Ensure Data Quality, Spot Anomalies, and Validate Readiness — All in One Click
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.
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Automated data profiling with statistics, types, and missing values
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Visual detection of missing data using heatmaps and bar charts
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Unsupervised anomaly detection using Isolation Forest and LOF
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Supervised evaluation for classification and regression tasks
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Comprehensive data readiness reporting for model validation
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Profiling datasets to assess structure, quality, and readiness for modeling
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Detecting anomalies in unlabeled data for fraud or error detection
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Evaluating prediction accuracy for supervised ML tasks using error metrics
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