Data Cleaning Techniques

Data cleaning prepares raw datasets for analysis by fixing errors, inconsistencies, and missing values. It’s a critical step in data science. This article covers common issues, techniques, an example, and its importance.

Common Issues

• Missing Values: Gaps in data (e.g., NULLs).
• Duplicates: Repeated entries.
• Inconsistencies: Mismatched formats (e.g., “USA” vs. “U.S.”).
• Outliers: Extreme values skewing results.

Cleaning Techniques

• Imputation: Fill missing values (e.g., mean: \( \bar{x} \)).
• Deduplication: Remove repeats.
• Standardization: Uniform formats (e.g., dates as YYYY-MM-DD).
• Outlier Detection: Z-score (\( z = \frac{x - \mu}{\sigma} \)) to flag anomalies.

Example Process

Dataset: {Age: 25, NULL, 30, 25, 100}:

• Missing: Impute NULL with mean (45 → 30).
• Duplicates: Remove extra 25.
• Outlier: 100 has \( z > 3 \), drop or investigate.

Cleaned: {25, 30, 30}.

Applications

Essential for:

• Machine Learning: Reliable training data.
• Business: Accurate sales reports.
• Research: Valid statistical conclusions.

Ensures data quality.