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ai_data_validation [2025/05/25 20:03] – [AI Data Validation] eagleeyenebulaai_data_validation [2025/05/25 20:10] (current) – [Extensions & Best Practices] eagleeyenebula
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 This document elaborates on the functionality of **AI Data Validation**, its core processes, advanced use cases, integration details from the template, and additional best practices. The examples and instructions provided here are aimed to facilitate developers in getting the most out of this system. This document elaborates on the functionality of **AI Data Validation**, its core processes, advanced use cases, integration details from the template, and additional best practices. The examples and instructions provided here are aimed to facilitate developers in getting the most out of this system.
- 
---- 
- 
 ===== Core Functionalities ===== ===== Core Functionalities =====
  
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     - The corresponding HTML templates display validation summaries, statistics, and reports, offering integration for UI/UX systems and reporting dashboards.     - The corresponding HTML templates display validation summaries, statistics, and reports, offering integration for UI/UX systems and reporting dashboards.
  
----+===== DataValidation.py Class Documentation =====
  
-===== `DataValidation.py` Class Documentation ===== +The **DataValidation** class is the backbone of this system. It includes a static method **validate** that performs all logic for checking data consistency and logging the results.
- +
-The **`DataValidation`** class is the backbone of this system. It includes a static method `validate` that performs all logic for checking data consistency and logging the results.+
  
 === Class Design === === Class Design ===
  
-```python+<code> 
 +python
 import logging import logging
  
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         logging.info("Data validation passed.")         logging.info("Data validation passed.")
         return True         return True
-```+</code> 
  
 Key Points: Key Points:
  
 1. **Logging Integration**: 1. **Logging Integration**:
-    - Provides `INFO` logs on successful validation. +    - Provides **INFO** logs on successful validation. 
-    - Returns `ERROR` logs if data is empty or contains null values.+    - Returns **ERROR** logs if data is empty or contains null values.
  
 2. **Validation Rules**: 2. **Validation Rules**:
     - Checks if data is non-empty.     - Checks if data is non-empty.
-    - Scans for `None` values in the dataset.+    - Scans for **None** values in the dataset.
  
 3. **Modular**: 3. **Modular**:
     - The static method format ensures compatibility when extending or subclassing.     - The static method format ensures compatibility when extending or subclassing.
  
---- 
  
-===== Template Integration ===== 
- 
-The accompanying HTML template (`ai_data_validation.html`) is designed to integrate results from the validation system into web dashboards or front-end applications. It provides a framework to display validation results in real-time, including warnings, statistics, and visual feedback. 
- 
-=== Sample Template Structure === 
- 
-```html 
-<!DOCTYPE html> 
-<html> 
-<head> 
-    <title>Data Validation Results</title> 
-</head> 
-<body> 
-    <h1>Data Validation Summary</h1> 
-    <div id="validation-status"> 
-        Validation Status: <span style="color: green;">Passed</span> 
-    </div> 
-    <div id="validation-errors"> 
-        <h2>Validation Errors:</h2> 
-        <ul> 
-            <li>No errors detected.</li> 
-        </ul> 
-    </div> 
-    <footer> 
-        <p>Generated by AI Data Validation System</p> 
-    </footer> 
-</body> 
-</html> 
-``` 
- 
-Key Features of Template: 
-  * **Dynamic Status Update**: Displays whether the validation passed or failed using a clear color-coded visual. 
-  * **Error Reporting**: Lists all encountered validation errors (if any). 
-  * **Extensibility**: Can be dynamically updated via backend values using Flask, Django, or other frameworks. 
- 
---- 
  
 ===== Advanced Usage Examples ===== ===== Advanced Usage Examples =====
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 Expand the basic validation to enforce uniform data type rules. For example, ensuring all elements are integers: Expand the basic validation to enforce uniform data type rules. For example, ensuring all elements are integers:
- +<code> 
-```python+python
 class DataTypeValidation(DataValidation): class DataTypeValidation(DataValidation):
     @staticmethod     @staticmethod
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 if not DataTypeValidation.validate(data): if not DataTypeValidation.validate(data):
     print("Failed Validation: Non-integer found.")     print("Failed Validation: Non-integer found.")
-```+</code>
  
 ==== 2. Threshold-based Validation ==== ==== 2. Threshold-based Validation ====
  
 Check if numeric data values lie within a specific range: Check if numeric data values lie within a specific range:
- +<code> 
-```python+python
 class ThresholdValidation(DataValidation): class ThresholdValidation(DataValidation):
     @staticmethod     @staticmethod
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 if not ThresholdValidation.validate(data, 0, 100): if not ThresholdValidation.validate(data, 0, 100):
     print("Failed Validation: Data out of acceptable range.")     print("Failed Validation: Data out of acceptable range.")
-```+</code>
  
 ==== 3. JSON Schema Validation ==== ==== 3. JSON Schema Validation ====
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 For structured datasets, integrate JSON schema validation using libraries like `jsonschema`: For structured datasets, integrate JSON schema validation using libraries like `jsonschema`:
  
-```python+<code> 
 +python
 import jsonschema import jsonschema
 from jsonschema import validate from jsonschema import validate
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             logging.error(f"Schema validation failed: {err}")             logging.error(f"Schema validation failed: {err}")
             return False             return False
 +</code>
 # Sample JSON and Schema # Sample JSON and Schema
 +<code>
 data = {"name": "John", "age": 30} data = {"name": "John", "age": 30}
 schema = { schema = {
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 if JsonSchemaValidation.validate(data, schema): if JsonSchemaValidation.validate(data, schema):
     print("JSON Schema Validated Successfully")     print("JSON Schema Validated Successfully")
-``` +</code>
- +
---- +
 ===== Extensions & Best Practices ===== ===== Extensions & Best Practices =====
  
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   * Use detailed logging to ensure traceability.   * Use detailed logging to ensure traceability.
   * Modularize validation logic for usability across pipelines.   * Modularize validation logic for usability across pipelines.
- 
---- 
  
 ===== Conclusion ===== ===== Conclusion =====
  
 The **AI Data Validation** system is both flexible and powerful, enabling basic to advanced data integrity checks. Its integration into web-based systems and extensibility make it an essential component in data pipelines. The **AI Data Validation** system is both flexible and powerful, enabling basic to advanced data integrity checks. Its integration into web-based systems and extensibility make it an essential component in data pipelines.
ai_data_validation.1748203428.txt.gz · Last modified: 2025/05/25 20:03 by eagleeyenebula