A Guide to Data Cleaning and Preprocessing
Machine Learning is the discipline or set of processes that uses data, algorithms and statistics for the purpose of training machines to perform tasks with minimal human intervention. It also enables Artificial Intelligence to learn from data and improve accordingly without explicit instruction. There are multiple ways in which data needs to be treated before further application of algorithms. One of these steps is called Data Preprocessing in machine learning. It refers to any kind of processing or action performed on raw data in order to prepare and convert it into a more coherent format for the next processing step. On the other hand, Data cleaning in machine learning is one of the major tasks of data preprocessing. It pertains to literally cleansing the datasets, wherein all kinds of inconsistent, incorrect, inaccurate and incomplete data is removed and subsequently, any missing components – if present – are restored. While there are several other processes in the machine learning field, this article outlines two major ones below.
Data Preprocessing in Machine Learning:
Importance: The quality of data is extremely important in any machine learning process. Data preprocessing is the step that ensures that raw data is tactfully transformed into usable datasets. It eliminates the noise and produces clean data that can be highly beneficial for building good AI and machine learning models. Essentially, data preprocessing techniques in machine learning enhance the readability, reliability and consistency of data in order to promote further accurate analysis as well as interpretation.
Steps / Tasks: Following are some of the broad data preprocessing steps in machine learning.
- Data Cleaning – Data cleaning in data preprocessing is also referred to as the data cleansing process which entails steps to eliminate and fix inconsistencies in raw data.
- Data Integration – This refers to the consolidation of data from a range of different sources into a single dataset. All of this data is usually found in distinct structures and formats, which creates difficulties in this step of data preprocessing. It pertains to resolving differences in attribute values, solving problems of entity identification and also prioritizes schema integration from multiple sources and databases.
- Data Transformation – As the term suggests, data transformation is that step of data preprocessing which converts raw data into structures and formats that are suitable for further analysis and interpretation. The complexity of this process can vary as per the data requirements for specific purposes. One of the ways in which data transformation can be carried out is Smoothing, which highlights main features of the dataset. On the other hand, aggregation summarizes the data while discretization segregates it into intervals. Another way is ranging or scaling the data, that is, normalization.
Data Reduction: Data reduction is one of those data preprocessing steps in machine learning that aims to compress the size of the datasets by minimizing their volume so that they consume less storage space and become easier to navigate. In this process, the goal is to achieve little to no actual loss of data.
Data Cleaning Process:
Here are some of the major steps in the data cleansing process.
- Remove Data – There are a lot of portions in datasets that are partially or entirely irrelevant, inaccurate, inconsistent, or even duplicate. Some of this can also be what is referred to as “noisy” data, which is those parts that are practically meaningless to machines and therefore cannot be interpreted. This type of data can be “smoothened” through sorting, segmentation or clustering.
- Fix Errors – The raw data you are working with might have parts that are duplicated and unnecessary. These will eat up a lot of your essential storage space and be of no real use to you. In fact, duplicates can dramatically slow down the rest of the processes and make analysis even more tedious while bearing the risk of producing inaccurate results. To fix this, deduplicating is vital. Besides this, your datasets may even contain certain structural errors in spelling, grammar and semantics. These will have to be corrected on priority so that machine learning algorithms can run smoothly.
- Take care of Missing Data Values – Many times, there are values that might be important to a dataset but are found missing. In such cases, these will have to be manually filled in to make sure that the dataset is complete and makes sense in its entirety.
- Get rid of Outliers – An outlier is a data point that lies farther away from the rest of the data and differs from the norm. When outliers are a result of errors, they tend to skew the data in an undesirable direction and heavily distort the analysis. In a situation like this, the best course of action can be to delete the outlier entirely.
Ultimately, after carrying out all the steps of the data cleaning process, one must make sure that the resultant datasets are perfectly uniform and ready to be passed on for further processing and analysis.
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