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ai_edge_case_handling [2025/05/26 15:34] – [Example 1: Validating a Data Source] eagleeyenebulaai_edge_case_handling [2025/05/26 15:37] (current) – [Best Practices] eagleeyenebula
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 ==== Example 2: Handling Missing Values with Mean Strategy ==== ==== Example 2: Handling Missing Values with Mean Strategy ====
  
-The `handle_missing_values()` method allows you to fill missing values in a dataset using the mean of existing values.+The **handle_missing_values()** method allows you to fill missing values in a dataset using the mean of existing values.
  
-```python +<code> 
-# Sample data with missing "value" fields+python 
 +</code> 
 +**Sample data with missing "value" fields** 
 +<code>
 data = [ data = [
     {"id": 1, "value": 10},     {"id": 1, "value": 10},
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     {"id": 3, "value": 30},     {"id": 3, "value": 30},
 ] ]
- +</code> 
-# Handle missing values with the "mean" strategy+**Handle missing values with the "mean" strategy** 
 +<code>
 cleaned_data = EdgeCaseHandler.handle_missing_values(data, strategy="mean") cleaned_data = EdgeCaseHandler.handle_missing_values(data, strategy="mean")
  
 print(f"Cleaned Data: {cleaned_data}") print(f"Cleaned Data: {cleaned_data}")
-``` +</code>
 **Logs & Output:** **Logs & Output:**
- 
---- 
  
 ==== Example 3: Removing Records with Missing Values ==== ==== Example 3: Removing Records with Missing Values ====
  
-Using the `remove` strategy, you can eliminate entries that contain missing values.+Using the **remove** strategy, you can eliminate entries that contain missing values.
  
-```python+<code> 
 +python
 # Handle missing values by removing incomplete records # Handle missing values by removing incomplete records
 cleaned_data = EdgeCaseHandler.handle_missing_values(data, strategy="remove") cleaned_data = EdgeCaseHandler.handle_missing_values(data, strategy="remove")
  
 print(f"Cleaned Data: {cleaned_data}") print(f"Cleaned Data: {cleaned_data}")
-```+</code>
  
 **Logs & Output:** **Logs & Output:**
  
- 
---- 
  
 ==== Example 4: Adding Custom Strategies ==== ==== Example 4: Adding Custom Strategies ====
  
-Extend the `EdgeCaseHandler` class to define custom strategies for handling missing values.+Extend the **EdgeCaseHandler** class to define custom strategies for handling missing values.
  
-```python+<code> 
 +python
 class CustomEdgeCaseHandler(EdgeCaseHandler): class CustomEdgeCaseHandler(EdgeCaseHandler):
     @staticmethod     @staticmethod
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 print(f"Cleaned Data (Custom): {cleaned_data}") print(f"Cleaned Data (Custom): {cleaned_data}")
-```+</code>
  
 **Logs & Output:** **Logs & Output:**
- 
- 
---- 
- 
 ===== Use Cases ===== ===== Use Cases =====
  
 1. **Data Validation Pipelines**: 1. **Data Validation Pipelines**:
-   - Ensure data pipelines are robust to file-system errors, missing files, and unavailable data sources.+   * Ensure data pipelines are robust to file-system errors, missing files, and unavailable data sources.
  
 2. **Preprocessing Missing Features**: 2. **Preprocessing Missing Features**:
-   - Handle missing or incomplete feature values during feature engineering for machine learning models.+   * Handle missing or incomplete feature values during feature engineering for machine learning models.
  
 3. **Data Integrity Debugging**: 3. **Data Integrity Debugging**:
-   - Use extensive logging to identify problematic records or strategies causing anomalies in processing.+   * Use extensive logging to identify problematic records or strategies causing anomalies in processing.
  
 4. **Custom Cleaning Pipelines**: 4. **Custom Cleaning Pipelines**:
-   - Extend the module with domain-specific strategies, such as interpolation or external API lookups, to handle missing information. +   * Extend the module with domain-specific strategies, such as interpolation or external API lookups, to handle missing information.
- +
----+
  
 ===== Best Practices ===== ===== Best Practices =====
  
 1. **Validate Early**: 1. **Validate Early**:
-   - Always validate data sources at the start of your pipeline to avoid unnecessary runtime errors.+   * Always validate data sources at the start of your pipeline to avoid unnecessary runtime errors.
  
 2. **Choose Appropriate Strategies**: 2. **Choose Appropriate Strategies**:
-   - Select missing value handling strategies based on the nature of your data and downstream requirements.+   * Select missing value handling strategies based on the nature of your data and downstream requirements.
  
 3. **Log Everything**: 3. **Log Everything**:
-   - Use logging to track all edge case handling actions for accountability and debugging.+   * Use logging to track all edge case handling actions for accountability and debugging.
  
 4. **Modular Extensions**: 4. **Modular Extensions**:
-   - Extend methods to handle unique edge case scenarios tailored to your domain or application. +   * Extend methods to handle unique edge case scenarios tailored to your domain or application.
- +
----+
  
 ===== Conclusion ===== ===== Conclusion =====
ai_edge_case_handling.1748273685.txt.gz · Last modified: 2025/05/26 15:34 by eagleeyenebula