the data mining process. We can use a form of Data reduction and sort the data by Sex and by doing this we can simplify our dataset and choose what Sex we want to focus on more. Data mining The origins of data preprocessing are located in data mining. The idea is to aggregate existing information and search in the content. Later it was recognized, that for machine learning and neural networks a data preprocessing step is needed too. So it has become to a universal technique which is used in computing in general. Data preprocessing allows for the removal of unwanted data with the use of data cleaning, this allows the user to have a dataset to contain more valuable information after the preprocessing stage for data manipulation later in the data mining process. Editing such dataset to either correct data corruption or human error is a crucial step to get accurate quantifiers like true positives, true negatives, False positives and false negatives found in a Confusion matrix that are commonly used for a medical diagnosis. Users are able to join data files together and use preprocessing to filter any unnecessary noise from the data which can allow for higher accuracy. Users use Python programming scripts accompanied by the pandas library which gives them the ability to import data from a Comma-separated values as a data-frame. The data-frame is then used to manipulate data that can be challenging otherwise to do in Excel. pandas (software) which is a powerful tool that allows for data analysis and manipulation; which makes data visualizations, statistical operations and much more, a lot easier. Many also use the R (programming language) to do such tasks as well. The reason why a user transforms existing files into a new one is because of many reasons. Data preprocessing has the objective to add missing values, aggregate information, label data with categories (Data binning) and smooth a trajectory. More advanced techniques like principal component analysis and feature selection are working with statistical formulas and are applied to complex datasets which are recorded by GPS trackers and motion capture devices. Semantic data preprocessing Semantic data mining is a subset of data mining that specifically seeks to incorporate domain knowledge, such as formal semantics, into the data mining process. Domain knowledge is the knowledge of the environment the data was processed in. Domain knowledge can have a positive influence