Performing data transformation before the algorithm development phase is crucial for preparing your data for analysis and involves several techniques that help make your data more understandable, usable, and suitable for your specific needs. We’ll go through some of these techniques one by one, using the following examples.
- Standardization: Imagine you own a clothing store and have collected data from different sources about the sizes of clothes produced by various manufacturers. You might notice a lack of consistency in the size measurements, as some manufacturers use inches while others might use centimeters. Standardization is the process of bringing these disparate measurements onto a common scale or unit so that you can easily compare and analyze them.
- Normalization: Let’s say that you are tracking the sales of different products in your store. Some products might sell in the thousands, while others might only sell in the tens. Normalization involves adjusting numerical data to a common scale without distorting the differences between the numbers. This helps you make more meaningful comparisons between products with respect to their sales.
- Discretization: Suppose you want to analyze the age of your customers to better understand their preferences. The age data might be continuous, meaning it comprises a wide range of values. Discretization helps in converting continuous data into discrete groups or bins, making it easier to analyze patterns and trends. For example, you can group customers into age groups such as 18-24, 25-34, and 35-44.
- Encoding: As a business owner, you might have data on products that have different categories or attributes, such as color, brand, or material. These categorical variables often need to be encoded as numerical values to be used in machine learning algorithms or other data analyses. One common method is called “one-hot encoding,” where each category is represented by a binary value. For example, a red shirt may be encoded as [1, 0, 0], while a blue shirt may be [0, 1, 0].
- Feature Scaling: Continuing with the clothing store example, you might want to analyze the sizes of your products and their prices. Because the size and price data are on different scales, feature scaling can help bring these two variables onto a similar scale, making it easier for machine learning algorithms to process. Common methods include min-max scaling or standard scaling (subtracting the mean and dividing by the standard deviation).
- Mathematical Transformations: Sometimes, raw data can be somewhat difficult to work with or interpret. Mathematical transformations, such as taking the logarithm or square root of a variable, can help in making the data more manageable or reveal underlying patterns that were not immediately apparent. For example, you may be tracking the daily visitors to your store and notice that the number of visitors is growing exponentially. Taking the logarithm of the visitor data can make it easier to analyze how quickly the growth is happening.
In conclusion, data transformation techniques like standardization, normalization, discretization, encoding, feature scaling, and mathematical transformations are essential tools in preparing your data for meaningful analysis. By properly applying these techniques, you can make it easier to spot patterns, find trends, and draw valuable insights.