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Handling missing or corrupted data is a crucial step in data preprocessing, as it can significantly impact the performance of machine learning models. Here are several strategies to address these issues:
1. Identify Missing or Corrupted Data

• Exploratory Data Analysis (EDA): Use summary statistics and visualizations to identify missing values or anomalies.
  
• Data Types: Check for unexpected data types that may indicate corruption (e.g., strings in numeric columns).
  

2. Handling Missing Data
a. Removal:

• Listwise Deletion: Remove rows with any missing values. This is straightforward but can lead to loss of valuable data, especially in small datasets.
  
• Pairwise Deletion: Use available data for analyses, removing only the specific data points that are missing.
  

b. Imputation:

• Mean/Median/Mode Imputation: Replace missing values with the mean, median, or mode of the column. This is simple but may introduce bias.
  
• Forward/Backward Fill: Use the last known value (forward fill) or the next known value (backward fill) for time series data.
  
• K-Nearest Neighbors (KNN) Imputation: Use the values of the nearest neighbors to estimate the missing values based on similarity.
  
• Regression Imputation: Predict the missing values using regression models based on other available features.
  

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