Forum: The Racing Rules of Sailing
What additional preprocessing techniques can I apply to my dataset?
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Ralph Bright
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In addition to the basic preprocessing steps mentioned earlier, there are several advanced preprocessing techniques you can apply to your dataset to further enhance the quality and usefulness of the data. Here are some additional preprocessing techniques that you can consider implementing:
- Handling Missing Values:
- Imputation: Fill missing values using techniques like mean, median, mode imputation, or more advanced methods like K-Nearest Neighbors (KNN) imputation or predictive modeling.
- Deletion: Remove rows or columns with a high percentage of missing values if they cannot be imputed reliably.
- Interpolation: Use interpolation methods to estimate missing values based on the surrounding data points.
- Handling Outliers:
- Detection: Identify outliers using statistical methods like z-score, subway surfers, IQR (Interquartile Range), or visualization techniques.
- Treatment: Decide whether to remove outliers, cap them, transform them, or treat them specially based on domain knowledge.
- Feature Scaling:
- Standardization: Scale numerical features to have a mean of 0 and a standard deviation of 1.
- Normalization: Scale numerical features to a fixed range, typically between 0 and 1.
- Robust Scaling: Scale features using robust estimators to handle outliers better.
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