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ai_data_monitoring_reporing [2025/05/25 16:38] – [Extensibility] eagleeyenebulaai_data_monitoring_reporing [2025/05/25 16:50] (current) – [1. Monitoring Data Quality] eagleeyenebula
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 ===== Purpose ===== ===== Purpose =====
-The **ai_data_monitoring_reporing.py** module was designed to: 
  
-1. Provide clear visibility into the quality and state of any dataset being processed.+The **ai_data_monitoring_reporting.py** module was designed to:
  
-2. Automatically log and summarize findings in standard formats for debugging or documentation purposes. +  - Provide clear visibility into the quality and state of any dataset being processed. 
- +  - Automatically log and summarize findings in standard formats for debugging or documentation purposes. 
-3. Allow teams to make informed decisions regarding data preprocessing, cleaning, and curation. +  - Allow teams to make informed decisions regarding data preprocessing, cleaning, and curation. 
- +  - Improve compliance and data documentation by maintaining records of dataset transformations in pipelines.
-4. Improve compliance and data documentation by maintaining records of dataset transformations in pipelines.+
  
 By summarizing both issues and progress, this module is an essential tool for pipeline observability and governance. By summarizing both issues and progress, this module is an essential tool for pipeline observability and governance.
  
-----+===== Key Features =====
  
-===== Key Features ===== 
 The **DataMonitoringReporting** module includes the following core features: The **DataMonitoringReporting** module includes the following core features:
  
-  * **Data Monitoring Tools:**+  * **Data Monitoring Tools:**  
     1. Detect missing values and calculate dataset coverage (% completeness).     1. Detect missing values and calculate dataset coverage (% completeness).
  
-  * **Flexible Report Generation:**+  * **Flexible Report Generation:**  
     2. Automated string-based summary reports for processed datasets or workflows.     2. Automated string-based summary reports for processed datasets or workflows.
  
-  * **Detailed Logging:**+  * **Detailed Logging:**  
     3. Logs all actions, including data quality checks and report generation results, for thorough traceability.     3. Logs all actions, including data quality checks and report generation results, for thorough traceability.
  
-  * **Integration-Ready:**+  * **Integration-Ready:**  
     4. Easily integrates into existing pipelines as a monitoring or reporting component.     4. Easily integrates into existing pipelines as a monitoring or reporting component.
  
-  * **Customizable Reporting Templates:**+  * **Customizable Reporting Templates:**  
     5. Can be extended to generate reports in various formats like JSON, HTML, or Markdown.     5. Can be extended to generate reports in various formats like JSON, HTML, or Markdown.
  
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 The **DataMonitoringReporting** class provides two core methods: The **DataMonitoringReporting** class provides two core methods:
  
-1. **monitor_data_quality(data):**  +  * **monitor_data_quality(data):**   
- +    Monitors the quality of a dataset by calculating the total number of data points, missing values, and the completeness percentage.
-This monitors the quality of a dataset by calculating the total number of data points, missing values, and the completeness percentage. +
- +
-2. **generate_report(data):** +
  
-This generates a textual summary of the processed dataset.+  * **generate_report(data):**   
 +    Generates a textual summary of the processed dataset.
  
-The workflow is as follows: +**The workflow is as follows:**
-  * Pass data into  +
-  * **monitor_data_quality** +
  
-To receive a structured dictionary containing monitored results (e.g., missing value count, coverage percentage). Use **generate_report** to create a human-readable string report based on the findings or processed data.+  * Pass data into **monitor_data_quality** to receive a structured dictionary containing monitored results  (e.g., missing value count, coverage percentage). 
 +  * Use **generate_report** to create a human-readable string report based on the findings or processed data.
  
 ==== 1. Monitoring Data Quality ==== ==== 1. Monitoring Data Quality ====
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   * **Missing Data:** Identifies **None** or **NaN** values in the dataset.   * **Missing Data:** Identifies **None** or **NaN** values in the dataset.
   * **Total Data Points:** Counts the overall size of the dataset.   * **Total Data Points:** Counts the overall size of the dataset.
-  * **Coverage Percentage:** Calculates the completeness of the dataset as **(Total Values - Missing Values) / Total Values * 100**.+  * **Coverage Percentage:** Calculates the completeness of the dataset as **(Total Values - Missing Values) / Total Values 100**.
  
 The output is a dictionary summarizing quality statistics: The output is a dictionary summarizing quality statistics:
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 2. **Understand Coverage:** 2. **Understand Coverage:**
-   - Aim for high coverage (`>90%`) whenever possible. Use imputation methods for lower coverage levels.+   - Aim for high coverage (**>90%**) whenever possible. Use imputation methods for lower coverage levels.
  
 3. **Customize Reports for Stakeholders:** 3. **Customize Reports for Stakeholders:**
ai_data_monitoring_reporing.1748191106.txt.gz · Last modified: 2025/05/25 16:38 by eagleeyenebula