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Data Modeling and Feature Engineering
Data modelling is the cornerstone of successful data analysis and machine learning projects. It’s the crucial first step where you define the structure and organization of your data. Just imagine a construction project — before you start building, you need a blueprint to ensure everything fits together. Data modeling acts as the blueprint for your data, organizing it in a way that facilitates efficient exploration and model building. Here you can visit the detailed tutorial.
This process involves selecting a specific data model that best represents the relationships within your data and aligns with the intended use case. Here, we’ll explore some common data models, each with its own strengths and applications.
Data Modeling: Understanding the Landscape
Relational Model: This is the most widely used model, structured with tables containing rows (records) and columns (attributes). Each table represents an entity (e.g., customer) and its attributes (e.g., name, address). Relational databases like MySQL and PostgreSQL utilize this model for efficient data storage and retrieval.
