Most often, incomplete, noisy, and inconsistent data come from the real world. The likelihood of obtaining aberrant or false data is relatively significant due to the exponential growth in data generation and the rise in heterogeneous data sources.
However, only reliable data can result in precise models and, eventually, precise forecasts. Therefore, it is essential to process data for the highest quality. Data preprocessing is a crucial stage in data science, machine learning, and AI. It is a step in the data processing process.
Businesses can leverage data from virtually countless sources, including internal data, customer service encounters, and data from across the internet, to guide decisions and enhance their operations.
However, you can't immediately run machine learning and analytics tools on raw data. Your data must first undergo preprocessing in order for machines to correctly "read" or comprehend it. Learn about data pretreatment, why it's important for data mining, and how to do it in this guide.
What Exactly is Data Pre-processing?
In order to apply data mining analytics, raw data must be transformed into well-formed data sets through the process of data preparation. Raw data frequently has uneven formatting and is insufficient. The effectiveness of every project involving data analysis is directly correlated with how well or poorly the data was prepared.
Data imputation and data validation are both a part of Data pre-processing. Assessing whether the data in question is correct and full is the aim of data validation. Correcting errors and adding missing information is the aim of data imputation, which can be done manually or automatically with business process automation (BPA) programming.
Both database-driven and rules-based applications utilize data preparation. Data preparation is essential in machine learning (ML) operations to ensure that big datasets are prepared such that the data they contain can be processed and digested by learning algorithms.
Text, photos, video, and other types of unprocessed, real-world data are disorganized. In addition to the possibility of faults and inconsistencies, it is frequently lacking and lacks a regular, consistent design.
Machines prefer to process information that is neat and orderly; they read data as 1s and 0s. Therefore, it is simple to calculate structured data like whole numbers and percentages. However, unstructured data must first be cleaned and prepared in the form of text and graphics before analysis.
Preprocessing data can be done using a variety of tools and techniques, such as the following:
These techniques and tools can be applied to a range of data sources, including streaming data and data that has been stored in files or databases.
Also Read | Guide to Data Profiling
Why is Preparation of Data Necessary?
A database is a collection of data points, as you are aware. Events, samples of data, records, and observations are other names for data points. Different qualities, usually referred to as features or attributes, are used to describe each sample. To develop models with these properties successfully, data preparation is necessary.
Several issues may come up while gathering data. It can be necessary to combine data from various sources, which could result in inconsistent data formats like integer and float.
When combining information from two or more distinct datasets, the gender field may contain the two different values: man and male for males. Similar to this, if you combine data from 10 distinct datasets, a field that is present in eight of them can be absent from the remaining two.
We can make data easier to use and analyze by preparing it. The accuracy of a model is improved by removing data inconsistencies or duplicates that could otherwise exist. Preprocessing the data makes sure there aren't any incorrect or missing values brought on by bugs or human error. In other words, using data preparation techniques improves the accuracy and completeness of the database.
Features in Machine Learning
Features are individual independent variables that act as an input in our machine learning model. They can be viewed as representations or qualities that provide context for the data and aid classification/label prediction in models.
Examples of features in a structured dataset include Name, Age, Sex, Fare, and other columns that each represent a quantifiable piece of data that may be used for analysis.
Steps in Data Preprocessing
Unintended effects, such as prejudice, might result from an insufficient training set, giving one group of people an unjustified advantage or disadvantage. The results of data mining initiatives might also be negatively impacted by incomplete or inconsistent data. Data preparation is employed to address these issues.
There are generally 4 steps of Data pre-processing : Cleansing, Integration, Reduction, and Transformation.
Cleaning of data
Data cleaning, also known as cleansing, is the process of removing anomalies from datasets, accounting for missing values, resolving inconsistencies in the data, and smoothing noisy data. Data cleaning's main goal is to provide complete and accurate samples for machine learning models.
Depending on the data scientist's preferences and the issue at hand, different data cleaning tools are employed. Here's a quick rundown of the problems that data cleansing addresses and the methods used.
Missing data values are a rather prevalent issue. It could occur during data collection or as a result of a particular data validation rule. In such circumstances, you must gather more data samples or search for more datasets.
When you combine two or more datasets to create a larger dataset, the problem of missing values may also appear. It is preferable to remove any fields that are missing from one or both datasets before combining.
Unless any of the aforementioned techniques can be used to fill in the values, it is preferable to delete the entire row or column if 50% of the values for any of the rows or columns in the database are missing.
Noise is a big amount of useless data. More specifically, it's the random volatility in a measured variable or inaccurate attribute values in data. Data points that are duplicated or semi-duplicated, data segments that are useless for a certain research method, or undesirable information fields are all examples of noise.
For instance, it won't matter what a person's weight, height, or hair color is if you need to determine whether they can drive.
Even though some people view an outlier as a legitimate data item, it might be viewed as noise. Consider that you are teaching an algorithm to recognise tortoises in images. The image dataset may include pictures of turtles that were mistakenly identified as tortoises. This might be seen as noise.
There are certain tortoise images, though, that resemble turtles rather than actual tortoises. That sample might be an oddity rather than just noise. This is because divergence from the group is crucial if we want to teach the algorithm all feasible methods of detecting tortoises.
To find outliers for numerical values, utilize a scatter plot or box plot.
Data pre processing cycle
Integration of Data
Data integration is an essential component of data preparation since data is gathered from diverse sources. Integration may result in numerous redundant and inconsistent data points, which would ultimately produce less accurate models.
The following are some methods for integrating data:
Data Consolidation: The physical collection and storage of data in one location. Efficiency and productivity are increased when all the data is in one location. Usually, to complete this stage, data warehouse software is used.
Also Read | What is Data Processing and Why is it Important?
Data reduction is used to minimize the amount of data, as the name implies, and hence lower the cost of data mining or data analysis.
It provides a streamlined illustration of the dataset. Although the volume is decreased, the original data's integrity is preserved. When working with big data, this data preparation stage is very important because the volume of data involved would be enormous.
The techniques used for data reduction include the ones listed below.
The quantity of features or input variables in a dataset is decreased through dimensionality reduction, sometimes referred to as dimension reduction.
A dataset's dimensionality refers to the quantity of characteristics or input variables that it contains. It is more difficult to visualize the training dataset and develop a predictive model as the number of characteristics increases.
A list of principal variables can be obtained by using dimensionality reduction algorithms to reduce the number of random variables in specific situations where the majority of these attributes are redundant due to correlation.
Feature selection and feature extraction are the two halves of dimensionality reduction.
We search for a subset of the initial set of features during feature selection. This enables us to obtain a more manageable subset that may be applied to data modeling to visualize the issue. Feature extraction, on the other hand, reduces data from a high-dimensional space to a lower-dimensional space, or a space with fewer dimensions.
The process of changing data from one format to another is known as data transformation. Essentially, it entails techniques for converting data into acceptable representations that the computer can effectively learn from.
The speed units, for instance, could be kilometers per hour, meters per second, or miles per hour. As a result, a dataset may store values for a car's speed in many units. We must convert the data into the same unit before submitting it to an algorithm.
Here are a few techniques for data transformation :
With the aid of algorithms, this statistical technique is utilized to reduce noise from the data. It aids in pattern prediction and draws attention to a dataset's most important elements. To make the patterns more obvious, outliers must also be removed from the sample.
Data from several sources are combined and presented in a single format for data mining or analysis, which is known as "aggregation." Increasing the quantity of data points by combining data from several sources is crucial since only then will the ML model have enough examples to learn from.
Discretization is the process of breaking up sets of continuous data into smaller periods. For instance, grouping people into categories like "teen," "young adult," "middle age," or "senior" is more effective than utilizing continuous age data.
In order to generalize, low-level data characteristics must be transformed into high-level data features. For instance, higher-level criteria like city or state can be generalized from category data like residential address.
The process of transforming all data variables into a particular range is referred to as normalization. In other words, it's used to scale attribute values such that they fall inside a narrower range, such as 0 to 1. Data normalization techniques include decimal scaling, min-max normalization, and z-score normalization.
The process of creating new features from the existing collection of features is known as feature construction. By streamlining the initial dataset, this technique makes it simpler to mine, analyze, or visualize the data.
Concept hierarchy generation
Although it isn't stated, concept hierarchy generation allows you to construct a hierarchy between features. This technique can be used to arrange the data in hierarchical ways, for instance, if you have a house address dataset that includes information about the street, city, state, and country.
Accurate information, precise outcomes
Algorithms for machine learning are like children. They know very little to nothing about what is good or bad. Inaccurate or inconsistent data can quickly have an impact on ML models, similar to how children begin repeating offensive language they learn from adults. The trick is to provide them with reliable, high-quality data, and data preparation is a crucial stage in this process.
Also Read | Different Types of Learning in Machine Learning?
Good, organized data are necessary for good data-driven decision making. Following the steps above will prepare your data for any number of downstream procedures once you've determined the analysis you need to perform and where to get the data you need.
Data preprocessing can be a laborious job, but once your processes and procedures are established, you'll later benefit from it.