Lesson 3.3 – Data Cleaning Techniques | Handling Missing Values, Duplicates & Outliers

🧹 Why Data Cleaning Matters

Without cleaning, data can mislead analysis and produce wrong conclusions. Errors in raw data often arise due to manual entry mistakes, missing fields, system glitches, or inconsistent formats.

• ✔ Ensures accuracy and reliability of insights
• ✔ Reduces bias and inconsistencies
• ✔ Improves performance of machine learning models
• ✔ Builds trust among stakeholders using the data

❓ Handling Missing Values

Missing values are common in real-world datasets. If not treated properly, they can distort averages, correlations, and predictions.

📑 Removing Duplicates

Duplicate records can inflate metrics like sales, customer count, or clicks. They often appear when datasets are merged from multiple systems or due to manual entry.

• ✔ Identify duplicates using unique keys (customer ID, transaction ID)
• ✔ Remove exact duplicates (same record multiple times)
• ✔ Standardize fields (name, address) to catch partial duplicates
• ✔ Deduplicate at source if possible (CRM, ERP systems)

📈 Handling Outliers

Outliers are extreme values that deviate significantly from the rest of the data. They may signal genuine anomalies (fraud, defects) or data errors.