Differences

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--- ai_data_monitoring_reporing [2025/05/25 16:47] – [How It Works] eagleeyenebula
+++ ai_data_monitoring_reporing [2025/05/25 16:50] (current) – [1. Monitoring Data Quality] eagleeyenebula
@@ Line 49: / Line 49: @@
   * **Data Monitoring Tools:**
-    - Detect missing values and calculate dataset coverage (% completeness).
+. Detect missing values and calculate dataset coverage (% completeness).
   * **Flexible Report Generation:**
-    - Automated string-based summary reports for processed datasets or workflows.
+. Automated string-based summary reports for processed datasets or workflows.
   * **Detailed Logging:**
-    - Logs all actions, including data quality checks and report generation results, for thorough traceability.
+. Logs all actions, including data quality checks and report generation results, for thorough traceability.
   * **Integration-Ready:**
-    - Easily integrates into existing pipelines as a monitoring or reporting component.
+. Easily integrates into existing pipelines as a monitoring or reporting component.
   * **Customizable Reporting Templates:**
-    - Can be extended to generate reports in various formats like JSON, HTML, or Markdown.
+. Can be extended to generate reports in various formats like JSON, HTML, or Markdown.
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@@ Line 75: / Line 75: @@
     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
+  * Pass data into **monitor_data_quality** to receive a structured dictionary containing monitored results  (e.g., missing value count, coverage percentage).
-    (e.g., missing value count, coverage percentage).
+  * Use **generate_report** to create a human-readable string report based on the findings or processed data.
-  - Use **generate_report** to create a human-readable string report based on the findings or processed data.
 ==== 1. Monitoring Data Quality ====
@@ Line 85: / Line 84: @@
   * **Missing Data:** Identifies **None** or **NaN** values in 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: