Data preprocessing can refer to manipulation or dropping of data before it is used in order to ensure or enhance performance, and is an important step in the data mining process. The phrase "garbage in, garbage out" is particularly applicable to data mining and machine learning projects. Data-gathering methods are often loosely controlled, resulting in out-of-range values (e.g., Income: −100), impossible data combinations (e.g., Sex: Male, Pregnant: Yes), and missing values, etc. Analyzing data that has not been carefully screened for such problems can produce misleading results. Thus, the representation and quality of data is first and foremost before running any analysis. Often, data preprocessing is the most important phase of a machine learning project, especially in computational biology. If there is much irrelevant and redundant information present or noisy and unreliable data, then knowledge discovery during the training phase is more difficult. Data preparation and filtering steps can take considerable amount of processing time. Examples of data preprocessing include cleaning, instance selection, normalization, one hot encoding, transformation, feature extraction and selection, etc. The product of data preprocessing is the final training set. Data preprocessing may affect the way in which outcomes of the final data processing can be interpreted. This aspect should be carefully considered when interpretation of the results is a key point, such in the multivariate processing of chemical data (chemometrics). Tasks of data preprocessing Data cleansing Data editing Data reduction Data wrangling Example In this example we have 5 Adults in our dataset who have the Sex of Male or Female and whether they are pregnant or not. We can detect that Adult 3 and 5 are impossible data combinations. We can perform a Data cleansing and choose to delete such data from our table. We remove such data because we can determine that such data existing in the dataset is caused by user entry errors or data corruption. A reason that one might have to delete such data is because the impossible data will affect the calculation or data manipulation process in the later steps of the data mining process. We can perform a Data editing and change the Sex of the Adult by knowing that the Adult is Pregnant we can make the assumption that the Adult is Female and make changes accordingly. We edit the dataset to have a clearer analysis of the data when performing data manipulation in the later steps within