Why Data Preprocessing Is Needed for Machine Learning Functions

Data preprocessing

Data preprocessing in machine learning refers to the transformation of unstructured, ambiguous, and error-filled data into a clean and coherent version that can be used for further processing and manipulation. Preprocessing filters out data issues early on in the pipeline so that only valuable data elements remain. 

This data mining technique deals with issues like data duplication, incompleteness, broken data, ambiguity, inadequacy, mislabeling, miscategorization, corruption, etc. It improves the quality of training datasets for machine learning algorithms while also preventing the harm caused by common data errors. And, it plays a critical role in designing well-informed AI/ML models. 

Here’s a guide to help you unravel the advantages, needs, and challenges associated with data preprocessing and its contribution to machine learning. Additionally, we’ll also discuss how outsourcing data preprocessing and data enrichment services can help expedite the growth of an AI/ML project.

Table of Contents:

What Is Data Preprocessing In Machine Learning?

Every bit of data that goes into training a machine learning system should ideally help improve the model’s decision-making capability. Any data deficiency will ruin the model’s ability to adapt and perform in real-world scenarios, especially in critical scenarios like autonomous driving. 

Therefore, the data used to train an AI/ML algorithm must be clean, structured, and intent-appropriate. This is where data preprocessing is needed. 

Data Preprocessing Steps In Machine Learning

While every ML/AI modeling requirement is different, there are a few that are a must to produce a basic data quality level. These may be repeated in a cycle until the desired quality is obtained. Alternatively, each of them may be conducted repeatedly until they produce the desired effect on the data sets. The strategy employed will vary based on the application and feasibility.

Here are the four main data preprocessing steps in machine learning that are crucial to any model training operation: 

1. Data Cleansing

As mentioned earlier, data cleansing is the primary means of solving multiple data issues like inconsistency, missing data, outlier entries, etc. The process may be conducted right after data collection or anytime after the data has been stored in a database. Real-time cleansing, in particular, has to be done using machine learning algorithms as the dynamics of the input data can vary haphazardly. 

Data cleansing works in the following manner:

  • For Missing Data

    Data entries might get deleted accidentally, or there could be an error during transmission from the source. 

    If many missing values exist in a large data set, then those present within a tuple (a data type used to store data collections) are ignored. In other cases, the missing data is added by studying the data carefully and/or extracting the data set again. This may be done manually or with methods like regression and numerical ones like attribute mean. 

  • For Inconsistencies

    Every random error or variance present in a variable is measured and removed. This data preprocessing technique in machine learning serves multiple functions. 

    Data binning is one function where data gets divided into equally-sized “bins” to be dealt with individually. It is used on sorted data values, and the incorrect data gets replaced with its boundary, median, or mean counterparts. Another function is regression which finds immense use with prediction. Either a linear or polynomial regression equation is used to fit the data points depending on whether single or multiple attributes are being considered. 

  • For Outlier Removal

    Clustering is a function where data with similar values are grouped, and those lying outside the range of coverage are removed as noisy data.

    It’s important to note that cleaning a dataset is a very sensitive issue. Even a single bias or error can wreck havoc in your dataset and hence, in the ML algorithm. Therefore, it is preferable to go for professional data cleansing services as they are more likely to ensure accuracy on all fronts. 

2. Data Integration

Integration is a data preprocessing technique where heterogeneous datasets are combined and cleaned to create a single, coherent, and consolidated database. It plays a critical role in creating a single data store for machine learning algorithms by dealing with varying data formats, redundant attributes, and data value conflicts. 

3. Data Transformation

Transformation of the input ML data is required to consolidate the cleaned data into varying forms with the alteration of its value, structure, or format. This data preprocessing step in machine learning uses the following functions to accomplish it:

  • Normalization

    By far the most important transformation function, normalization correlates different data points by dividing them into containers based on certain attributes. The attribute numbers are scaled up or down to fit the data within a required range. Normalization is done using the Min-Max, Z-Score, and Decimal Scaling algorithms. 

  • Generalization

    This function is used to transform low-level data into high-level information. Concept hierarchies are used for this purpose to club primitive data forms under an evolved category.  

  • Attribute Selection

    Attribute selection aids the data mining process by creating new data properties. Existing data attributes are used as the reference points for the creation process. 

  • Data Aggregation

    Preprocessing in machine learning works well when the data it uses is summarized into salient points. Aggregation achieves this by storing and presenting data in such a format. It is very useful in generating reports based on various criteria that aids the model verification process performed later in the pipeline. 

    Preprocessing in machine learning works well when the data it uses is summarized into salient points. Aggregation achieves this by storing and presenting data in such a format. It is very useful in generating reports based on various criteria that aids the model verification process performed later in the pipeline. 

4. Data Reduction

Too much data can affect the data warehousing capabilities of a company as well as the analysis and mining algorithms acting upon them. The solution is to reduce the data quantity using various functions without affecting the overall data quality. For data preprocessing in machine learning, we commonly use the following data reduction functions:

  • Dimensionality Reduction

    This function is performed to extract features present in data sets and remove the redundant ones. 

  • Compression

    Data can be compressed using various encoding techniques for easier retrieval and processing. 

  • Discretization

    Continuous data is less likely to have good correlation chances with the targeted variables, making result interpretation difficult. Discretization creates target variable-specific groups that make it easy to interpret the data. 

  • Numerosity Reduction

    Data models are compact and easy to interpret, but they tend to add bulk to the storage. The alternative is to reduce them into equations and simplify storage.

  • Attribute Subset Selection

    One of the best ways to avoid the overload or underload of attribute numbers is to balance them. Judicious attribute selection adds value to the model training process as the unwanted ones are discarded.

5. Data Quality Determination

Data quality assessment is conducted using statistical methods to maintain accuracy. Data  quality assessment focuses on factors like completeness, accuracy/reliability, consistency, validity, and lack of redundancy and is done with the help of data profiling, cleaning, and monitoring. However, multiple sweeps may be required to achieve the expected data quality. 

Get Preprocessed Datasets For Precise AI/ML Model Training At Affordable Prices

Contact Our Team Now For A Free Trial

The above steps will yield the best results if they are augmented with some of the best practices for data preprocessing. 

  • Using a robust strategy that’s aligned with business goals can help streamline the entire pipeline and avoid wastage of resources. 
  • Preprocessing becomes easier with the use of pre-built libraries and statistical methods. They help provide the visualization needed to obtain the full picture of your data warehouse based on various attributes.
  • Assuring high-quality datasets involves caution. So, when working on data preprocessing in machine learning, taking early stock of data errors helps in creating a better strategy. 
  • Create summaries or data sets that can be correlated and worked upon easily instead of having a gigantic heap of unclassified data. 
  • Remove all unwanted attributes/fields to make the database lean. Dimensionality reduction plays a significant role in making ML model training efficient. 
  • Perform updates and QA often to keep the content fresh and relevant. Also, update the software/algorithms used for preprocessing to improve their efficiency and accuracy.

The Importance Of Data Processing In Machine Learning

Big data is an ever-increasing phenomenon and a real boon for ML development. Developers needn’t be concerned with falling short of the required quantity and variety of data to train the models. 

However, this also means the potential to gain erroneous data is high. Feeding such data into the model will jeopardize the model’s training and hamper its predictive abilities severely. This facet of Ml modeling is all the more pertinent because 69% of companies struggle with problems due to bad data. Therefore, multiple data preprocessing techniques in machine learning are employed to counter the data issues. 

There are multiple benefits to be gained by preprocessing ML training and test data, like

  • Reduced storage requirements
  • Decreased load and retrieval times
  • Cost savings from increased prediction accuracy and decreased time
  • Lack of delay in generating results due to reduced dimensionality and errors
  • Improved overall business operations performance due to better decision-making
  • Greater market insights from better AI market data processing
  • Increased multitasking as processing power is reduced due to missing unwanted attributes and data bulk. 

Data preprocessing in machine learning helps get the best out of every Ml model under construction or use. But the complexity and cost considerations make it a rather taxing process for a business, especially if it’s unfamiliar with data mining. Such businesses may choose to outsource data preprocessing to a professional agency. Doing so will provide further cost reductions and ensure faster results. 

You can choose the ideal outsourcing partner by studying their history, services offered, client list, customer testimonials, budget, customer experience, approach to the latest trends and technologies, etc. By selecting the right partner for data preprocessing in machine learning, you can expect to gain the best dataset for AI/ML training requirements.

Outsource Your Data Preprocessing Needs To Data-Entry-India.com

With 20+ years of experience in IT outsourcing and a deep-seated presence in the data annotation and mining niche, we can fulfill every data preprocessing requirement with ease. 

With a rich array of data cleansing, enrichment, and data standization services, we remove all issues from your database and make it an actionable asset for you. With us, you can gain access to 150+ full-time data annotators and experts for fast and accurate data preprocessing. 

Send your requirements enquiries, or queries at info@data-entry-india.com or call us on: +1 585 283 0055 | +44 203 514 2601|  +919311468458.


1. What’s the difference between AI and ML?

Artificial intelligence is a technology that enables automation and allows any system to mimic human intelligence. Machine Learning is a subset of AI where machines are modeled to behave like humans. An ML model must be trained to recognize various variables in any environment through a dataset that represents the environment and all its variables through labeling. 

2. What are the differences between Data Integration and Data Normalization? 

Data integration is an independent preprocessing step, while data normalization falls under the data transformation step. Integration is the act of putting together different datasets that may be scattered across databases using a chosen attribute. Normalization, on the other hand, finds correlations between different data sets based on common attributes. 

3. How are data quality conditions determined for ML data preprocessing?

Some data quality conditions are standard across the industry, like minimal data inconsistencies, missing data, broken links, redundancies/duplicates, ambiguity, hiding, etc. However, specific quality standards can be drawn depending on the use case of the machine learning model. 

The Data-Entry-India.com Blog

Brought to you by the Marketing & Communications Team at Data-Entry-India.com. We love sharing interesting guides & trends on eCommerce & Data Entry Services. Join our newsletter to know about the latest industry trends & market updates.

Comments are closed.