Data Science Pro

AI-agentic, LLM-augmented data science pipeline for CSV datasets. It performs EDA, preprocessing, modeling, evaluation, and reporting with minimal user code.

Install

pip install -e .
# or when released
# pip install data-science-pro

Verify:

import data_science_pro
from data_science_pro import DataSciencePro

Quickstart (Python API)

Minimal end-to-end run that generates a markdown report:

from data_science_pro import DataSciencePro

dsp = DataSciencePro(api_key="YOUR_OPENAI_KEY")  # or "" to run without LLM/RAG embeddings

# Optional guidance to the agent
dsp.controller.goal = "Predict churn with strong accuracy"
dsp.controller.user_target = "churn"  # optional; if omitted the agent will infer

final = dsp.run("/path/to/data.csv")
print(final.get("report", "No report produced"))

What happens:

• Loads your CSV, performs EDA and indexes summaries (RAG) for retrieval
• Plans and suggests next steps
• Selects/validates the target
• Iterates: preprocess → train → evaluate → critique → improve
• Stops on target metric or max iterations and produces a professional report

Notes:

• When api_key is empty or a test value, RAG uses a local mode and still runs.
• Set dsp.controller.goal (string) to guide the agent. Set dsp.controller.user_target (string) to force the target column.

Beginner-Friendly Example

from data_science_pro import DataSciencePro

dsp = DataSciencePro(api_key="")  # run without external LLM (RAG in local mode)
final = dsp.run("titanic.csv")
print(final["report"])  # Executive summary, EDA, preprocessing, modeling, metrics, recommendations

Advanced Usage (Experienced DS)

Control the agent through state:

from data_science_pro import DataSciencePro

dsp = DataSciencePro(api_key="YOUR_OPENAI_KEY")

# Configure agent objectives
dsp.controller.goal = "Reach accuracy >= 0.90 with interpretable model"
dsp.controller.user_target = None  # let the agent choose from candidates

# (Optional) adjust loop parameters directly by editing controller.run() defaults if needed
state = dsp.run("/data/training.csv")

# Inspect artifacts
print(state.get("analysis"))           # EDA summary
print(state.get("retrieved_context"))  # RAG snippets
print(state.get("evaluation"))         # metrics dict
print(state.get("history"))            # actions taken across iterations

Key Concepts

• Agentic LangGraph

  • Orchestrator decides next step (analyze, preprocess, train, evaluate, report)
  • Planner proposes a concise plan; Critic flags quality issues
  • TargetSelector chooses target via user hint > LLM > heuristic

• RAG over EDA

  • Indexes column and dataset summaries (missingness, cardinality, basic stats)
  • Retrieves relevant context to ground suggestions and reports
  • Uses OpenAI embeddings when api_key is valid; falls back to local mode otherwise

• Reporting

  • Structured markdown report with executive summary, EDA highlights, preprocessing, modeling, metrics, and recommendations

Troubleshooting

• No OpenAI key: set api_key="" to run without external calls; RAG runs in local mode.
• Target not detected: set dsp.controller.user_target = "your_target".
• Healthcheck: run a local end-to-end check without network calls:

python corrected_imports.py

FAQ

• Does it handle large CSVs? Yes, but consider sampling for initial iterations.
• Can I change the model? The agent selects sensible defaults; you can modify Trainer to register alternatives.
• Where is the final report? Returned in final_state["report"].

License

MIT