Data Preprocessing: What it is, Steps, & Methods Involved

Organizations face various challenges while integrating their in-house data sources for insights generation. Data extracted from various sources is often incomplete—containing outliers, missing values, and other data quality inconsistencies. These issues cause you to spend more time cleaning data before performing an analysis.

This article will guide you through the process of data preprocessing, a series of steps and techniques that can be used to process the raw data obtained and make it more suitable for analysis or modeling.

What is Data Preprocessing?

Data preprocessing is the first step of a data analysis process. This method involves preparing data so that it can be made ready for analysis and modeling. You must prepare and transform the raw data in a format that is easy to interpret and work with.

Data preprocessing is one of the most critical steps of any machine learning pipeline. This method requires more time and effort as it transforms the raw, messy data into a better, easily understandable, and structured format.

Why Consider Data Preprocessing?

You must be wondering, “Why consider data preprocessing in the first place?” The simple answer is that since the data is acquired from different resources, most of it is not fit for analysis. It probably consists of null, missing, and other discrepant values.

If these discrepancies are directly included in the analysis process, they might lead to biased insights and wrong conclusions that lack the true essence of your analysis. Hence, data preprocessing becomes the most critical step that every data professional considers before getting into the nitty-gritty of data analysis.

Here are a few of the benefits of data preprocessing:

• Noise Reduction: Data preprocessing eliminates errors in the dataset, reducing the noise produced by inconsistencies. It also makes it easier for machine learning algorithms to find patterns in the dataset and make accurate predictions.‍
• Handling Categorical Data: Certain machine learning algorithms require the data to be present numerically rather than in categorical form. Data preprocessing enables categorical data to be encoded into numerical data so that it can become compatible with the algorithm.‍
• Normalization of Data: Data preprocessing helps normalize the data so that the data can be converted into equalized scale values. This will ensure no single feature has more dominance over others during the data modeling step.‍
• Dimensionality Reduction: When dealing with higher-dimensional data, managing the data features that do not significantly contribute to the analysis' outcome becomes necessary. Data preprocessing reduces the extra features that increase the computation during the modeling step without contributing to the analysis.

What are the Steps Involved in Data Preprocessing?

Here are some of the most common data preprocessing methods:

Data Integration

Data might be present on a range of platforms and in different formats. To comprehensively understand it, you can integrate data from various sources. However, you need to perform specific methods like record linkage or data fusion while extracting the data from different sources.

You must check the different data types involved in each dataset and the semantics of the dataset where you will store the final data. This way, your integration process can be carried out seamlessly.

You can merge all your data into a single location so that it can be accessed from a single location. Before merging data from different sources, you must check for any differences in the data coming in.

• Record Linkage: You can use the record linkage technique to link records that refer to the same entity across different datasets.‍
• Data Fusion: Through this method, you can combine information from different sources into a single comprehensive dataset. This enhances the data quality and completeness.

Data Transformation

In most real-world scenarios, the data you work with needs to be transformed before insights are generated. This involves data cleaning steps, the conversion of datasets into a suitable format for modeling, data standardization, normalization, and discretization.

Data cleaning is the most fundamental step in data transformation. This step allows you to clean your dataset, enhancing its usability for gaining insights into it. While data cleaning can be considered a major step in data transformation, it is necessary to understand the distinction between data cleaning vs data preprocessing.

Data cleaning involves identifying and removing errors or inconsistencies in the dataset. On the other hand, data preprocessing comprises a broader range of tasks, including data cleaning. The process also involves functions like data integration, data transformation, and data reduction.

You can cleanse the data by removing inconsistencies, such as null values, anomalies, and duplicate values. Various methods can be applied to clean datasets, including direct removal of the value or filling up the value with some statistical alternative.

Data cleaning and data preprocessing both ensure data quality and reliability. By removing irrelevant and redundant data, data decision-making improves significantly.

There are multiple methods that are used for data cleaning purposes. A few of such methods are:

• Outlier Detection: There are several methods for detecting outliers and removing them if they are present. These include using statistical approaches like Z-Score, interquartile range, and other machine learning-based methods.‍
• Handling Missing Values: You can impute the missing values with the data feature's mean, median, or mode. Machine learning models, such as K Nearest Neighbor, can also be used in this case.‍
• Removing Duplicate Data: If duplicate data values are present, you can remove them to reduce the influence of certain data points and the production of biased data.

Once your data is cleaned, you can apply various data transformation methods to prepare it for further analysis. You can use data standardization and normalization to convert your data into a specific range of values, such that no feature is given more importance and the contribution of every metric is essential.

Here’s a toolkit of powerful data transformation methods:

• Feature Scaling Method: This method involves scaling the feature values to fit within a specific range of values. The most commonly used techniques are normalization and standardization of data.
• Encoding Categorical Features: Machine learning algorithms often require the features to be numeric. Therefore, encoding categorical data becomes an essential step. This method involves one-hot encoding, label encoding, and binary encoding.‍
• Data Discretization Method: This method converts continuous data into discrete data. KBinsDiscretizer is one of the most widely used data discretization techniques. Some machine learning algorithms demand discrete data values rather than continuous data.

Data Reduction

Data Reduction is an important part of data preprocessing. It revolves around reducing the amount of data while maintaining the core identity that your data represents. This step involves dimension reduction, data compression, and feature selection. Each of these steps saves storage space, increasing the quality of data and making the data modeling process more efficient.

If the data you are working with is highly dimensional and has many features, you can use dimensionality reduction. This will reduce your data features while maintaining their original characteristics.

You can use feature selection to select certain features out of all the present features based on statistical or other relevance metrics. Data compression can be used to compress the original data into a shorter form.

You can perform data reduction using these methods:

• Dimensionality Reduction: This method reduces the storage of data by reducing the dimensions considered in the dataset. The most commonly used technique here is Principal Component Analysis (PCA).‍
• Feature Selection: It is used in most real-world applications where the features present in the dataset are not significant. This method involves statistical measures like correlation to determine which feature contributes the least to the dataset and remove it, as well as predictive modeling to filter a feature subset.

Conclusion

Data preprocessing is necessary before making sense of data since the data you work with might be biased and produce distorted information. This involves removing missing data, integrating data from various sources, transforming data, and reducing data.

Although you must be aware of specific pinpoints while performing data preprocessing, the benefits of preprocessed data are undeniably significant. This process can enhance the accuracy of your analysis.