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Describe Your Problem

Enter your machine learning problem or dataset goals below and click 'Analyze & Recommend' to generate server-side Gemini AI model suggestions.

How this works
  1. Type or paste your project goal or problem description in plain text (up to 750 words).
  2. Click "Analyze & Recommend" to send your problem statement to backend Gemini AI.
  3. You will automatically navigate to Model Guidance to review recommended model algorithms.
Problem Statement
0 / 750 words
Describe → AI Analysis

Model Guidance

Review AI model evaluations and click 'Make this simpler' to ELI5 technical jargon into plain language, or hover terms for definitions.

How this works
  1. Review the top recommended machine learning algorithms selected specifically for your problem statement.
  2. Click "Make this simpler" repeatedly — each click makes the explanation noticeably simpler down to an ELI5 child analogy.
  3. Hover over highlighted technical terms (dotted purple underline) for instant glossary definitions.
AI Model Recommendation Summary
Submit a problem description on the Describe page to generate custom AI recommendations.
Recommended Model Family

Dataset Cleaning

Upload your raw tabular dataset and click 'Run Automated Cleaning' to impute missing values, drop duplicates, and view an audit timeline.

How this works
  1. Select a dataset file (.csv, .json, .tsv, .parquet, .txt) from your local system.
  2. Click "Run Automated Cleaning" to trigger server-side Pandas data quality transforms.
  3. Review clean row counts, quality index score, and step-by-step audit logs.
Upload Dataset for Cleaning
Ready to Clean Your Data
Select a dataset file above to upload it to backend storage. Once uploaded, automated cleaning will handle missing values, duplicates, and outliers.

Dataset Supply

Browse curated baseline datasets below and click 'Download' to stream raw files directly to your device, or upload custom datasets in Admin mode.

How this works
  1. Search or filter pre-loaded machine learning datasets (Iris, Wine, Breast Cancer, Diabetes, etc.).
  2. Click "Download" to stream raw CSV files straight to your computer.
  3. Use the Admin Upload form below (or drop files into data-library/ on server) to add new datasets.
+ Admin: Add New Dataset to Curated Supply Library

Testing Framework

Select a model estimator (or upload your custom model file) and click 'Execute Testing Pipeline' to evaluate performance and view accuracy/R² metrics.

How this works
  1. Optionally upload your own custom model artifact (.py, .js, .pkl, .html) using the box below.
  2. Select an estimator algorithm and specify the target task type (Classification vs. Regression).
  3. Click "Execute Testing Pipeline" to run model evaluation and inspect metrics and logs.

You can input models of types - .py/.js/.pkl/.html

Evaluation Config

Training Framework

Upload a trained model file, configure cross-validation folds, then click 'Start Model Training' to execute model training and inspect performance logs.

How this works
  1. Optionally upload your own custom model artifact (.py, .js, .pkl, .html).
  2. Choose cross-validation split strategy (5-fold vs 10-fold CV).
  3. Click "Start Model Training" to execute fitting loops and monitor execution progress.

You can input models of types - .py/.js/.pkl/.html

Training Options