Project Structure for Doctor Name Data Cleansing

Objectives

The primary goal of this project is to cleanse the "Doctor Name" column to maintain data integrity and ensure the column solely contains valid human names.

Key Challenges

The following issues have been identified in the current column data:

• Presence of blanks and null values
• Mixed delimiters or delimiter-only entries
• Unwanted symbols and numeric values
• Non-English names, primarily Arabic
• Names associated with hospitals, clinics, diseases, medicines, treatments, or services
• Names beginning with "Dr" or "Dr."
• Unidentified characters (e.g., ??? ?????? ????)

Recommended Structure

1. Data Acquisition

• Source Inspection: Understand how data is sourced and identify the structure.
• Backup: Create a backup of the original data for reference and recovery.

2. Initial Data Exploration

• Examine Data: Use descriptive statistics and visualizations to understand data distribution and frequency of invalid entries.
• Document Findings: Record insights regarding the types and frequencies of erroneous entries.

3. Data Cleansing Workflow

3.1 Data Preprocessing

• Handle Missing Values:

  • Identify blanks and null values; decide on a strategy (e.g., imputation or exclusion).

• Remove Special Characters and Digits:

  • Implement regex to filter out any rows with symbols or digits.

3.2 Filtering Names

• Regular Expressions: Use regex to extract valid human names while excluding non-relevant rows.

  • Example regex pattern: ^[A-Za-z ]+$ to match entries with only alphabets and spaces.

• Remove Entries with Specific Patterns:

  • Filter out names starting with "Dr" or "Dr." using string operations.
  • Exclude names containing hospital/clinic names, symptoms, medicines, and treatments through keyword matching.

• Language Filtering:

  • For non-English names (e.g., Arabic), identify using character ranges or libraries designed for language detection.

• Handle Unidentified Characters:

  • Replace or remove rows with unidentified characters using regex or string replacement methods.

4. Validation and Quality Assurance

• Cross-Validation: Implement a mechanism to cross-verify the cleansed data against an external dataset of valid names, if available.
• Manual Review: Establish a sample review process to confirm the effectiveness of cleansing actions.

5. Data Transformation

• Standardization: Ensure consistent name formatting (e.g., capitalization of first letters).
• Removal of Duplicates: After cleansing, identify and remove duplicate entries.

6. Documentation

• Record Transformation Steps: Maintain a log of all cleansing operations carried out for transparency and reproducibility.
• Create a Report: Summarize the final outcomes and insights gained during the cleansing process.

7. Tools and Technologies

• Programming Languages: Use Python for data manipulation and cleansing with libraries like Pandas and NumPy.
• Data Visualization: Employ libraries such as Matplotlib or Seaborn to visualize data before and after cleansing.

Example Python Snippet for Data Cleansing

import pandas as pd
import re

# Load data
data = pd.read_csv('doctor_names.csv')

# Example cleansing function
def clean_doctor_names(name):
    # Remove blanks and nulls
    if pd.isnull(name) or name.strip() == '':
        return None
    
    # Remove digits and special characters
    name = re.sub(r'[^A-Za-z ]', '', name)
    
    # Remove entries starting with 'Dr' or 'Dr.'
    if name.lower().startswith('dr'):
        return None
    
    # Check for invalid keyword matches
    keywords = ['hospital', 'clinic', 'disease', 'medicine', 'treatment']
    if any(keyword in name.lower() for keyword in keywords):
        return None
    
    # Return cleansed name
    return name.strip().title()

# Apply cleansing
data['Cleaned_Name'] = data['Doctor_Name'].apply(clean_doctor_names)

# Removing rows with None values in 'Cleaned_Name'
cleaned_data = data.dropna(subset=['Cleaned_Name'])

Conclusion

By following the outlined structure, the project will systematically address the various data quality issues present in the "Doctor Name" column. Regular reviews and adjustments will ensure optimal results and maintain the data's relevance and usability. For more advanced methodologies and detailed insights, consider exploring relevant courses on the Enterprise DNA Platform.