Article - main feature engineering techniques
Now, coming to the more technical side of data transformation, let’s look at the different methods and processes you can use to make your data more meaningful. Here is a list of commonly used approaches:
Aggregation involves combining data from multiple sources into a single dataset to create a unified view of data from disparate systems. For example, you may aggregate sales data by product or region to get an overview of your sales performance.
Smoothing removes noise or meaningless data from a dataset to make the data more manageable and easy to analyze. Data analysts often use this to reduce volatility in time-series data and make trends more visible by making small changes.
Generalization involves reducing the level of detail in a dataset. For example, you may generalize customer data by grouping customers into segments based on similar characteristics.
This method involves replacing detailed data points with more general ones. For example, you can generalize a dataset containing personal details by replacing names and addresses with codes.
Discretization divides continuous data into a finite number of intervals or categories to make analyzing and interpreting data easier, especially when working with large datasets.
Discretization helps handle continuous attributes in datasets such as age, income, etc. For instance, you can discretize a continuous attribute such as age into three categories- young (18-30 years), middle-aged (31-50 years), and old (> 50 years).
Data scientists create new attributes or variables based on existing data through the attribute construction process. This method involves feature engineering, where you can create unique attributes from existing ones by combining multiple fields.
It helps you identify patterns or relationships between different data points that would not be obvious in the raw data. For example, you could construct an attribute for “total sales” by summing up the values of individual transactions over a certain period.
You can scale your data through normalization to fit within a specified range and ensure data consistency across different datasets. Normalization also makes comparing other variables easier and helps reduce data redundancy.