Significance of Data Transformation in Machine Learning

by November 12, 2019 0 comments

Machine Learning advancements allow enterprises to process and comprehend their data much faster using modern tools with validated algorithms. But that data can be intricate and chaotic in its raw state. Thus, some form of data transformation here is required before any data analysis that can assist in accomplishing business use cases such as market campaigns, implement efficient logistics operations, outpace competitors, among others.

Data transformation makes businesses’ data more constructive. However, it can be time-consuming, expensive, and monotonous if the right technology stack not in place. Transforming business data can ensure maximum data quality which is vital to gain precise analysis, leading to valuable insights that will ultimately reinforce data-driven decisions.

Machine learning models are only as good as the data leveraged to train them. Though building and training models to process data is an intense concept, and many enterprises have deployed or planning to deploy the technology to handle more practical applications. For models to learn from data to make valuable predictions, it is also significant that the data itself must be organized to make sure its analysis yields valuable insights.

 

Processing Data for Machine Learning

Machine learning alongside AI is utilized for prevalent applications, such as detecting financial fraud and identifying opportunities for investments and trade. It is also used for autonomous vehicles, speech recognition, robotics, and improving customer service.

Processing and understanding data insights, models need to consume clean datasets all while keeping up with new incoming data. If the quality of datasets is not checked properly, enterprises cannot get an accurate outcome from the machine learning jobs, making business decisions more complex. Thus, it will create challenges for organizations in transforming their data as it increases in volume, variety, and pace.

Fortunately, the cloud infrastructure here can change the way businesses manage and store their data. To overcome and avail the potential of big data, businesses need to leverage the power of the cloud and consider deploying data transformation purpose-built for the cloud.

 

Data Processing Operations

Data transformation in machine learning models needs certain data processing operations.

1. Data Cleansing – Removing superfluous and repeated data records from raw data will enhance the speed at which a business’s ML model trains, as well as improve their analysis.

2. Alter Data Types – Leveraging the correct data types assists in saving memory usage and can be a requirement for predictions to be performed against it.

3. Convert Categorical Data to Numerical – Most ML models require categorical data to be in a numerical format. But some models work either with numeric or categorical features, while others can handle mixed-type features.

In a nutshell, with the progressions in technology and the power of cloud computing, each business can capitalize on machine learning in a cost-effective and agile manner with data transformation. However, transforming data for analysis can be challenging but it can be worth if businesses continue to leverage data and insights to innovate and grow.

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