Data Preprocessing Questions
The common techniques used for data scaling 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. Standardization: Also known as z-score normalization, 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. Robust Scaling: This technique is similar to standardization but is more robust to outliers. It scales the data using the median and interquartile range instead of the mean and standard deviation.
4. Normalization: This technique scales the data so that each data point has a unit norm or length of 1. It divides each data point by the Euclidean norm of the data vector.
5. 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.
These techniques are commonly used in data preprocessing to ensure that the data is in a suitable range and distribution for further analysis or modeling. The choice of technique depends on the specific characteristics of the data and the requirements of the analysis.