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ai_data_preparation [2025/05/25 17:46] – [Dependencies] eagleeyenebulaai_data_preparation [2025/05/25 18:13] (current) – [Future Enhancements] eagleeyenebula
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 ====== AI Data Preparation ====== ====== AI Data Preparation ======
-* **[[https://autobotsolutions.com/god/templates/index.1.html|More Developers Docs]]**:+**[[https://autobotsolutions.com/god/templates/index.1.html|More Developers Docs]]**:
 ===== Overview ===== ===== Overview =====
 The **AI Data Preparation** module provides a robust framework for preparing raw datasets for further analysis, feature engineering, and machine learning workflows. It automates common tasks such as cleaning, normalization, and feature preparation, ensuring that data is clean, consistent, and ready for downstream tasks. The **AI Data Preparation** module provides a robust framework for preparing raw datasets for further analysis, feature engineering, and machine learning workflows. It automates common tasks such as cleaning, normalization, and feature preparation, ensuring that data is clean, consistent, and ready for downstream tasks.
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   * **Data Cleaning:**   * **Data Cleaning:**
-    * Removes `None` or invalid entries from raw datasets.+    * Removes **None** or invalid entries from raw datasets.
     * Provides extensibility to define custom cleaning logic.     * Provides extensibility to define custom cleaning logic.
  
   * **Data Normalization:**   * **Data Normalization:**
-    * Scales numerical data to a standard range (e.g., 0 to 1) with Min-Max normalization, improving compatibility with machine learning algorithms.+    * Scales numerical data to a standard range (e.g., **0** to **1**) with Min-Max normalization, improving compatibility with machine learning algorithms.
  
   * **Error Handling and Logging:**   * **Error Handling and Logging:**
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 ==== Required Libraries ==== ==== Required Libraries ====
-  * **`logging`:** For tracking preprocessing activities via logs. +  * **logging:** For tracking preprocessing activities via logs. 
-  * **`pandas` (optional):** For handling structured data in tabular format.+  * **pandas (optional):** For handling structured data in tabular format.
  
 ==== Installation ==== ==== Installation ====
-To install optional dependencies, such as `pandas`, run: +To install optional dependencies, such as **pandas**, run: 
-```bash+<code> 
 +bash
 pip install pandas pip install pandas
-```+</code>
  
 ---- ----
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 Cleansing and normalizing a simple dataset: Cleansing and normalizing a simple dataset:
  
-```python+<code> 
 +python
 from ai_data_preparation import DataPreparation from ai_data_preparation import DataPreparation
- +</code> 
-# Example dataset+# **Example dataset** 
 +<code>
 data = [1, None, 2, 3, None, 5] data = [1, None, 2, 3, None, 5]
- +</code> 
-# Step 1: Clean the dataset+# **Step 1: Clean the dataset** 
 +<code>
 cleaned_data = DataPreparation.clean_data(data) cleaned_data = DataPreparation.clean_data(data)
 print("Cleaned Data:", cleaned_data) print("Cleaned Data:", cleaned_data)
- +</code> 
-# Step 2: Normalize the cleaned dataset+# **Step 2: Normalize the cleaned dataset** 
 +<code>
 normalized_data = DataPreparation.normalize_data(cleaned_data) normalized_data = DataPreparation.normalize_data(cleaned_data)
 print("Normalized Data:", normalized_data) print("Normalized Data:", normalized_data)
-```+</code>
  
 **Output:** **Output:**
-```plaintext+<code> 
 +plaintext
 Cleaned Data: [1, 2, 3, 5] Cleaned Data: [1, 2, 3, 5]
 Normalized Data: [0.0, 0.25, 0.5, 1.0] Normalized Data: [0.0, 0.25, 0.5, 1.0]
-```+</code>
  
 ---- ----
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 === 1. Min-Max Normalization Extension === === 1. Min-Max Normalization Extension ===
-Extend the `normalize_data` method to specify custom normalization ranges.+Extend the **normalize_data** method to specify custom normalization ranges.
  
-```python+<code> 
 +python
 class ExtendedNormalization(DataPreparation): class ExtendedNormalization(DataPreparation):
     @staticmethod     @staticmethod
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 custom_range_data = ExtendedNormalization.normalize_data([10, 20, 30], new_min=-1, new_max=1) custom_range_data = ExtendedNormalization.normalize_data([10, 20, 30], new_min=-1, new_max=1)
 print("Normalized Data with Custom Range:", custom_range_data) print("Normalized Data with Custom Range:", custom_range_data)
-```+</code>
  
 **Output:** **Output:**
-```plaintext+ 
 +<code> 
 +plaintext
 Normalized Data with Custom Range: [-1.0, 0.0, 1.0] Normalized Data with Custom Range: [-1.0, 0.0, 1.0]
-```+</code>
  
 --- ---
  
 === 2. Custom Cleaning Rules === === 2. Custom Cleaning Rules ===
-Modify the `clean_data` method to remove additional invalid entries, such as negative values.+Modify the **clean_data** method to remove additional invalid entries, such as negative values.
  
-```python+<code> 
 +python
 class ExtendedCleaning(DataPreparation): class ExtendedCleaning(DataPreparation):
     @staticmethod     @staticmethod
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 cleaned_data = ExtendedCleaning.clean_data([1, None, -3, 4, -2, 5]) cleaned_data = ExtendedCleaning.clean_data([1, None, -3, 4, -2, 5])
 print("Cleaned Data with Custom Rules:", cleaned_data) print("Cleaned Data with Custom Rules:", cleaned_data)
-```+</code>
  
 **Output:** **Output:**
-```plaintext+<code> 
 +plaintext
 Cleaned Data with Custom Rules: [1, 4, 5] Cleaned Data with Custom Rules: [1, 4, 5]
-```+</code>
  
 --- ---
  
 === 3. Integration with Scikit-learn Pipelines === === 3. Integration with Scikit-learn Pipelines ===
-Integrate the `DataPreparation` module into a Scikit-learn pipeline for end-to-end preprocessing.+Integrate the **DataPreparation** module into a Scikit-learn pipeline for end-to-end preprocessing.
  
-```python+<code> 
 +python
 from sklearn.pipeline import Pipeline from sklearn.pipeline import Pipeline
  
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 processed_data = pipeline.fit_transform(data) processed_data = pipeline.fit_transform(data)
 print("Processed Data via Pipeline:", processed_data) print("Processed Data via Pipeline:", processed_data)
-```+</code>
  
 **Output:** **Output:**
-```plaintext+<code> 
 +plaintext
 Processed Data via Pipeline: [0.0, 0.6666666666666666, 1.0] Processed Data via Pipeline: [0.0, 0.6666666666666666, 1.0]
-```+</code>
  
 ---- ----
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 ===== Best Practices ===== ===== Best Practices =====
 1. **Analyze Data Before Preparation:** 1. **Analyze Data Before Preparation:**
-   - Inspect datasets for unique issues (e.g., outliers) before applying generalized cleaning rules.+   - Inspect datasets for unique issues (e.g., **outliers**) before applying generalized cleaning rules.
  
 2. **Normalize for ML Algorithms:** 2. **Normalize for ML Algorithms:**
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 The **DataPreparation** module can be extended for advanced preprocessing tasks: The **DataPreparation** module can be extended for advanced preprocessing tasks:
   * **Custom Outlier Removal:**   * **Custom Outlier Removal:**
-    - Add logic to discard outliers based on statistical bounds (e.g., Z-scores, IQR).+    - Add logic to discard outliers based on statistical bounds (e.g., **Z-scores, IQR**).
   * **Feature Engineering:**   * **Feature Engineering:**
     - Extract derived metrics from datasets, such as mean, variance, or ratios.     - Extract derived metrics from datasets, such as mean, variance, or ratios.
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 ===== Future Enhancements ===== ===== Future Enhancements =====
 +
 The following additions would extend the functionality of the module: The following additions would extend the functionality of the module:
-  1. **Support for Tabular Data:** + 
-     - Add preprocessing for structured/tabular data via `pandas`. +- **Support for Tabular Data**   
-  2. **Advanced Scaling Options:** +  Add preprocessing for structured/tabular data via **pandas**. 
-     - Support other normalization techniques like Z-score scaling or logarithmic transformations. + 
-  3. **Distributed Processing:** +- **Advanced Scaling Options**   
-     - Enable parallelized data preparation for large datasets using frameworks like Dask.+  Support additional normalization techniques such as **Z-score** scaling or logarithmic transformations. 
 + 
 +- **Distributed Processing**   
 +  Enable parallelized data preparation for large datasets using frameworks like Dask. 
  
 ---- ----
  
 ===== Conclusion ===== ===== Conclusion =====
-The **`AI Data Preparation`** module provides easy-to-use and extensible tools for preparing datasets for machine learning pipelines and data workflows. With its robust cleaning, normalization, and logging capabilities, it simplifies the often tedious data preprocessing steps essential for successful AI/ML projects. Users can extend and customize its functionality to suit domain-specific needs, ensuring flexibility and scalability.+The **AI Data Preparation** module provides easy-to-use and extensible tools for preparing datasets for machine learning pipelines and data workflows. With its robust cleaning, normalization, and logging capabilities, it simplifies the often tedious data preprocessing steps essential for successful AI/ML projects. Users can extend and customize its functionality to suit domain-specific needs, ensuring flexibility and scalability.
ai_data_preparation.1748195190.txt.gz · Last modified: 2025/05/25 17:46 by eagleeyenebula