Data Preprocessing: How To Process Your Data For Optimal Performance

Author

Robert Koch

I write about AI, SEO, Tech, and Innovation. Led by curiosity, I stay ahead of AI advancements. I aim for clarity and understand the necessity of change, taking guidance from Shaw: 'Progress is impossible without change,' and living by Welch's words: 'Change before you have to'.

Data preprocessing is a crucial data mining technique that involves transforming raw data into a clean, organized, and meaningful format suitable for machine learning algorithms. It encompasses a series of steps to clean, normalize, and prepare data by handling missing values, removing noise, and standardizing data formats to ensure optimal model performance.

Data preprocessing is one of the early steps of creating and utilizing a machine learning model. In this step, the raw data is prepared to be suitable for feeding to the machine learning model. It is often the first step undertaken when creating a machine learning project, as the availability of clean and well-formatted data is not always possible.

Table of Contents

• Data Preprocessing Features

• Uses and Importance of Data Preprocessing in Machine Learning

• What is AI Data Preprocessing?

• Who Benefits from AI Data Preprocessing?

• Data Preprocessing Steps/Stages

• Best Practices for optimized Data Preprocessing in Machine Learning

The data preprocessing process consists of any action to make the input data compatible with the machine learning (ML) model. These actions can include data cleaning, formatting, data reduction, finding missing data, data enhancement, and more.

Data Preprocessing Features

Machine learning models operate on datasets with the help of data properties or features. A feature is an independent variable with a certain value representing a particular dataset attribute. For instance, in the case of a dataset containing personnel details, the person’s name, age, sex, role, and qualifications can all be considered features. Each machine learning model is trained to work with certain features and derive its predictions and insights based on these features. Therefore, AI data preprocessing in machine learning helps narrow down or clean out the raw data into focused datasets. These datasets will include the necessary features that can be easily operated upon by a machine learning model.

Features can be broadly classified into two types:

• Categorical features

Features whose values are derived from a fixed, defined set of possible values or explanations are called categorical. In addition, they can have any definitive value, such as date, Boolean (true or false), positive, neutral, and types.

• Numerical features

These features contain values that can be numerically associated on a continuous scale or statistically related. For example, a number, value, or percentage, such as income, can be classified as a numerical feature. Additionally, the number of words in a document, time duration, and more can be included.

Tip:

While data preprocessing is a critical step in the machine learning process, it’s important to remember that not all data sets are created equal. Therefore, to get the most out of your machine learning model, use high-quality, preprocessed datasets for optimal performance.

More about Datasets for Machine Learning

Uses and Importance of Data Preprocessing in Machine Learning

Data preprocessing in data mining is a crucial step in creating and training machine learning models. It ensures that the machine learning model works with high-quality data, which is fundamental for accurate results and predictions.

• Removed Noise and Enhanced Data Quality

Most real-world data are inherently noisy, come in various formats, and might be incomplete. They are collected from diverse sources, leading to a dataset with many inaccuracies and inconsistencies. Directly feeding this raw data into a machine learning model is nearly impossible. Thankfully, AI data preprocessing filters and cleans data, removing noise, handling outliers, and ensuring consistency. This process ensures that only valid and suitable data is used in machine learning models, thereby enhancing data quality.

• Easy Data Consumption through Transformation

Even structured input data may not have the required fields and properties for the specific problem a machine learning model tries to solve. Data preprocessing involves crucial steps such as data transformation, where data formats are converted, features are scaled, and categorical variables are encoded. This makes the data more suitable for machine learning algorithms and ensures it can be readily consumed for further analysis.

• Improves Accuracy

Machine learning models run on data and rely on it to remain accurate and unbiased. Techniques such as normalization (min-max scaling, Z-score normalization), feature scaling, and handling missing values (through imputation or interpolation) are essential steps in data preprocessing. These actions help ensure the accuracy and reliability of the results gained from the machine learning model while considering outliers and inconsistent data points, reducing false predictions.

• Improves Performance and Reduces Resource Utilization

Data preprocessing not only allows for better accuracy but also eliminates several bottlenecks in data analysis, making the input datasets more relevant and easier to parse. It improves the machine learning model’s performance by providing clean data that can be processed faster, thereby reducing training time and the computational resources required. Additionally, steps like data reduction (through techniques like dimensionality reduction and PCA) further enhance model performance.

The quality of a machine learning model is directly tied to the quality of its results, which cannot be achieved without proper data preprocessing. If a model is trained with dirty data, it will produce no useful results. Hence, data preprocessing, now including modern concepts like data wrangling and utilizing data lakes, is considered a crucial and mandatory step in machine learning.

What is AI Data Preprocessing?

AI data preprocessing refers to the process of preparing raw data for use in artificial intelligence (AI) and machine learning (ML) models. It involves various techniques and procedures aimed at cleaning, transforming, and organizing data to make it suitable for analysis and model training. The primary goal of data preprocessing is to improve the quality and usability of the data, thereby enhancing the performance and accuracy of AI and ML models.

Who Benefits from AI Data Preprocessing?

Various stakeholders benefit from AI data preprocessing, including:

1. Data Scientists and Analysts: They benefit from clean and organized data, which enables them to build accurate and efficient machine learning models. Preprocessing ensures that the data is in a suitable format for analysis, saving time and effort in model development.
2. Businesses and Organizations: They benefit from improved decision-making based on insights derived from preprocessed data. Clean and integrated data enables better understanding of customer behavior, market trends, and operational efficiencies, leading to enhanced strategies and outcomes.
3. Researchers: AI data preprocessing aids researchers in analyzing large datasets more effectively, allowing them to extract meaningful patterns and correlations. This is particularly valuable in fields such as healthcare, finance, and social sciences, where data analysis plays a crucial role in research advancements.
4. Customers and End Users: Ultimately, customers and end users benefit from AI data preprocessing through improved products and services. For example, personalized recommendations on e-commerce platforms or accurate medical diagnoses based on preprocessed healthcare data enhance user experiences and outcomes.

For comprehensive insights into managing diverse datasets for such preprocessing tasks, our machine learning datasets page can be extremely useful. This process is also one of the initial steps in other data analysis tasks, such as data mining and data analysis. Analytical applications require formatted data that can be understood by the computers and the machine learning model used.

The raw input data that goes into the AI data preprocessing process can be any data, such as text, images, video, and so on. Similarly, it can be unstructured, structured, or a combination of unstructured and structured data. Much of this data comes from various sources that can be gained via data mining and warehousing techniques. An example of transforming raw images into training data for face recognition software can be explored in depth at Clickworker’s case study. Finally, any raw data is transformed into the format and order the ML model requires for optimized data analysis.

Data Preprocessing Steps/Stages

The basic data preprocessing steps in machine learning are:

Data Cleaning

Data cleaning involves basic operations such as filling in the missing values, removing noise, and removing inconsistencies and outliers from the input data. There are many techniques used for each of these operations.

Missing values could be resolved by either ignoring the tuples with missing values or filling them with proper values either manually or through a predictive model.

Noise in data can be handled by using binning, regression, and clustering techniques.

Outliers can be removed by clustering the data into groups.

Data Integration

As mentioned earlier, input data can be aggregated from multiple sources. But doing so would require you to handle the inconsistencies in format and missing values that could arise from combining the various datasets. The data integration part of data preprocessing takes care of this by merging the data from multiple sources into a single data store. This process is similar to how a data warehouse operates.

Data collected from different sources must be integrated into a single large database and then worked upon to smooth out the noise and inconsistencies. Some usual problems you might face when trying to merge datasets could be:

• Schema integration and object matching: Variations in formats and data attributes could make it difficult to merge data into a single database.
• Redundancy: Duplicate and redundant data should be removed from all sources.
• Data value conflicts: Different sources could give conflicting data values for the same attribute, and the correct value must be determined.

Data Transformation

Data consolidated from multiple sources will have to be transformed into a more acceptable format with the help of transformation strategies.

• Generalization

The collected low-level data are transformed into high-level information with the help of concept hierarchies. For instance, address data collected from customer information can be organized into country-level hierarchies.

• Normalization

There are multiple methods to normalize data, such as mi-max normalization, z-score normalization, and decimal scaling normalization. In normalization, the numerical attributes of data are normalized to fit within a particular range of values. Several data points can also be transformed into a single data attribute that fits into an acceptable range of values. Thus, the inconsistencies and differences between various data values are resolved.

For example, when huge numerical values are presented for different attributes, the values can be made to fall under a range of 0 to 1 by applying a common denominator. Take the example of a data set with two features: age and income. Age usually ranges from 0 to 100 values, whereas income values go higher than 6-digit values. These two data features can be normalized in the same range of 0 to 1 using min-max scalar normalization, which is particularly effective when the data distribution is unknown or non-Gaussian, and maintaining the distribution’s original shape is critical. On the other hand, standardization, or Z-score normalization, is used to transform the data into having a mean of 0 and a standard deviation of 1, making it incredibly useful for algorithms that assume normally distributed data.

• Attribute selection

A data set may contain a lot of attributes that the machine learning model does not necessarily consider. There could also be new properties added to the combined dataset. Attribute selection is performed to retain only the required features.

• Aggregation

Aggregation is performed to get a summary of the datasets by correlating one or more features. For instance, a sales dataset can be summarized to show sales data per month or year.

Data Reduction

While it is true that more data means more accuracy, the quality of the data is what counts. Just a huge quantity of redundant data will not help increase the accuracy of learning models. And having a lot of data to process can also slow down the machine-learning model’s performance. One effective approach to maintaining high-quality results without compromising performance is to perform data reduction or sampling during the data preprocessing stage. Techniques such as data cube aggregation, dimensionality reduction through principal component analysis (PCA), data compression, discretization, numerosity reduction, and attribute subset selection help get a reduced quantity of data that delivers the same quality of results. PCA, for example, reduces the number of features while retaining most of the vital information, thus preventing issues like overfitting or underfitting when training machine learning models.

• Data cube aggregation

Data cube aggregation

Data is presented in a summarized format.

• Dimensionality reduction

Dimensionality reduction

This technique allows for extracting only the required features and eliminating redundant features. Techniques such as principal component analysis help reduce the number of features and only retain the necessary ones. Too many features or too few features can cause problems like overfitting or underfitting while training the machine learning models.

Data compression

Data compression helps efficiently store the huge machine learning datasets. These techniques use encoding technologies and can be lossy or non-lossy. If the original data is retained after compression, it is called non-lossy/lossless compression. If any data is lost during the data compression process, it is called “lossy compression.”

Discretization

Data discretization is similar to summarizing data, where data of a continuous nature is divided into groups of particular ranges. For instance, personnel data can be grouped in terms of income brackets.

Numerosity reduction

If data can be simplified and represented as an equation or a mathematical model, it is called numerosity reduction. This method is hugely helpful in reducing the storage space required.

Attribute subset selection

Besides selecting the particular attributes, further optimization can also be achieved by selecting the specific subset attributes of each attribute./p>

Data Quality Assessment

A quality assessment of the data is performed to ensure the input data does not contain any issues. This includes checking for the validity and consistency of data across all its features. As the insights derived from machine learning are used in real-world decision-making, it is of utmost importance that the input data is of high quality. The three main activities involved in data quality assurance are

• Data profiling: Investigating the dataset for any quality issues
• Data cleaning: Fixing the found data issues

Data monitoring: Ensuring that data is maintained in a clean state and continuously checking whether the available data meets its intended needs.

Need help with data preprocessing?

Data preprocessing can be complex and time-consuming. Let Clickworker’s expert team help you prepare high-quality datasets optimized for your machine learning models. We offer comprehensive data preprocessing services to transform your raw data into clean, structured datasets ready for training.

Learn About Our Data Services

Best Practices for optimized Data Preprocessing in Machine Learning

• Get a good understanding of the concept

Before getting into data preprocessing in machine learning, it is important to understand the purpose of the machine learning model under consideration. You need to have a good idea of the exact business needs and expectations you seek to satisfy and correlate them to the data to be collected and processed.

• Make use of statistics and pre-built libraries

Standardized data preprocessing methods such as statistical models and pre-built libraries allow you to save time and have assured results.

• Summarization

Summarizing data in terms of duplicates, missed values, outliers, and so on can give you a good idea of how much effort it takes to pre-process the data. You can thus go ahead with the preprocessing with a good estimate of the resources required.

• Dimensionality reduction to feature engineering

Understanding the problem you intend to solve will help you identify the necessary attributes to design the machine learning model. Using too many unnecessary attributes will slow your models and affect their quality. Make sure to cut down on the attributes used and clarify what is required to make your data preprocessing efficient and faster. Feature engineering helps you achieve this by helping you identify the attributes that are most useful for your machine-learning project.

Data preprocessing thus plays an important role in machine learning, cleaning the raw data and making it suitable for machine learning processing.