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

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Human Error: Mistakes during data entry or manual handling can result in missing values.
Data Collection Issues: Incomplete surveys, sensor malfunctions, or transmission errors can cause gaps.
System Failures: Software bugs, hardware malfunctions, or network interruptions can corrupt data.
Data Integration Problems: Merging datasets from different sources without proper alignment can lead to inconsistencies.
Understanding the root cause helps determine the most appropriate method for handling the problem.

Techniques to Handle Missing Data

1. Deletion Methods
   Listwise Deletion: Removes entire rows where any data is missing. This is simple but can result in significant data loss if many records have missing values.
   Pairwise Deletion: Analyzes data only with available values for each specific analysis. This retains more data but can complicate correlation calculations.
   When to Use: Deletion methods are suitable when the dataset is large, and missing data is minimal and random (MCAR).

2. Imputation Techniques
   Imputation involves filling in missing values with substitute data.

Mean/Median/Mode Imputation: Replaces missing values with the mean (for continuous data), median (for skewed data), or mode (for categorical data).
K-Nearest Neighbors (KNN) Imputation: Estimates missing values based on the values of similar (neighboring) data points.
Regression Imputation: Uses regression models to predict missing values based on other features.
Multiple Imputation: Generates multiple datasets with imputed values and averages the results, accounting for uncertainty in missing data.
When to Use: Imputation is effective when missing data is MAR and you want to retain as much information as possible without biasing the dataset.

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.