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ai_data_detection [2025/05/25 15:01] – [Required Libraries] eagleeyenebulaai_data_detection [2025/05/25 15:09] (current) – [Best Practices] eagleeyenebula
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   * **Missing Value Detection:**   * **Missing Value Detection:**
-    - Identifies any `NaN` or `Null` values present in the dataset.+    - Identifies any **NaN** or **Null** values present in the dataset.
  
   * **Duplicate Row Detection:**   * **Duplicate Row Detection:**
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 ==== Installation ==== ==== Installation ====
 To install the required libraries, use the following: To install the required libraries, use the following:
-```bash+<code> 
 +bash
 pip install pandas pip install pandas
-```+</code>
  
 ---- ----
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 ==== Basic Example ==== ==== Basic Example ====
-Using the `has_issues` method to detect data issues.+Using the **has_issues** method to detect data issues.
  
-```python+<code> 
 +python
 import pandas as pd import pandas as pd
 from ai_data_detection import DataDetection from ai_data_detection import DataDetection
- +</code> 
-# Create a sample dataset+# **Create a sample dataset** 
 +<code>
 data = pd.DataFrame({ data = pd.DataFrame({
     'A': [1, 2, None, 4],     'A': [1, 2, None, 4],
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     'C': [1.1, 2.2, 2.2, None]     'C': [1.1, 2.2, 2.2, None]
 }) })
- +</code> 
-# Create an instance of DataDetection+# **Create an instance of DataDetection** 
 +<code>
 detector = DataDetection() detector = DataDetection()
- +</code> 
-# Check for data issues+# **Check for data issues** 
 +<code>
 if detector.has_issues(data): if detector.has_issues(data):
     print("The dataset has quality issues.")     print("The dataset has quality issues.")
 else: else:
     print("The dataset is clean.")     print("The dataset is clean.")
-``` +</code>
 **Output:** **Output:**
-```plaintext+<code> 
 +plaintext
 WARNING:root:Data contains missing values. WARNING:root:Data contains missing values.
 The dataset has quality issues. The dataset has quality issues.
-```+</code>
  
 ---- ----
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 === 1. Customizing Data Checks === === 1. Customizing Data Checks ===
-You can extend the `DataDetection` class to add checks for other data quality metrics, such as outliers or invalid values.+You can extend the **DataDetection** class to add checks for other data quality metrics, such as outliers or invalid values.
  
 **Example: Adding Outlier Detection** **Example: Adding Outlier Detection**
-```python+<code> 
 +python
 import numpy as np import numpy as np
  
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 if extended_detector.has_outliers(data): if extended_detector.has_outliers(data):
     print("Outliers detected in the dataset.")     print("Outliers detected in the dataset.")
-```+</code>
  
 **Output:** **Output:**
-```plaintext+<code> 
 +plaintext
 WARNING:root:Outliers detected in dataset. WARNING:root:Outliers detected in dataset.
 Outliers detected in the dataset. Outliers detected in the dataset.
-```+</code>
  
 --- ---
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 === 2. Integrating DataDetection into a Pipeline === === 2. Integrating DataDetection into a Pipeline ===
 This module can be integrated as part of a Scikit-learn pipeline for preprocessing. This module can be integrated as part of a Scikit-learn pipeline for preprocessing.
- +<code> 
-```python+python
 from sklearn.pipeline import Pipeline from sklearn.pipeline import Pipeline
  
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     ('model', LogisticRegression())     ('model', LogisticRegression())
 ]) ])
-``` +</code>
 --- ---
  
 === 3. Handling Large Datasets === === 3. Handling Large Datasets ===
 For large datasets, optimize checks using chunk-based processing in Pandas: For large datasets, optimize checks using chunk-based processing in Pandas:
-```python+<code> 
 +python
 def has_issues_in_chunks(file_path, chunk_size=1000): def has_issues_in_chunks(file_path, chunk_size=1000):
     detector = DataDetection()     detector = DataDetection()
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             return True             return True
     return False     return False
-```+</code>
  
 ---- ----
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 ===== Best Practices ===== ===== Best Practices =====
 1. **Use Incremental Checks:** Perform quality checks at different stages of the pipeline (e.g., after loading raw data and after preprocessing steps). 1. **Use Incremental Checks:** Perform quality checks at different stages of the pipeline (e.g., after loading raw data and after preprocessing steps).
 +
 2. **Automate Logging:** Set up centralized logging for tracking data issues across multiple datasets. 2. **Automate Logging:** Set up centralized logging for tracking data issues across multiple datasets.
 +
 3. **Adapt Custom Methods:** Extend the module for domain-specific checks, such as outlier detection, range checks, or invalid category detection. 3. **Adapt Custom Methods:** Extend the module for domain-specific checks, such as outlier detection, range checks, or invalid category detection.
 +
 4. **Handle Issues Early:** Address identified data issues before training machine learning models. 4. **Handle Issues Early:** Address identified data issues before training machine learning models.
  
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 **Example: Adding Invalid Category Detection** **Example: Adding Invalid Category Detection**
-```python+<code> 
 +python
 def has_invalid_categories(data, valid_categories): def has_invalid_categories(data, valid_categories):
     for col in data.select_dtypes(include=['object']):     for col in data.select_dtypes(include=['object']):
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             return True             return True
     return False     return False
-```+</code>
  
 ---- ----
ai_data_detection.1748185277.txt.gz · Last modified: 2025/05/25 15:01 by eagleeyenebula