The Librarian
Ensure Data Quality, Spot Anomalies, and Validate Readiness — All in One Click
DataQuality&Anomaly Detection Agent Overview
This AI agent helps data scientists and analysts assess and prepare datasets efficiently. It automatically profiles data, detects missing values, identifies anomalies (unsupervised or supervised), and evaluates dataset readiness for modeling. Whether you're working with classification or regression tasks, the agent delivers detailed metrics including confusion matrices, residual plots, and performance scores. Built with Python, Pandas, Scikit-learn, and Streamlit, it provides an interactive UI for instant data quality insights and decision-making.
DataQuality&Anomaly Detection Agent Key Features
Automated data profiling with statistics, types, and missing values
Visual detection of missing data using heatmaps and bar charts
Unsupervised anomaly detection using Isolation Forest and LOF
Supervised evaluation for classification and regression tasks
Comprehensive data readiness reporting for model validation
DataQuality&Anomaly Detection Agent Use Cases
Profiling datasets to assess structure, quality, and readiness for modeling
Detecting anomalies in unlabeled data for fraud or error detection
Evaluating prediction accuracy for supervised ML tasks using error metrics
Pricing
Free
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