Dimensional reduction

Dimensional reduction is usually used to reduce the number of variables that are to be considered in an machine learning project. It is often used where columns of data in a file have more than an acceptable number of missing values, have low variance, or are extremely variable in nature. Before attempting to reduce your data source by removing those unwanted columns, you need to be comfortable that this is the right thing to be doing. In other words, you want to make sure that the data you reduce does not create a bias in the remaining data. Profiling the data is an excellent way to determine whether the dimensional reduction of a particular column or columns is appropriate. Data profiling is a technique that is used to examine data to determine its accuracy and completeness. This is the process of examining a data source to uncover the erroneous sections...