Data cleaning is the process of ensuring data is correct, consistent, and usable. You can clean data by identifying errors or corruptions, correcting or deleting them, or manually processing data as needed to prevent the same errors from occurring.
The quality of data will have a huge impact on the quality of the model. Data preprocessing is a technique to convert the data collected into a clean dataset. It is one of the vital and important steps in building a machine learning model. It is also known as data wrangling or data cleaning.
Some datasets contain missing values, duplicate values, or incorrect data. In those cases, remove a specific row that has a null value for a feature or a particular column where the values are missing. Or calculate the mean of a particular row that contains a missing value and replace the result for the missing value. Most datasets have a large number of features. Which increases planes, it is difficult to model and visualize. The volume of data is reduced by methods like Principal Component Analysis (PCA) and SVD.
Some of the reasons why pre-cleaning steps are important to complete prior to data cleaning are as follows
- Using tools to pre-clean the data will make it more efficient as we can able quickly get what you need from the data available.
- It removes major errors and inconsistencies that are inevitable when multiple sources of data are being pulled into one dataset.
- It allows for mapping different data functions, a better understanding of what the available data is intended to do, and learning where it is coming from.
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