We all know data is the new oil. But before it gives us the wealth of intelligence we are after, it needs to be dug out and prepared. This is exactly what data preprocessing is all about.
Understanding the Significance of Data Preprocessing
Companies take data from a variety of sources and in a huge variety of forms. It can be unstructured, meaning texts, images, audio files, and videos, or structured, meaning customer relationship management (CRM), invoicing systems or databases. We call it raw data – data processing solutions unprocessed data that may contain some inconsistencies and does not have a regular form which can be used straight away.
To analyse it using machine learning, and therefore to make huge use of it in all areas of business, it needs to be cleaned and organised –preprocessed, in one word.
So, what is data preprocessing? As such, data preprocessing is a crucial step in data analysis and machine learning pipeline. It involves transforming raw, usually structured data into a format that is suitable for further analysis or training machine learning models with the aim of improving data quality, address missing values, handle outliers, normalise data and reduce dimensionality.
Its main benefits include:
Data preprocessing helps identify and handle issues such as errors and inconsistencies in raw data, resulting in much improved quality of data, which by removing duplicates, correcting errors and addressing missing values becomes more accurate and reliable.
Raw data often have missing values, which can pose challenges during analysis or modelling. Data preprocessing includes imputation (replacing missing values with estimated values) and deletion (removing instances or features with missing data), which address that problem.
- Outlier detection and handling
Outlier means data points that significantly deviate from the normal patterns on a dataset – they can be a result of errors, anomalies, or rare events. Data preprocessing helps to identify and handle them by removing or transforming them or treating them separately based on the analysis or model’s requirements.
- Normalisation and scaling
Normalisation of data ensures all features have similar ranges and distributions, preventing certain features from dominating others during analysis or modeling. Scaling brings the data within a specific range, making it more suitable also for machine learning algorithms.
High dimensional datasets can pose challenges for analysis and modeling, leading to increased computational complexity and the risk of overfitting. Dimensionality reduction allows to reduce the number of features while retaining the most relevant information, which simplifies the data representation and can improve model performance.
Feature engineering involves creating new features from existing ones or transforming features to improve their relevance or representation, helping capture important patterns or relationships in the data that might be missed by raw features alone, leading to more effective models.
Different machine learning algorithms have specific assumptions and requirements about the input data. Data preprocessing ensures that the data is in a suitable format and adheres to the assumptions of the chosen model.
Preprocessing ensures that data used for analysis is accurate, consistent, and representative, leading to more reliable and meaningful insights. It reduces the risk of drawing incorrect conclusions or making flawed decisions due to data issues.
The Data Preprocessing Process and Major Steps
The data preprocessing process typically involves several major steps to transform raw data into a clean format, suitable for analysis or machine learning. While the steps may vary depending on the dataset and the specific requirements of the analysis or modeling task, the most common major steps in data preprocessing include:
The first step is to gather the raw data from various sources, such as databases, files, or APIs. The data collection process can involve extraction, scraping, or downloading data.
Data Cleaning
This step focuses on identifying and handling errors, inconsistencies, or outliers in the data. It involves tasks such as:
- removing duplicate records – identifying and removing identical or nearly identical entries;
- correcting errors – identifying and correcting any errors or inconsistencies in the data;
- handling missing data – addressing missing values in the dataset, either by imputing estimated values or considering missingness as a separate category;
- handling outliers – detecting and handling outliers by either removing them, transforming them, or treating them separately, based on the analysis or model requirements.
Data Transformation
In this step, data is transformed into a suitable format to improve its distribution, scale, or representation. Transformations based on information included in data should be done before the train-test split, on training data, after which transformation can be moved to the test set straight away. Some common data transformation techniques include:
- feature scaling – scaling the numerical features to a common scale, such as standardisation or min-max scaling;
- normalisation – ensuring that all features have similar ranges and distributions, preventing certain features from dominating others during analysis or modeling;
- encoding categorical variables – converting categorical variables into numerical representations that can be processed by machine learning algorithms. This can involve techniques like one-hot encoding, label encoding, or ordinal encoding;
- text preprocessing – for textual data, tasks like tokenisation, removing stop words, stemming or lemmatisation, and handling special characters or symbols may be performed;
- embedding – meaning representing textual data in a numerical format.
Feature Selection / Extraction
In this step, the most relevant features are selected or extracted from the dataset. The goal is to reduce the dimensionality of the data or select the most informative features using techniques like principal component analysis (PCA), recursive feature elimination (RFE), or correlation analysis.
If multiple datasets are available, this step involves combining or merging them into a single dataset, aligning the data based on common attributes or keys.
It is common practice to split the dataset into training, validation, and test sets. The training set is used to train the model, the validation set helps in tuning model parameters, and the test set is used to evaluate the final model’s performance. The data splitting ensures unbiased evaluation and prevents overfitting.
Dimensionality reduction is used to reduce the number of features or variables in a dataset while preserving the most relevant information. Its main benefits include improved computational efficiency, mitigating the risk of overfitting and simplifying data visualisation.
Summary: Data Preprocessing Really Pays Off
By performing effective data preprocessing, analysts and data scientists can enhance the quality, reliability, and suitability of the data for analysis or model training. It helps mitigating common challenges, improving model performance, and obtaining more meaningful insights from the data, which all play a crucial role in data analysis and machine learning tasks. It also helps unlock the true potential of the data, facilitating accurate decision-making, and ultimately maximising the value derived from the data.
After data preprocessing, it’s worth using Feature Store – a central place for keeping preprocessed data, which makes it available for reuse. Such a system saves money and helps managing all work.
To make the most out of your information assets and learn more about the value of your data, get in touch with our team of experts, ready to answer your questions and to advice you on data processing services for your business. At Future Processing we offer a comprehensive data solution which will allow you to transform your raw data into intelligence, helping you make informed business decisions at all times.
By Aleksandra Sidorowicz
