[size= 14px]Human Error: Mistakes during data entry or manual handling can result in missing values.[/size]

[size= 14px]Data Collection Issues: Incomplete surveys, sensor malfunctions, or transmission errors can cause gaps.[/size]

[size= 14px]System Failures: Software bugs, hardware malfunctions, or network interruptions can corrupt data.[/size]

[size= 14px]Data Integration Problems: Merging datasets from different sources without proper alignment can lead to inconsistencies.[/size]

[size= 14px]Understanding the root cause helps determine the most appropriate method for handling the problem.[/size]

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[size= 14px]Types of Missing Data[/size]

[size= 14px]Missing data can be categorized into three types:[/size]

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[size= 14px]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.[/size]

[size= 14px]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.[/size]

[size= 14px]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.[/size]

[size= 14px]Identifying the type of missing data helps in choosing the right handling technique.[/size]

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.