tell the workflow of data preprocessing

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Data preprocessing involves cleaning, transforming, and organizing raw data before analysis.

1. Data cleaning: Removing or correcting errors in the data, handling missing values.
2. Data transformation: Normalizing, scaling, encoding categorical variables.
3. Data reduction: Dimensionality reduction techniques like PCA.
4. Data integration: Combining data from multiple sources.
5. Feature engineering: Creating new features from existing data.
6. Splitting data: Dividing data into training and testing sets.
7. Data normalization: Scaling numerical features to a standard range.
8. Handling outliers: Identifying and dealing with outliers in the data.

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