AI Readiness Insights

AI Use case

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e5-Data-quality-and-Anomaly-agent

Data Quality & Anomaly Detection Agent

Application: Streamlit Dashboard, Data Profiling & Analysis Agent

AI Frameworks: Python (Pandas, scikit-learn)

AI Category: Data Quality Assessment, Anomaly Detection, Missing Value Detection

AI Platform: Matplotlib/Seaborn for visualization, Optional Cloud Deployment (Azure/AWS/GCP)

Data Profiling: Automatically generate an overview of the dataset structure.

    • Displays column names, data types, missing values, unique counts, and basic statistics (mean, std, min, max).
    • Helps identify data quality issues early in the pipeline.
    • Uses Python, Pandas, and Streamlit to display metrics in an interactive UI.
Data Profiling

Missing Value Detection: Identify incomplete data entries that may affect downstream tasks.


    • Detects and flags columns or rows with missing/null values.
    • Visual representation of missing data using bar charts or heatmaps.
    • Assists in deciding whether to impute or drop data.
Missing Value Detection

Missing Value Detection: Identify incomplete data entries that may affect downstream tasks.


    • Detects and flags columns or rows with missing/null values.
    • Visual representation of missing data using bar charts or heatmaps.
    • Assists in deciding whether to impute or drop data.

Anomaly Detection (Unsupervised): Identify outliers or unusual data points that deviate significantly from the dataset’s norm.

    • Uses Isolation Forest, Local Outlier Factor (LOF), and One-Class SVM to detect anomalies.
    • Suitable for unlabeled datasets.
      Highlights anomalous rows with an anomaly score and visual plot.
Anomaly Detection

Supervised Evaluation (Classification): If a label column is present, evaluate how well the model can classify anomalies.


    • Trains classifiers and validates predictions using accuracy, precision, recall, and confusion matrix.
    • Useful for binary or multi-class anomaly classification scenarios.
    • Powered by scikit-learn models and metric functions.
Supervised Evaluation

Supervised Evaluation (Classification): If a label column is present, evaluate how well the model can classify anomalies.


    • Trains classifiers and validates predictions using accuracy, precision, recall, and confusion matrix.
    • Useful for binary or multi-class anomaly classification scenarios.
    • Powered by scikit-learn models and metric functions.

Supervised Evaluation (Regression): For numerical target columns, detect anomalies based on prediction errors (residuals).


    • Compares predicted vs actual values.
    • Detects outliers in residuals using thresholds or z-scores.
    • Evaluates using RMSE, MAE, and R² metrics.
Supervised Evaluation (Regression)

Data Readiness Reporting: Help analysts or ML engineers validate datasets before modeling.


    • Combines profiling, anomaly flags, missing value reports, and evaluation metrics.
    • Generates a clear picture of whether the dataset is suitable for training or further analysis.
Data Readiness Reporting

Data Readiness Reporting: Help analysts or ML engineers validate datasets before modeling.


    • Combines profiling, anomaly flags, missing value reports, and evaluation metrics.
    • Generates a clear picture of whether the dataset is suitable for training or further analysis.

Fields that can be extracted:

Column Names

Data Types

Missing Value
Counts

Unique Value
Counts

Basic Stats:
Mean, Median, Std,
Min, Max