Adatkészítés

Adatkészítés is a Hungarian term that translates to data preparation or data wrangling in English. It refers to the entire process of cleaning, transforming, and organizing raw data into a format that is suitable for analysis, machine learning, or other data-driven applications. This stage is crucial in the data science workflow, as the quality of the prepared data directly impacts the accuracy and reliability of any subsequent insights or models.

The typical adatkészítés process involves several key steps. These often include data collection, which is gathering data from various sources. Following collection, data cleaning is essential to identify and handle missing values, outliers, and erroneous entries. Data transformation might be necessary to change data types, normalize values, or create new features from existing ones. Data integration is another common step, where data from different sources is combined into a unified dataset. Finally, data reduction may be performed to decrease the volume of data while retaining its essential information, such as through feature selection or dimensionality reduction.

Effective adatkészítés requires a good understanding of the data and the goals of the analysis. It can be a time-consuming part of data science projects, but investing sufficient effort in this phase significantly improves the efficiency and success of later stages like model building and interpretation. Various tools and programming languages, such as Python with libraries like Pandas and NumPy, or R, are commonly used to perform adatkészítés tasks.