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An Ultimate Guide to Data Preprocessing

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3rd Feb, 2022
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An Ultimate Guide to Data Preprocessing

In 2020, 59 Zettabytes of data had been generated, consumed, recorded, and duplicated, according to the 2020 International Data Corporation. This prediction becomes even more intriguing when we go back to 2012 when IDC predicted that the digital world would reach just 40 zettabytes by 2020, cumulatively. Given the rapid pace at which data is being produced and processed, every statistical prediction of earlier has been proved wrong. 

The primary explanation for the large discrepancy between actual and predicted numbers is the COVID-19 epidemic. Everyone went online due to the global quarantine, and data creation skyrocketed. This necessitates AI technology users to effectively manage and optimize data to glean lucrative insights. It is now predicted that data on the global platform will hit 175 Zettabytes by 2025. 

What is Data Preprocessing?

Converting raw data into an understandable format is known as data preprocessing. We can’t work with raw data. Thus, this proves to be an important stage in data mining.

After obtaining the data, further investigation and assessment are required to detect significant interesting trends and discrepancies. The following are the key objectives of Data Quality Assessment:

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  • A comprehensive overview: It begins with putting together an overview by understanding the data formats and the complete structure and format in which the data is stored. In addition to this, we determine the various data attributes, including mean, median, standard quantiles, and standard deviation. 
  • Identifying missing data: In almost every real-life dataset, missing data is expected. If there are a few absent cells present in a given dataset, it can cause a significant disturbance in the actual data trends, resulting in severe data loss, especially when it leads to entire rows and columns being eliminated.
  • Identification of outliers or anomalous data: Specific data points are known to marginally deviate from the norm. These points are outliers and must be deleted to glean more accurate forecasts unless the algorithm’s primary goal is to identify abnormalities.
  • Inconsistency removal: In addition to missing information, real-world data contains a variety of anomalies, such as incorrect spellings, wrongly populated columns and rows (for example, salary populated in the gender column), duplicated data, etc. These irregularities can sometimes be rectified by automation, but most of the time, they require manual inspection.

Importance of Data Preprocessing

Data preprocessing aims to ensure that the data is of good quality. The criteria are: 

  • Accuracy: To determine whether or not the data entered is correct.
  • Checking for completeness: To check for the availability of the relevant data. 
  • Checking for consistency: By checking if the same information is retained in all areas that match or don’t match.
  • Regularity: Data should be updated regularly.
  • Trustworthiness: The data should be reliable.
  • Data interpretability: The data’s ability to be comprehended.

Steps of Data Preprocessing | How is Data Preprocessing in Machine Learning Carried Out?

Step 1: Data Cleaning

Data cleaning is the practice of removing erroneous, incomplete, and inaccurate data from datasets and replacing missing information. There are a few methods for cleaning data:

Missing value handling:

  • Missing variables can be replaced with standard values such as “NA.”
  • Users can manually fill in missing values. However, this is not suggested if the dataset is large.
  • When the data is regularly distributed, the attribute’s mean value can replace the missing value.
  • In the case of a non-normal distribution, the attribute’s median value is employed.
  • The missing value can be replaced with the most potential value using regression or decision tree methods.

Noisy: Noisy is a term that refers to random errors or data pieces that aren’t needed. Here are a few approaches to dealing with noisy data.

Binning: It is a technique for smoothing or handling noisy data. The data is first sorted, after which the sorted values are segregated and stored in bins. In the bin, there are three ways of smoothing data.

  • Using the bin mean approach for smoothing: The values in the bin are replaced by the bin’s mean value in this manner. 
  • Smoothing by bin median: The values in the bin are replaced with the median value in this approach.
  • Smoothing by bin boundary: This approach takes the minimum and maximum bin values and replaces them with the nearest boundary value.

Regression: It is a technique for smoothing data and handling data when there is excess data. Purpose regression aids in determining which variable is appropriate for our investigation.

Clustering: It is a technique for identifying outliers and grouping data. Clustering is a technique that is commonly employed in unsupervised learning.

Step 2: Data Integration 

The process of combining data and information gleaned from different sources to produce a single dataset is typically referred to as data integration. One of the most important aspects of data management is the data integration process, which includes:

  • Schema integration: It combines metadata (a collection of data that describes other data) from many sources.
  • Identification of entities: Identifying items from numerous databases is a difficult challenge. For example, the system or the user should be aware that a database’s student _id and another database’s student name pertain to the same entity.
  • Detecting and resolving notions of data value: When integrating data from multiple databases, the results may differ. For example, attribute values in one database may vary from those in another.

Step 3: Data Reduction

This procedure aids in the decrease of data volume, making analysis easier while producing the same or almost the same results.

  • Data compression: The term used to describe the compressed version of data. We can use lossless or lossy compression. Lossless compression occurs when no data is lost during the compression process. While lossy compression decreases data, it eliminates the information that isn’t needed.
  • Dimensionality reduction: Because the data size in real-world applications is enormous, dimensionality reduction is required. The removal of random variables or characteristics is done to lower the dimensionality of the data collection. Data attributes are combined and merged without losing their original properties. The “Curse of Dimensionality” is an issue that emerges when data is very dimensional.
  • Numerosity Reduction: This approach reduces the amount of data representation to make it smaller. There will be no data loss as a result of this decrease.

Step 4: Data Transformation

The process of changing the format or organization of data is known as data transformation. This method might be simple or complex, depending on the requirements. The following are some examples of data transformation methods:

  • Normalization: is the process of scaling data to present it in a more limited range. As an example, consider a range of -1.0 to 1.0.
  • Smoothing: We may use techniques to remove noise from a dataset, which helps us uncover the dataset’s core qualities. By smoothing, we can detect even the tiniest change that assists prediction. In this example, the continuous data is discretized into intervals. 
  • Discretization: In this example, the continuous data is discretized into intervals. The amount of data is reduced when it is discretized. We could provide a break instead of specifying the class time (e.g., 3 pm-5 pm, 6 pm-8 pm).
  • Aggregation: In this case, the data is maintained and displayed in the form of a summary. The data set is integrated with the data analysis description, which originates from many sources. It is an essential step since the quantity and quality of the data impact the accuracy of the data.


This article is for data science enthusiasts who wish to pursue a career in the same niche. If you possess basic knowledge of data analytics, you can apply concepts of data preprocessing to real-world scenarios and increase your chances of success in the field.

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