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What Is Data Normalization And Why?
Data normalization is a data preprocessing technique used to organize and structure data so that it becomes consistent, accurate, and usable for analysis. In the context of databases and analytics, normalization... moreWhat Is Data Normalization And Why?
Data normalization is a data preprocessing technique used to organize and structure data so that it becomes consistent, accurate, and usable for analysis. In the context of databases and analytics, normalization minimizes redundancy and dependency by dividing large tables into smaller, related ones. In machine learning and statistical modeling, it usually refers to scaling numerical data to a common range (e.g., 0 to 1) to ensure that features contribute equally to the model’s learning process.
Normalization is essential because real-world data often comes in varied scales and units. For example, if one feature ranges from 1 to 1000 and another from 0 to 1, models like k-NN or gradient descent-based algorithms may give undue importance to the larger-scaled feature. By normalizing, we bring uniformity to the data, improve the model's convergence speed, and ensure better accuracy and interpretability of results.
There are various normalization techniques such as Min-Max scaling, Z-score standardization, and decimal scaling, each used depending on the dataset and the algorithm in use.
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