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Data preprocessing

Data preprocessing

Data preprocessing definition

Data preprocessing refers to the process of cleaning and transforming raw data into a format that can be accurately analyzed or fed into a model. It involves various techniques to handle inaccuracies and irrelevancies in the data, ensuring it meets the necessary quality standards for analysis.

See also: information processing, data deduplication

Where is data preprocessing used?

• Machine learning. Before training a model, data needs to be appropriately formatted. For example, you may need to normalize data, handle missing values, or encode categorical variables. It allows the algorithm to understand and use it effectively.
• Data mining. To extract meaningful patterns and insights from large datasets, preprocessing ensures the data is consistent and relevant, leading to more accurate and insightful results.
• Statistics. Because outliers can distort results, preprocessing helps in refining the dataset for better statistical inference.

Steps of data preprocessing:

1. Data cleaning. Filling in the gaps where data might be missing.
2. Data integration. Bringing data together in a unified manner.
3. Data transformation. Making sure all numerical data is on the same scale.
4. Data reduction: Reducing the amount of data while keeping its essence.
5. Data discretization. Converting continuous data (like age) into groups or bins.
6. Feature engineering. Making new attributes from the existing data that might help in analysis.
7. Feature selection. Choosing which attributes are most important for the analysis.