Data ScienceLesson 8Free preview
DS Data Preparation
This lesson is part of Data Science · All levels
Summary
Data preparation or preprocessing is the process of preparing and cleaning raw data to be ready for accurate analysis. Its main activities deal with missing values, duplicate, and outlier data, as well as converting categorical data to numeric through encoding. The quality of the results of the analysis depends heavily on the accuracy of this stage.
Key points
- Objective: clean, complete, and consistent data before it is analyzed.
- Missing value is dealt with by imputation (mean/median/mode) or deleting according to context.
- Duplication makes statistics and models biased.
- The outlier is analyzed first: valid or data error.
- Encoding changes categories like low/high/high into numbers.
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