Question

What is Data Cleaning, and how do you handle missing values in a dataset?

Hint:

Clean data leads to reliable results — always analyze the pattern of missing values before choosing a handling method.

Answer 1

Concept: Data Cleaning is a key preprocessing step in data science that ensures datasets are accurate, consistent, and suitable for analysis. Real-world data often contains errors, duplicates, and missing values that can negatively affect model performance. Step 1: {\color{red}What is Data Cleaning?}
Data cleaning involves:

• Removing duplicates
• Fixing formatting errors
• Handling missing or inconsistent data

It improves data reliability and quality.
Step 2: {\color{red}Understanding Missing Values}
Missing data may occur due to:

• Data entry errors
• Sensor failures
• Incomplete surveys

Handling them correctly is essential to avoid biased analysis.
Step 3: {\color{red}Method 1 — Deletion}
Remove rows or columns with missing values:

• Useful when missing data is minimal
• Risky if large portions of data are removed

Step 4: {\color{red}Method 2 — Imputation}
Fill missing values using statistical measures:

• Mean (numerical data)
• Median (robust to outliers)
• Mode (categorical data)

Step 5: {\color{red}Method 3 — Advanced Techniques}
More sophisticated approaches include:

• Predictive modeling (e.g., regression)
• Interpolation for time-series data
• KNN or machine learning-based imputation

Step 6: {\color{red}Choosing the Right Method}
The strategy depends on:

• Amount of missing data
• Data type and distribution
• Impact on analysis goals