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.
c. Using Algorithms that Support Missing Values:
Some algorithms, like decision trees, can handle missing values natively without requiring imputation.