Data Preprocessing is a set of techniques used to prepare data for use in computer algorithms and programs. It is a fundamental step in the data mining process and involves the selection, cleaning, integration, transformation and extraction of data for further processing.
Data preprocessing is a procedure that entails several activities. These activities include data cleaning, normalization, transformation, and reducing the amount of data. The goal is to have consistent, high-quality data in a form that can easily be fed into computer algorithms.
Data cleaning removes or fills in missing values, eliminates data inconsistent with the data’s original format, and removes outliers. Normalization is the process of transforming values of several variables into a common scale, such as 0 to 1 or -1 to +1. The normalized data becomes easier to analyze, interpret, and compare.
Data transformation includes scaling, aggregation, discretization, and encoding. Scaling adjusts the data to uniform magnitudes, and is critical when different variables have different measurement units or have different scales. Aggregation combines multiple observations into one and provides an overview of the data. Discretization assigns labels and categories to observations, and encoding is used to project a data set onto smaller dimensions.
Reducing the amount of data is done in order to reduce complexity and optimize the performance of the computer algorithms. Dimensionality reduction techniques are used to remove redundant and noisy components from data, leading to data visualization and better-performing models.
Overall, data preprocessing is an important step before applying advanced algorithms such as machine learning, artificial intelligence and deep learning. It ensures only high-quality data enters the process, enabling algorithms to make better predictions and decisions.
