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