Data Preprocessing Steps: How to Prepare Data for Machine Learning and Analytics

Key takeaways

Data preprocessing is crucial for getting reliable results in machine learning and data analysis. Removing flaws ensures more accurate outcomes.

• Data plays a key role in machine learning, a quickly growing field projected to reach a $282.13 billion market valuation by 2030 [1].

• Data also plays a valuable role in several areas for businesses and organizations, including informing decision-making, mitigating risk, reducing costs, and enhancing operational efficiency.

• You can mitigate bias, leading to improved accuracy and superior performance, by accessing quality training data when building machine learning models.

Learn about data preprocessing and how following various key steps can help lead to better outcomes in your project. Afterward, get started building your skills with a Google Data Analyst Professional Certificate, where you have the opportunity to learn data cleaning, analysis, and visualization skills, and more, while also practicing programming using SQL and R. 

What is data preprocessing?

Data preprocessing describes the process of preparing raw data for further use, such as training machine learning models, data mining, and data analysis. Raw data refers to any type of data that has not undergone any form of data processing or manipulation. You can collect raw data from various sources, including surveys, web scraping, and databases. When working with raw data, you may encounter flaws such as duplicate data, errors, or irrelevant information. During data preprocessing, you can address these flaws and improve the overall quality of your data, resulting in more accurate analysis. 

What are the four major tasks in data preprocessing?

The four key tasks in data preprocessing are data cleaning, integration, transformation, and reduction. Each is necessary to have data that’s in a format ideal for use. Additionally, the four major tasks in data preprocessing also play a crucial role in the data preprocessing steps.

How data preprocessing improves machine learning and analytics

Data preprocessing improves machine learning and analytics processing by helping you generate more accurate results than you’d otherwise obtain by simply using raw data. For example, different machine learning algorithms require varying data formats to train the model correctly and more quickly. Through data preprocessing, you can get your data ready specifically for the task at hand. By implementing these improvements, you can gain more valuable business insights, enhance customer satisfaction, and improve diagnostic accuracy in medical settings. 

Data preprocessing in machine learning

If you want high-quality output, you need high-quality data to input into your machine learning models. That’s why data preprocessing techniques such as categorical encoding, data normalization, and dimensionality reduction may be necessary to achieve the desired data quality. 

With the growth of machine learning to reach an anticipated $282.13 billion valuation, knowledge of machine learning skills is in high demand [1]. Knowing how to process data beforehand allows you to remove errors and address missing values, enabling the model to identify patterns within the data more effectively. Learn more about these machine learning-specific preprocessing techniques:

• Categorical encoding: Often used in neural networks, categorical encoding allows you to convert categorical variables for implementation into algorithms.

• Data normalization: Neural networks and support vector machines benefit from data normalization, which improves data uniformity.

• Dimensionality reduction: By reducing some of the variables within your data, you can ensure the model focuses on the most important and relevant information.

Data preprocessing in data analytics

Data analytics processes, such as data mining and data warehousing, require high-quality data to ensure the generation of accurate and usable insights. Data mining helps uncover patterns within data sets, while a data warehouse is where you store all that data. Through data preprocessing, you can ensure that the data within your data warehouse is reliable and of high quality, ready for mining and analysis. 

What are the main steps in data preprocessing?

Effective data preprocessing involves assessing, cleaning, integrating, transforming, and reducing data. Adhering to these data preprocessing steps will help you better understand the data you are working with, while also making sure the data is correctly set up for the problem you’re trying to solve. Details about these fundamental steps in data preprocessing are as follows:

1. Data assessment

The first step in data preprocessing is data assessment. It involves closely examining your data to determine its quality, highlighting factors such as whether the data uses consistent formatting and whether it contains any clear outliers.

2. Data cleaning

With the information you gather during the data assessment phase, you can construct a plan for how to best clean your data. This may include practices like removing unnecessary data that isn’t relevant to your analysis or adding missing values.

3. Data integration

If the data you’re using for your analysis comes from different data sets, then you will use data integration to combine them into one data set. During the data integration step, assess whether the data combined from various sources shares the same structure. If not, you can address this through data transformation.

4. Data transformation

Now that you have set yourself up with clean, quality data, you can start working on optimizing the format of your data. Methods include converting unstructured data into structured data and aggregation. Aggregation entails condensing data into smaller, more manageable values, such as calculating the median age of customers in your database, rather than including the age of each customer in the final analysis.

5. Data reduction

Data reduction improves the efficiency of your analysis by organizing your data into a smaller, more manageable sample. It’s a key step to maintaining the integrity of your data set while also allowing the model to work faster. An example of a data reduction technique is sampling, where you use a subset of your data that accurately represents your overall data set for analysis, rather than analyzing each data point.

Data preprocessing steps in machine learning

Data preprocessing in machine learning can include another step called data validation. During data validation, you will divide your data into two sets, one for training the model and the other for measuring the accuracy of your results. You can use that information to adjust and optimize the hyperparameters of your model. 

Data preprocessing tools

Data preprocessing tools like scikit-learn, OpenRefine, MATLAB, and pandas can simplify many steps during data preprocessing. Consider ways these specific tools can assist your data preprocessing workflow:

• Scikit-learn: When working with Python, you can access scikit-learn, an open-source machine learning library that includes tools for data preprocessing tasks like feature selection and normalization.

• OpenRefine: An open-source tool for cleaning and transforming data, OpenRefine can help you with additional data preprocessing tasks, like data normalization.

• MATLAB: The programming language MATLAB features its Data Cleaner app, which can help you clean several variables at once, edit your previously performed actions, and identify messy data.

• Pandas: Another popular Python library, pandas assists with cleaning, transforming, and aggregating your larger data sets, as well as data analysis and visualization.

Read more: 15 Data Analysis Tools and When to Use Them

Explore our free resources for data analysts

Want to advance your skills in data analytics? Check out the following free resources, including our YouTube channel, where you can keep learning more about data: 

• Watch on YouTube: Which Data Analytics Course Should You Take? (Coursera Comparison)

• Hear from an expert: 6 Questions with a Microsoft Data Analytics Leader

• Study terminology: Data Analysis Terms & Definitions

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