Data preprocessing is crucial in machine learning to enhance the quality of input data and improve model performance. Common techniques include:
1. Handling Missing Data:
- Imputation: Fill missing values using mean, median, or mode.
- Deletion: Remove rows or columns with missing data.
2. Handling Categorical Data:
- One-Hot Encoding: Convert categorical variables into binary vectors.
- Label Encoding: Assign a unique numerical label to each category.
3. Normalization and Standardization:
- Normalization scales features to a standard range (e.g., 0 to 1).
- Standardization transforms data to have a mean of 0 and standard deviation of 1.
4. Data Scaling:
- Min-Max Scaling: Scale features to a specific range.
- Robust Scaling: Scaling with median and interquartile range to handle outliers.
5. Dealing with Outliers:
- Identify and handle outliers using techniques like Z-score or IQR.
6. Feature Engineering:
- Create new features or transform existing ones to provide more information to the model.
7. Data Splitting:
- Split the dataset into training and testing sets to evaluate model performance.
8. Noise Removal:
- Remove irrelevant information or noise from the data.
9. Handling Imbalanced Data:
- Techniques like oversampling minority class or undersampling majority class.
10. Text Cleaning:
- Tokenization, stemming, and removal of stop words for textual data.
Remember, the choice of preprocessing techniques depends on the characteristics of the data and the requirements of the specific machine learning task.
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Derek