain-language reading.

4. Data checks flag the silent mistakes before you model, target leakage, constant or ID-like columns, duplicates, high cardinality, and outliers.

5. Detect the task on its own, classification, regression, clustering, time series, or text.

6. Recommend cleaning and wait for your approval.

7. Compare models on a cross-validated leaderboard, or build an ensemble of the best ones.

8. Train and evaluate with task-appropriate metrics, diagnostic plots, feature importance, and honest cross-validation.

9. Tune the winner, which actually retrains and keeps the improved model.

10. Explain, a self-written analysis report and an interactive dashboard.

11. Use it, score new data, download predictions as CSV, or export the model as a bundle with a ready-to-run code snippet.

The algorithm hub

Thirty-two models built in, and thirty-six with the optional boosting libraries.

• Classification (11): Logistic Regression, Ridge, Naive Bayes, KNN, Decision Tree, Random Forest, Extra Trees, AdaBoost, Gradient Boosting, SVM, MLP.

• Regression (12): Linear, Ridge, Lasso, ElasticNet, KNN, Decision Tree, Random Forest, Extra Trees, AdaBoost, Gradient Boosting, SVR, MLP.

• Clustering (9): KMeans, MiniBatchKMeans, Agglomerative, Spectral, Birch, Gaussian Mixture, DBSCAN, MeanShift, OPTICS.

• XGBoost and LightGBM join automatically when installed.

• Time series reuses the regression models on lag and calendar features; text reuses the classification models on TF-IDF.

For any supervised task it cross-validates every candidate, ranks them, tunes the winner, ensembles the best, and exports the fitted pipeline.

RAG, the reasoning layer

• A curated knowledge base of data-science guidance, retrieved to ground the insights, with the source cited.

• Experience retrieval, every run is fingerprinted, and new runs retrieve the most similar past datasets to recommend by analogy. The tool learns from use.

• RAG-driven recommendations, the cleaning and model choices are justified with retrieved guidance.

• A grounded Ask panel, ask any question and get an answer built only from retrieved material, with the sources shown.

Three ways to run it

cd AutoDS_Copilot
python3 -m pip install -r requirements.txt

# 1. Web console  ->  open http://127.0.0.1:5050
python3 run.py

# 2. Autonomous agent (LangGraph, human-in-the-loop from the terminal)
python3 -m autods.agents.graph data/sample_customers.csv --target churned
python3 -m autods.agents.graph data/sample_customers.csv --target churned --auto

# 3. MCP server (drive it from any MCP client, e.g. Claude Desktop)
python3 -m autods.mcp_server

Bundled sample datasets: sample_customers.csv (classification / regression), sample_timeseries.csv (forecasting), sample_reviews.csv (text), sample_imbalanced.csv (rare-class classification).

Note on the port: 5050 is used because macOS AirPlay Receiver occupies 5000. Override with PORT=8000 python3 run.py.

Tests

An offline pytest suite covers the engine, the leakage-safe pipeline, task detection, the quality guardrails, the leaderboard, the RAG retriever, and the agent graph. It needs no API key.

python3 -m pip install pytest
python3 -m pytest -q

Where each skill lives

Connect the MCP server to Claude Desktop

See MCP_SETUP.md. In short, install the requirements, add the block from mcp_config.example.json to your Claude Desktop config, and restart.

Project layout

AutoDS_Copilot/
├── run.py                  # launch the web app
├── mcp_launch.py           # launch the MCP server
├── autods/
│   ├── config.py
│   ├── pipeline/           # the ML engine
│   │   ├── loader.py       preclean.py   task_detect.py  eda.py
│   │   ├── quality.py      preprocess.py model_select.py train.py
│   │   ├── evaluate.py     leaderboard.py insights.py    report.py
│   ├── rag/                # knowledge base + retrieval + experience + ask
│   ├── agents/             # LangGraph orchestration
│   ├── mcp_server.py       # MCP tools
│   └── web/                # Flask console, dashboard, report, api
└── data/                   # sample datasets

Design decisions worth knowing

• No data leakage. All preprocessing lives inside an sklearn Pipeline fit only on the training split.

• Human-in-the-loop. The tool recommends, you approve, at cleaning and model choice.

• Trustworthy evaluation. Cross-validation on the final model, and tuning that actually retrains and replaces the model.

• Grounded, not hallucinated. Insights and answers are backed by retrieval, and everything works with zero API keys because the LLM step is optional.

• Time series done right. The differenced target plus lag and calendar features, and a time-ordered split.

Requirements

Python 3.10+, pandas, numpy, scikit-learn, matplotlib, seaborn, joblib, Flask, langgraph, mcp. Optional: xgboost, lightgbm, and an OpenAI key for richer wording.