How Do You Handle Missing or Corrupted Data in a Dataset?

[shrutii]

Handling missing or corrupted data is a fundamental skill in machine learning. Whether it’s through deletion, imputation, or advanced algorithmic techniques, the goal is to ensure data integrity without compromising model performance. As you progress through machine learning classes in Pune, these strategies will become second nature, enabling you to build robust models that deliver accurate and actionable insights.


Types of Missing Data
Missing data can be categorized into three types:


Missing Completely at Random (MCAR): The missingness is entirely random and not related to any other data. For example, a sensor occasionally fails without any identifiable pattern.
Missing at Random (MAR): The missingness is related to other observed data but not the missing data itself. For example, older respondents in a survey might be less likely to answer questions about technology usage.
Missing Not at Random (MNAR): The missingness is related to the missing data itself. For example, people with higher incomes may choose not to disclose their income levels in surveys.
Identifying the type of missing data helps in choosing the right handling technique.

[abel1303]

If you’re looking for adrenaline-pumping action, Escape Road delivers an unforgettable experience filled with fast-paced pursuits and strategic challenges.

[alice5]

The smart and creative puzzles in escape road 2 require players to think and be creative to overcome.

[Loaksi]

One of the other characters that may be found in the game is a pair of scissors that can temporarily remove some barriers
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