![]() ![]() Normalization and Standardization are the two scaling processes. Scaling is a technique of using feature engineering. The technique changes the categorical data to the numerical format and enables to group the categorical data without losing any important information. These binary values expresses a relationship between grouped and the encoded column. It involves spreading values in a column to multiple flag columns and assigning 0 or 1 to them. One-hot encoding is a technique of using feature engineering. Log transform only requires positive data as an input and if negative data is given as input, it will give an error. It decreases the effect of outliers due to normalization of magnitude differences and the model become more robust and effective. It involves handling of skewed data and after transformation, distribution becomes more approximate to the normal. Log Transform is a technique of using feature engineering. It helps in preventing overfitting and improving model accuracy.
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