Preprocessing is a crucial step in data analysis and machine learning that involves transforming raw data into a clean and usable format, enhancing the quality and performance of the models. It encompasses a range of techniques such as data cleaning, normalization, and feature extraction to ensure that the data is consistent, complete, and ready for analysis.

Preprocessing

Data analysis involves systematically applying statistical and logical techniques to describe, illustrate, condense, and evaluate data. It is crucial for transforming raw data into meaningful insights that drive decision-making and strategic planning.

data analysis

Machine learning is a subset of artificial intelligence that involves the use of algorithms and statistical models to enable computers to improve their performance on a task through experience. It leverages data to train models that can make predictions or decisions without being explicitly programmed for specific tasks.

machine learning

raw data

clean and usable format

quality and performance

Data cleaning is the process of detecting and correcting or removing corrupt, inaccurate, or irrelevant records from a dataset, thereby improving the quality and reliability of the data. It is a crucial step in data preprocessing that ensures the data is accurate, consistent, and usable for analysis and decision-making.

data cleaning

Normalization is a process in database design that organizes data to reduce redundancy and improve data integrity by dividing large tables into smaller, related tables. It involves applying a series of rules or normal forms to ensure that the database is efficient, consistent, and scalable.

normalization

Feature extraction is a process in data analysis where raw data is transformed into a set of features that can be effectively used for modeling. It aims to reduce the dimensionality of data while retaining the most informative parts, enhancing the performance of machine learning algorithms.

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