Data normalization and standardization are techniques used in data preprocessing to improve the quality and accuracy of machine learning models. Normalization involves scaling the values of features to a specific range, ensuring they have a similar impact on the model. On the other hand, standardization involves transforming data to have a mean of 0 and a standard deviation of 1, making it easier to interpret and compare different features. These processes help to reduce bias, enhance model performance, and facilitate more effective data analysis.

Data normalization and standardization are crucial processes in data preprocessing that aim to enhance the quality and efficiency of data analysis. Normalization involves rescaling numeric features to a standard range, reducing the impact of varying scales on the model. On the other hand, standardization centers the data around mean zero and standard deviation one for better interpretability and comparability across features. These techniques ensure data uniformity, improve model performance, and aid in drawing meaningful insights from the data.