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Idan Novogroder
Idan Novogroder Author

Idan has an extensive background in software and DevOps engineering....

Last updated on April 30, 2024

Data is a valuable asset to any company today. But can you really use this massive amount of data in its raw form to train ML algorithms? Not really. Most of the time, you’re looking at noisy data full of missing data points. This is where data preprocessing comes in.

Data in the actual world is quite messy – full of errors, noise, partial information, and missing values. It’s also inconsistent, often compiled from many sources using data mining and warehousing techniques. In machine learning, the general rule is that the more data you have, the better machine learning models you can train. However, the data needs to be of high quality.

This is why data preprocessing makes up a significant portion of the daily jobs of data practitioners, who dedicate around 80% of their time to data preprocessing and management.

In this article, we dive into the details of data preprocessing in machine learning to show you key steps and best practices for improving your data quality.

What is Data Preprocessing in Machine Learning?

Data preprocessing is the process of evaluating, filtering, manipulating, and encoding data so that a machine learning algorithm can understand it and use the resulting output. The major goal of data preprocessing is to eliminate data issues such as missing values, improve data quality, and make the data useful for machine learning purposes.

Why is Data Preprocessing Important?

Data-driven algorithms are statistical equations that operate on database values. As the adage goes, “If garbage goes in, garbage comes out.” Your data project will only be as successful as the input data you feed into your machine learning algorithms.

Since a variety of people, business processes, and applications frequently produce, process, and store real-world data, it’s bound to get chaotic. This is usually the result of manual errors, unanticipated occurrences, technological faults, or several other factors. Algorithms can’t ingest incomplete or noisy data because they’re typically not built to manage missing values. And noise disrupts the sample’s real pattern.

This is why data preprocessing is required for almost all types of data analysis, data science, and AI development to produce trustworthy, precise, and resilient findings for corporate applications.

Why is machine learning data preparation so important? Machine learning and deep learning algorithms perform best when data is presented in a way that streamlines the solution to a problem.

Data wrangling, data transformation, data reduction, feature selection, and feature scaling are all examples of data preprocessing approaches teams use to reorganize raw data into a format suitable for certain algorithms. This can significantly reduce the processing power and time necessary to train a new machine learning or AI system or perform an inference against it.

There’s good news! Most of the current data science packages and services now contain preprocessing libraries that automate many of these activities.

Here are the reasons why data preprocessing is so important for machine learning projects:

It Improves Data Quality

Data preprocessing is the fast track to improving data quality since many of its steps mirror activities you’ll find in any data quality management process, such as data cleansing, data profiling, data integration, and more.

It Handles Missing Data

There are several reasons why a data collection may be missing values (particular fields of data). Data practitioners must determine if it’s best to reject records with missing values, ignore them, or fill them in with an estimated value.

It Normalizes and Scales Data

Dependent and independent variables change on separate scales, or one changes linearly while another changes exponentially. Salary, for example, might be a multiple-figure digit, whereas age is expressed in double digits. Normalizing and scaling help to modify data in a way that allows computers to extract a meaningful link between these variables.

It Eliminates Duplicate Records

When two records appear to repeat, an algorithm must identify whether the same metric was captured twice or whether the data reflects separate occurrences. In rare circumstances, a record may have minor discrepancies due to an erroneously reported field. Techniques for finding, deleting, or connecting duplicates help to address such data quality issues automatically.

It Handles Outliers

Data practitioners sometimes need to merge many data sources to construct a new machine learning model. Principal component analysis, for example, is an important technique for lowering the number of dimensions in the training data set and producing a more efficient representation.

It Helps in Enhancing Model Performance

Preprocessing often entails developing new features or modifying existing ones to better capture the underlying problem and enhance model performance. This might include encoding category variables, developing interaction terms, and retrieving pertinent data from text or timestamps.

7 Data Preprocessing Steps in Machine Learning

1. Acquire the Dataset

Naturally, data collection is the first step in any machine learning project and the first among the data preprocessing steps. Gathering data might seem like a straightforward process, but it’s far from that.

Most companies end up with data kept in silos and divide it across many departments, teams, and digital solutions. For example, the marketing team might have access to a CRM system, but that system may operate in isolation from the web analytics solution. Combining all data streams into consolidated storage will be challenging.

2. Import Libraries

Next, it’s time to import the libraries you’ll need for your machine learning project. A library is a collection of functions that an algorithm can call and utilize.

You can streamline data preprocessing procedures using tools and frameworks that make the process easier to organize and execute. Without certain libraries, one-liner solutions might take hours to code and optimize.

3. Import Datasets

The next key step is to load the data that will be utilized in the machine learning algorithm. This is the most critical machine learning preprocessing step.

Many companies start by storing data in warehouses that require data to pass through an ETL. The problem with this method is that you never know which data will be useful for an ML project. As a result, warehouses are commonly used to access data through business intelligence interfaces in order to observe metrics that we know we need to monitor.

Data lakes are used for both structured and unstructured data, including photos, videos, voice recordings, and PDF files. However, even when data is structured, it’s not transformed prior to storage. You load the data in its present condition and then decide how to use and alter it later.

4. Check for Missing Values

Evaluate the data and look for missing values. Missing values can break actual data trends and potentially result in additional data loss when entire rows and columns are deleted due to a few missing cells in the dataset.

If you discover any, you can choose from two methods to deal with this issue:

  • Remove the whole row with a missing value. However, eliminating the full row increases the likelihood of losing some critical data. This strategy is beneficial if the dataset is massive.
  • Estimate the value using the mean, median, or mode.

5. Encode the Data

Non-numerical data is incomprehensible to machine learning modules. To avoid issues later on, the data should be arranged numerically. The answer to this problem is to convert all text values to numerical form.

6. Scaling

Scaling is unnecessary for non-distance-based algorithms (such as the decision tree). Distance-based models, on the other hand, require all features to be scaled.

These are some of the more common scaling approaches:

Scaling Approach Description
Min-Max Scaler It reduces the feature values between any range of options (for example, between zero and four)
Standard Scaler It assumes that the variable is normally distributed and then scales it down until the standard deviation is one and the distribution is centered at zero
Robust Scaler It performs best when the dataset contains outliers. After eliminating the median, the data is scaled based on the interquartile range
Max-Abs Scaler Similar to the min-max scaler, except instead of a certain range, the feature is scaled to its greatest absolute value

7. Split Dataset Into Training, Evaluation and Validation Sets

This is the final step among the data preprocessing steps. It’s time to divide your dataset into training, evaluation, and validation sets. The training set is the data you’ll use to train your machine learning model. The evaluation set will assess the data and the model, while the validation set will validate it.

Data Preprocessing Examples and Techniques

Data Transformation

One of the most important stages in the preparation phase is data transformation, which changes data from one format to another. Some algorithms require that the input data be changed – if you fail to finish this process, you may receive poor model performance or even introduce bias.

For example, the KNN model uses distance measurements to determine which neighbors are closest to a particular record. If you have a feature with a particularly high scale relative to the other features in your model, your model will likely employ this feature more than the others, resulting in a bias.

Feature Engineering

The feature engineering strategy is used to produce better features for your dataset, which will improve the model’s performance. We mostly employ domain knowledge to produce those features, which we manually generate from existing features after applying a transformation to them.

Here are some simple examples to help you understand this:

Imagine that you have a hair color feature in your data with values of brown, black, or unknown. In this scenario, you may add a new column named “has color” and assign 1 if there is a color and 0 if the value is unknown.

Another example is deconstructing a date/time feature, which provides significant information but is difficult for a model to use in its original format. So, if you believe your problem involves temporal dependencies and you discover a link between the date/time and the output variable, spend some time trying to turn that date/time column into a more intelligible feature for your model, such as “period of the day,” “day of the week,” or so on.

Imbalanced Data

One of the most prevalent issues you may encounter while working with real-world data categorization is that the classes are unbalanced (one contains more samples than the other), resulting in a significant bias for the model.

Imagine that you’d like to forecast if a transaction is fraudulent. Based on your training data, 95% of your dataset consists of legitimate transaction records, whereas just 5% consists of fraudulent transactions. Based on this, your model will most likely forecast the majority class, identifying fraudulent transactions as usual.

To solve this weakness in the dataset, you can use three techniques:

  • Oversampling – Oversampling is the technique of augmenting your dataset with generated data from the minority class. The Synthetic Minority Oversampling Technique (SMOTE) is the most commonly used method for doing this; it selects a random sample from the minority class.
  • Undersampling – Undersampling is the process of lowering a dataset and eliminating genuine data from the majority class. The two primary algorithms used in this method are TomekLinks, which eliminates observations based on the nearest neighbor, and Edited Nearest Neighbors (ENN).
  • Hybrid Oversampling – The hybrid strategy incorporates both oversampling and undersampling strategies in your dataset. One of the methods used in this technique is the SMOTEENN, which uses the SMOTE algorithm for minority oversampling and the ENN algorithm for majority undersampling.

Sampling Data

The more data you have, the higher the model’s accuracy. Still, some machine learning algorithms may struggle to handle a large quantity of data, resulting in issues such as memory saturation, computational increases to update the model parameters, etc.

To overcome this issue, you can use the following sample data techniques:

Technique Description
Sampling without replacement This method prevents repeating the same data in the sample, so if a record is chosen, it’s deleted from the population
Sampling with replacements This method doesn’t remove the object from the population and may be used several times for sample data because it can be picked up more than once
Stratified sampling This is a more sophisticated approach that involves partitioning the data and taking random samples from each partition. In circumstances where the classes are disproportional, this method maintains the proportionate number of classes based on the original data
Progressive sampling This last strategy starts with a tiny dataset and gradually increases it until a suitable sample size is achieved

Data Preprocessing Best Practices

1. Data Cleaning

The goal here is to identify the simplest solution to correct quality concerns, such as removing incorrect data, filling in missing data, or ensuring the raw data is appropriate for feature engineering.

2. Data Reduction

Raw data collections often contain duplicate data resulting from diverse methods of defining events, as well as material that just doesn’t work for your machine learning architecture or project scope.

Data reduction techniques, such as principal component analysis, are used to convert raw data into a simplified format that is appropriate for certain use cases.

3. Data Transformation

Data scientists consider how different components of the data should be structured to achieve the best results. This might entail arranging unstructured data, merging salient variables where it makes sense, or determining which ranges to focus on.

4. Data Enrichment

In this stage, data practitioners use various feature engineering libraries on the data to achieve the needed changes. The end result should be a data set arranged in such a way that it strikes the best balance between training time for a new model and compute requirements.

5. Data Validation

Data validation starts with separating data into two sets. The first set is used to train a machine learning or deep learning algorithm. The second one serves as the test data, which is used to assess the correctness and robustness of the final model. This second stage helps to identify any issues with the hypothesis used in data cleaning and feature engineering.

If the team is pleased with the results, they may assign the preprocessing assignment to a data engineer, who will choose how to scale it for production. If not, the data practitioners can go back and adjust how they carry out the data cleaning and feature engineering procedures.

Data Preprocessing with lakeFS

Keeping track of many versions of data might be equally challenging. Without proper coordination, balance, and precision, everything can fall apart easily. And this is the last place you want to be when starting a new machine learning project.

Data versioning is a method that allows you to keep track of many versions of the same data without incurring significant storage expenses.

Creating machine learning models takes more than simply executing code; it also calls for training data and the appropriate parameters. Updating machine learning models is an iterative process where you need to keep track of all previous modifications.

Data versioning enables you to keep a snapshot of the training data and experimental results, making implementation easier at each iteration.

Versioning data also helps to achieve compliance. Almost every company is subject to data protection regulations such as GDPR, which require them to keep certain information in order to verify compliance and the history of data sources. In this scenario, data versioning can benefit both internal and external audits.

Many data preprocessing tasks become more efficient when data is maintained in the same way as code. Data versioning tools like lakeFS help you implement data versioning at every stage of the data’s lifecycle.

lakeFS includes hooks for zero-copy isolation, pre-commit, and pre-merge to build an automated process. All in all, it’s a massive helping hand in the process of data quality assessment using the practices above. Check out lakeFS on GitHub to learn more.

Hooks for zero-copy isolation, pre-commit, and pre-merge to build automated processes.

Conclusion

Data preprocessing is critical in the early phases of machine learning development. In the AI domain, data preprocessing enhances data quality by cleaning, transforming, and formatting it to increase the accuracy of a new model while minimizing the amount of computation necessary.

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