tuiml

Using tuiml, load customer_churn.csv, detect the target column churn, preprocess the data, train a classification model, and return accuracy, recall, and a feature importance summary.

A complete classification experiment result with evaluation metrics, key features, and a model performance summary.

Use tuiml to read house_prices.csv, build a regression model with sale_price as the target, compare at least two algorithms, and provide the best model's error metrics and sample predictions.

Regression experiment comparisons, best-model selection rationale, error metrics, and sample predictions.

With tuiml, run clustering on users_behavior.csv, automatically choose an appropriate number of clusters, and output cluster characteristics, sample distribution, and a business-friendly summary.

A clustering report including segment definitions, feature descriptions for each cluster, and actionable analytical conclusions.