It is the machine learning algorithm that learns from labeled data. After the data is analyzed and learned, the algorithm determines which label should be given to new data supplied by the user based on pattern and associating the patterns to the unlabeled new data. "
Selecting a suitable machine learning algorithm for your problem can be a difficult task. If you have a lot of time, you can try all of them. However, usually the time you have to solve a problem is limited. You can ask yourself several queries before you start to work on your problem. Depending on your answers and situation, you can list out some of the algorithms to try them on your data."
Data Pre-processing is one of the prerequisites for real-world Data mining problems. The real-world data are susceptible to high noise, contains missing values and a lot of vague information, and is of large size. These factors cause degradation of the quality of data. And if the data is of low quality, then the result obtained after the mining or modeling of data is also of low quality. So, before mining or modeling the data, it must be passed through a series of quality upgrading techniques called data pre-processing.
Data cleaning is the process of preparing data for analysis by modifying or removing the data that is inaccurate, incomplete, meaningless, duplicated, or formatted inappropriately. Usually, this data is not essential or helpful when it comes to analyzing data as it may disturb the further processes or may generate inaccurate or irrelevant outputs. There are several methods for data cleaning data depending upon the nature of data." " "
In computing, the process of converting data from one format or structure into another format or structure is Data transformation. Most system creation and data processing functions such as data wrangling, data storage, data creation are a crucial part of this. We are trying to alter the essence of the data using some methods in this process, so we can extract useful information from it.