Data preprocessing is the procedure for preparing raw data for use in a machine learning model, and it’s the first and most crucial stage in building a machine learning model.

When working on a machine learning project, we don’t always come across new, well-prepared data. Additionally, data must be formatted and cleaned before any data-related activities are performed. This is why we use a data preparation process.

Significance Of Data Preprocessing In ML

Due to their various origins, the majority of real-world datasets are particularly prone to missing, inconsistent, and noisy data. Applying data mining algorithms to this noisy data would produce poor results since they would be unable to detect patterns. As a result, data processing is critical for improving overall data quality.

  • Due to duplicate or missing numbers, the overall statistics of data may be misrepresented.
  • Outliers and inconsistent data points might cause the model’s overall learning to be disrupted, resulting in incorrect predictions.

Quality data must be used to make quality judgments. Data preprocessing in ML is necessary to obtain this high-quality data; otherwise, it would be a case of garbage in, garbage out.

Machine Learning Features

Features are individual independent variables that are used as inputs in our machine learning model. They can be regarded as representations or qualities that help the models anticipate the classes/labels by describing the data.

In a structured dataset, such as one saved in CSV format, features refer to each column indicating a quantifiable piece of data that may be analyzed: Name, Age, Sex, Fare, and so on.

Preprocessing Data In Four Steps

Let’s take a closer look at the four key steps of data preprocessing in ML.

Cleaning Of Data

Data cleaning is a step in the data preprocessing process that involves filling in missing values, smoothing noisy data, resolving inconsistencies, and removing outliers.

Integration Of Data

Data integration is a data preparation phase that combines data from numerous sources into a single, more extensive data storage, such as a data warehouse.

Data integration is critical when trying to tackle a real-world problem like recognizing nodules from CT scan images. The only way to create a more extensive database is to combine photos from numerous medical nodes.

While using Data Integration as one of the Data Preprocessing processes, we may encounter the following issues:

  • Data might be in various formats and properties, which can make data integration problematic.
  • Duplicate attributes are being removed from all data sources.
  • Conflicts in data values are detected and resolved.

Transformation Of Data

After the data has been cleared, we must consolidate the quality data into alternative forms by modifying the data’s value, structure, or format utilizing the Data Transformation techniques listed below.

Reduction Of Data

The dataset in a data warehouse may be too large for data analysis and data mining techniques to handle. One option is to create a simplified representation of the dataset substantially less in size but delivers comparable analytical results.

Evaluation Of Data Quality

Data Quality Assessment refers to the statistical procedures that must be followed to ensure that the data is free of errors. Data must be of high quality because it will be used for operations, customer management, marketing analysis, and decision-making. Data Quality Assessment consists of the following components:

  1. Completeness with no missing attributes
  2. Accuracy and reliability of data
  3. All features must be consistent.
  4. Keep your data accurate.
  5. There are no redundancies in it

There are three key actions in the data quality assurance process.

  • Profiling of data– It entails examining the data in order to discover data quality issues. After the issues have been analyzed, the data must be summarised to ensure that there are no duplicates, blank values, or other errors.
  • Cleaning up the data– It entails resolving data difficulties.
  • Data collection and analysis– It entails keeping data clean and keeping a constant eye on whether the data is meeting business requirements.

Conclusion

We’ve now covered all the steps to preprocess your data for analysis. Learn how to select ML algorithms and find the best datasets for your project in our blog.

So, if you wish to include machine learning in business, contact the ONPASSIVE team.