Clean, preprocess and perform EDA on tabular data. Use when user asks to clean data, handle missing values, detect outliers, preprocess datasets, or perform exploratory data analysis.
Generate statistical charts and visualizations for ML projects. Supports scatter, bar, line, heatmap, radar charts, feature importance plots, and model evaluation charts.
Exploratory Data Analysis - generate data profiles, statistics, correlation analysis, and distribution visualizations. Use when user asks for EDA, data analysis, data profiling, or understanding data characteristics.
Create maps and spatial visualizations for urban/regional data. Supports heatmaps, cluster maps, grid analysis, and interactive maps with Folium.
Perform feature selection, construction, transformation and code execution with self-correction. Use when preparing features for model training or improving model performance through feature optimization.
Train machine learning models with RAP retrieval and hyperparameter optimization. Use when user wants to train, predict, or build ML models.
Evaluate model performance with multi-stage verification. Use when user wants to validate, test, or evaluate ML models.
Explain model predictions using SHAP values. Use when user wants to understand model behavior, feature importance, or interpretability.