Data Preprocessing in Machine Learning | Techniques & Steps
Data in the real world is usually inconsistent, incomplete, and always has a number of missing values. Data is collected from multiple sources using data mining and warehousing techniques. It is a common rule in machine learning that the more data we have, the better models we can train. In the following, we want to talk about data preprocessing in machine learning and related issues.
What is Data Preprocessing?
Data processing refers to the manipulation and transformation of raw data into a meaningful and useful format. It involves various operations such as collecting, organizing, cleaning, analyzing, and storing data to gain valuable insights or achieve specific purposes. Data processing can involve both manual and automated procedures, depending on the complexity and volume of the data. The procedure often involves filtering out irrelevant data, applying algorithms or statistical techniques for analysis, and presenting the processed data in a structured manner for decision-making or further use in applications and systems. Data processing is a critical step in transforming raw data into actionable information.
Why is Data Preprocessing important?
From artificial intelligence development to data analysis, data scientists need data preprocessing to deliver accurate and robust results. Data is typically inconsistent because it is collected by humans, different applications, and business processes. It is not uncommon to find multiple inconsistencies in this raw data in the form of manual entry errors, missing values, duplicate data, multiple names for the same thing, and more. When humans work with this raw data, they can catch inconsistencies. However, machine learning models and deep learning algorithms mostly perform better when the data is pre-processed beforehand.
Data preprocessing techniques
Now that you are aware of the data preprocessing stage and its significance, let's discuss some of the primary data application approaches and attempt to apply them to more realistic projects in the future:
Data Cleanup
To increase the quality of your dataset, one of the most crucial components of the data preprocessing procedure is to locate and eliminate inaccurate and unreliable observations. This technique refers to the identification of incomplete, incorrect, duplicate, irrelevant, or null values in the data. Once these problems have been identified, they should be corrected or removed. The strategy chosen must be related to the problem area and the goal of the project.
Dimensionality Reduction
Dimensionality reduction is related to reducing the number of input features in the training data. There are usually a large number of features in a real-world data set, and if we do not reduce this number, it may affect the performance of the model later. Reducing the number of features by preserving as much diversity in the data set as possible will have a positive effect in several ways:
Use fewer computational resources
Increase overall model performance
Avoid overfitting
Avoid multilinearity and reduce noise
Feature Engineering
The feature engineering approach is used to create better features for the target data set, which increases the performance of the model. Domain knowledge is mainly used to create those features that are manually created from the existing features by making changes to them.
Sampling Data
The more data you have, the more accurate the model will be, but some machine learning algorithms may have problems managing a large amount of data and face issues such as memory saturation, increased computation to adjust model parameters, and other issues.
Read More: Data Preprocessing Techniques for Machine Learning Guide
Steps of Data Preprocessing in Machine Learning
In this section, we will discuss the Data Preprocessing in Machine Learning steps that must be taken to ensure that the data has been successfully pre-processed:
Assessing Data Quality
Take a good look at your data and get an idea of its overall quality, relevance to your project, and compatibility. There may be several data anomalies and inherent problems that should be considered in any collection:
Mixed data values
Outlier data
Missing data
Data Cleansing
Data cleaning is the process of adding missing data and correcting, repairing, or removing incorrect and irrelevant data from a dataset. Data cleaning is the most important preprocessing step because it ensures that your data is ready for your lower-level needs.
Data cleaning corrects any inconsistent data you discovered during the data quality assessment.
Data transformation
Data cleansing has begun the process of modifying the data, but data transformation begins the process of converting the data into the appropriate format you need for analysis and downstream processes.
This is typically done in one or more of the following cases:
Collection: Converting text or category attributes into numeric representations required by algorithms. Methods include label encoding, one-hot encoding, hashing trick, etc.
Normalization: Rescaling real-valued features into standard numeric ranges through min-max scaling, z-scores or decimal scaling. Avoids skewed ranges.
Feature selection: Selecting only the most predictive subset of input features through statistical tests, correlations, recursive elimination or embedded methods.
Imputation: Assigning replacement values for missing data based on averages, regression, clustering or other statistics.
Discrediting: Reducing continuous data into a smaller number of categorical intervals or bins to remove noise.
Concept Hierarchy Generation
Data Reduction
The more data you work with, the harder it is to analyze, even after cleaning and transforming it. In some situations, you may have more data than you need, especially when working with text analytics; much of regular human speech is redundant or irrelevant to the researcher's needs. Data reduction not only makes analysis easier and more accurate, it also reduces data storage.
Fixing Flaws Through Data Cleaning
Real-world datasets usually contain imperfections that can skew the ability of models to discern underlying patterns. Data cleaning handles these issues:
- Missing Values: Occur when data points are omitted due to human error or sensor issues. Simple strategies like deletion or basic imputation can address depending on use case.
- Anomalies and Outliers: Abnormal data points arising from errors that markedly differ from the overall distribution. Models often improve after removal or imputation of anomalies.
- Irrelevant Features: Attributes unrelated to the target lose predictive power and add noise. Pruning unnecessary input features simplifies modeling.
- Imbalanced Classes: When datasets have far more samples for some classes versus others. Resampling helps balance class representation.
- Errors: Detect and fix data entry mistakes, formatting problems, incorrect labels or other glitches before training models.
Careful data cleaning avoids misleading models with dirty data not reflective of the domain relationships and patterns.
Advantages of Data Preprocessing
Data preparation is a crucial stage of the data analysis process. These are a few advantages of data preparation:
This process increases accuracy and reliability. By removing missing or inconsistent data values caused by human or computer error, data preprocessing can improve the accuracy and quality of a data set and make it more reliable.
Data preprocessing leads to its adjustment. There may be duplicate data during data collection, so discarding them during preprocessing can ensure that the data values are consistent for analysis and lead to accurate results.
Data preprocessing increases the readability of the data algorithm. Preprocessing increases data quality and makes it easier for machine learning algorithms to read, use, and interpret.
Crafting New Features for Data Preprocessing in Machine Learning Through Engineering
Feature engineering leverages domain expertise to create new attributes for data preprocessing in machine learning:
Deriving insightful new data like ratios, aggregates, trends, custom metrics etc. that reveal relationships.
Helping uncover nonlinearities, interactions, and patterns hard to discern from just raw data.
Iteratively adding and testing engineered features improves model performance.
Strategic feature engineering amplifies existing signals and trends within the data.
The data preprocessing in machine learning efforts outlined equip ML models to effectively extract meaningful, generalized, actionable patterns from quality training data. Setting up modeling for success relies on diligent, knowledgeable data preprocessing aligned to the analytical objectives.
Conclusion
To sum up, a crucial stage in the machine learning pipeline is preparing data before using it with an algorithm. This work helps to improve accuracy, reduce the time and resources required for model training, avoid overfitting, and improve the interpretability of the model.
Here we come to the end of this article. Note that the mentioned steps and techniques also have sub-topics, each of which can contain a lot of information. In this article, we tried to describe the steps and techniques that are widely used and popular. After the data preprocessing is finished, your data can be divided into training, testing and validation sets for the model fitting and model prediction stage. become