Machine learning algorithms are widely used for various tasks such as classification and regression. For training a machine learning model, you need to feed it with accurate data to improve the model's performance.

What is data preparation?

Data preparation involves cleaning and transforming raw data before it is processed and analyzed. 

Before processing, data are reformatted, errors are fixed, and datasets are combined to enrich the data. 

Data preparation is one of the most time-consuming tasks for individuals or organizations that use data. 

Turning data into insights is vital and eliminating bias caused by poor data quality. 

Standardizing data formats, enriching source data, and removing outliers are examples of data preparation.

How to prepare data?

To prepare data, the following steps are taken:

• Access the data: Every organization has a variety of business data sources. Examples include endpoint data, customer data, and marketing data.
• Ingest (or fetch) the data: Once the data has been identified, it needs to be brought into the analysis tools. The data will likely combine structured and semi-structured data in different repositories.
• Cleanse the data: Data cleansing ensures that the data set can provide valid answers when analyzed. Small datasets can be processed manually, but most realistically sized datasets require automation.
• Format the data: After the dataset has been cleansed, it needs to be formatted. This step involves resolving issues like multiple date formats or inconsistent abbreviations.
• Combine the data: After cleansing and formatting the data set, merging, splitting, or joining the input sets is possible. The data can be moved to the data warehouse staging area once the combining step has been completed.
• Analyze the data: Once the analysis has begun, changes to the data set should only be made after careful consideration. A variety of algorithms are adjusted during analysis and compared with other results.

How do you prepare data for machine learning?

Results will be better if you are disciplined with data. Data preparation for machine learning algorithms can be summarized as follows:

Step 1 - Select Data: In this step, select the subset of all available data you will work with. There is always a strong desire to include all available data, so the maxim "more is better" will hold. This may or may not be true.

Step 2 - Preprocess Data: Once you have selected the data, you need to consider how you will use it. A preprocessing step involves transforming the selected data into a form that can be processed.

Step 3 - Transform Data: The process data must be transformed. Your knowledge of the problem domain and the algorithm you're using will impact this step. You will probably have to revisit different transformations of your preprocessed data throughout your problem-solving process.

Conclusion

Today, in modern societies, the most attention is paid to information activities such as processing, production, recording, transmission, dissemination, and management of information.

Information processes are one of the most significant expenses, and with the expansion of database systems and the large amount of data stored in these systems, tools are essential.

These data can be processed, making the resulting information available to users. The data mining process has the task but cannot achieve real and effective results without accurate and reliable inputs.

Make sure the data is accurate and suitable before conducting any analysis.