Autonomous Data Science Agent

An intelligent computational data scientist that investigates raw data, inspects data hygiene, forms statistical hypotheses, trains validated ML pipelines, audits leakage, and generates actionable decision dossiers.

1. Formulate Business Problem & Investigative Objective

Suggested Prompts: 📉 Telecom Churn Drivers 🏠 Housing Valuations 📊 Marketing Attribution 👥 Customer Segmentation

Drop your custom CSV dataset here, or click to browse

Supports files up to 50MB. Processed with Python & Scikit-Learn.

2. Or Load Curated Real-World Benchmark Datasets

Dataset Intelligence & Profiling

Automated statistical profiling and semantic variable type discovery.

Semantic Column Profiles

Column Name	Semantic Type	Data Type	Missing %	Unique Values	Sample Values

Formulated Problem Opportunities

Data Quality & Hygiene Audit

Automated audit of missingness, extreme outliers, target leakage, and anomalies.

Manual Imputation & Cleaning Tools

Active Data Preview

Showing first 100 rows

Intelligent Exploratory Data Analysis

Automated visual inspections with scientific takeaways and anomaly flags.

Interactive Chart Visualizer

Formulated Scientific Hypotheses

Statistical hypothesis testing with p-values, effect sizes, and plain-language interpretations.

Pearson & Spearman Correlation Matrix

Symmetric matrix indicating linear associations and collinearity risks.

Strongest Pairwise Associations

Domain-Aware Feature Engineering

Automated detection of business domains and intelligent feature generation.

Domain: General

Automated Machine Learning & Benchmarking

Baseline-first evaluation, cross-validation, and multi-model leaderboard.

Target Column (Y)

Problem Formulation

Algorithm

Model Leaderboard vs Baseline

Model Name	Accuracy / R²	Precision / MAE	Recall / RMSE	F1 / MAPE	CV Score (5-Fold)	Status

Top Feature Importances

Interactive Prediction Simulator

Critic Agent & Statistical Integrity Audit

Strict verification of target leakage, sample size constraints, cross-validation stability, and overfitting.

Audit Verdict: PASS

No critical integrity flaws detected in model formulation.

Audited Potential Vulnerabilities

Model Explainability & Decision Logic

Multi-persona transparency reports and feature impact breakdowns.

Executive Summary

Model has been trained. The primary drivers are highlighted above. Select different personas to view technical formulations.

Unsupervised Segment & Persona Discovery

K-Means clustering identifying natural behavioral cohorts and distinct sub-populations.