Data Cleaning and Handling Missing Values

One of the first steps in preprocessing is cleaning the data. Real-world datasets often contain missing values, duplicate entries, incorrect labels, or outliers that can negatively impact model performance. There are several strategies for handling missing data:

• Removing Missing Data: If a feature contains a high percentage of missing values, it may be best to remove it entirely to prevent introducing bias.
• Mean/Median/Mode Imputation: Replacing missing numerical values with the mean, median, or mode of the feature.
• Predictive Imputation: Using machine learning algorithms to predict and fill in missing values based on other features.
• Interpolation Techniques: Applying methods like linear or polynomial interpolation to estimate missing values.

Handling missing values properly ensures that models receive complete and consistent data, leading to more accurate predictions and better generalization.