Data Preparation

Data preparation is a crucial phase in the machine learning workflow, involving several key steps to transform raw data into a clean, organized format suitable for model training. This process includes collecting data from various sources, labeling data, adding data to a project, and distributing the data into different sets for training, validation, and testing. Each step is vital for building robust and accurate machine learning models, as the quality and structure of the data directly impact the model’s performance and reliability.

Data Preparation for building a machine learning model can be broken down into the following stages:

• Data Collection: Data Collection is the initial phase where data is gathered from various sources, such as databases, sensors, or manual collection. The objective is to assemble a comprehensive dataset that accurately represents the problem you aim to solve.

• Adding data into the project: After collecting the data, the next step is to import the data into the project. This allows you to start working with the data within the machine learning tool.

• Data Labeling: Data labeling is an integral step in developing high performing machine learning models. It is the process of converting unstructured data to structured data by adding informative labels, that helps the machine learning model to make accurate predictions.

• Distributing Data: Once the data is collected and labeled, it needs to be distributed into different sets, such as training, validation, and test sets. This distribution ensures that the model is trained, validated, and tested on different subsets of the data, enhancing its generalizability and performance.