Data Cleaning Techniques for Missing Values: Best Practices

Which data cleaning methods are suitable for handling missing values?

• 'Fill in a best estimate manually.'
• 'Use the average value.'
• 'Don't fill in, just leave it blank.'

The correct answer is:

'd. ii) and iii) only'

Explanation:

• 'Fill in a best estimate manually': While this is a valid approach, it's not included in the given options. This involves manually determining the most suitable value to replace missing data.
• 'Use the average value': This is a common practice for handling missing data. The average value of existing data points is calculated and used to fill in the gaps.
• 'Don't fill in, just leave it blank': This method is also acceptable, especially when missing data cannot be accurately estimated or imputed.

Therefore, the correct answer is 'ii) and iii) only.'