What Is Data Preparation for Machine Learning?
Data preparation for machine learning refers to converting raw data into something more meaningful and actionable.
Businesses use data for various purposes, including informed and timely decisions, executing successful marketing and sales strategies, and whatnot. With raw data, companies can’t implement strategies. Data can only come in handy if it’s appropriately cleansed, annotated, well-labeled, and prepared.
Data can be gathered internally or acquired from a third-party. No matter the source, the raw data should be clean, structured, augmented, and labeled – The process of doing that is actually data preparation.
For any data analytics or machine learning project, data preparation is the first step before it could be analyzed further. This step may also include many complex tasks such as data ingestion (or loading), fusion, cleaning, delivery, etc.
Why Is Data Preparation Valuable?
According to a study by Cognilytica, around 80% of data scientist’s time goes into data preparation.
The better the quality of the data that goes into building analytical models, the better, more accurate will be the results. However, ideally, data scientists should be spending more time in advanced analytics, interacting with the data, and deployment of analytical models. This can only happen if we use advanced data preparation methods.
To understand the importance of data preparation, consider the garbage in, garbage out the concept – where the quality of the output solely depends upon the quality of the input (data preparation).
Data Preparation and LogicPlum
LogicPlum can help citizen data scientists and analysts by allowing them to interactively sort out data. This includes cleaning, shaping, combining, and exploring raw data for any data analytics project. We also enable data science and BI teams to reuse, collaborate, and share data sources. All this with full security and enterprise governance.
Using LogicPlum is easy. Once a user has the dataset ready, it can be seamlessly imported into LogicPlum. To upload data, you can use Hadoop, popular SQL databases, or simply drag and drop a .csv. No matter your programming skills, LogicPlum has a suitable import mechanism for everyone.
Once the data is uploaded, our platform allows easy analysis and cleaning mechanisms – enabling data scientists to work more effectively and efficiently.