Data Preprocessing and Quality Assurance

CONTEXT
In data analysis, preprocessing and data cleaning are crucial steps that ensure the quality and integrity of your datasets. These processes involve identifying and correcting errors, handling missing values, and standardizing formats.

OBJECTIVE
The goal is to provide a streamlined approach for cleaning data by focusing on key quality assurance techniques to enhance the usability of the dataset for analysis.

FORMAT
Please provide detailed steps or methods for performing data preprocessing and sanitization. Include examples of common data quality issues and suggested fixes.

EXAMPLES
• Remove duplicates from the dataset. • Fill in missing values with the mean or median. • Standardize date formats across the dataset. • Validate and sanitize string fields to eliminate special characters.