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ai_data_monitoring_reporing [2025/05/25 16:46] – [Purpose] eagleeyenebulaai_data_monitoring_reporing [2025/05/25 16:50] (current) – [1. Monitoring Data Quality] eagleeyenebula
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 ===== 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:
ai_data_monitoring_reporing.1748191606.txt.gz · Last modified: 2025/05/25 16:46 by eagleeyenebula