No-Code Machine Learning
Build and compare predictive models without writing code.
Upload a CSV and let the AutoML engine engineer useful features, compare advanced algorithms, tune the strongest candidates, test an ensemble, and validate the winner on unseen data.
The upgraded engine can use Random Forest, Extra Trees, XGBoost, LightGBM, adaptive hyperparameter tuning, soft-voting ensembles, and threshold optimization while preserving an untouched final test set.
1
Upload your dataset
Drop a CSV here
or select a file from your computer
2
Choose what you want to predict
The target is the outcome you want the model to learn. Examples: churn, house price, fraud, purchase outcome, or customer category.
3
Train & compare models
CSV inspection happens in your browser. When possible, the browser losslessly compresses the CSV before sending it to the hosted analysis API. Compression does not remove rows, columns, or precision. Do not upload confidential, regulated, medical, financial-account, or other sensitive personal data.
4
Review your results
Best Generalizing Tested Model
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Selected using measured cross-validation performance and checked against unseen holdout data.
AutoML Optimization
Optimization details will appear here.
Model Trust Center
Trust diagnostics will appear here.
Elite Model Diagnostics
Advanced training details will appear here.
Results Explained
What do these numbers mean?
The platform separates predictive performance from model trust. A very high accuracy score is useful only when the validation design is also trustworthy.
Cross-validationCandidate models are tested across multiple folds. Large datasets use a representative training subset to reduce compute while preserving an untouched holdout set.
Holdout validationAfter model comparison, the winning model is retrained on the full training split and evaluated on data that was never used for training or model selection.
Smart optimizationMemory optimization, automatic feature engineering, advanced boosting models, hyperparameter search, ensembling, threshold tuning, and adaptive sampling improve the chance of stronger generalization.