Data Preprocessing Questions
The common techniques used for data normalization are:
1. Min-Max Scaling: This technique rescales the data to a specific range, typically between 0 and 1. It subtracts the minimum value from each data point and then divides it by the range (maximum value minus minimum value).
2. Z-Score Standardization: This technique transforms the data to have a mean of 0 and a standard deviation of 1. It subtracts the mean from each data point and then divides it by the standard deviation.
3. Decimal Scaling: This technique involves moving the decimal point of the data values to a common scale. The decimal point is shifted to the left or right based on the maximum absolute value in the dataset.
4. Log Transformation: This technique is used to reduce the skewness of the data. It applies a logarithmic function to the data, which can help in handling data with a wide range of values.
5. Unit Vector Transformation: This technique scales the data to have a unit norm, which means that the length of each data point becomes 1. It divides each data point by the Euclidean norm of the data vector.
These techniques help in normalizing the data and bringing it to a consistent scale, which is important for many machine learning algorithms to perform effectively.