Model Building

Model building is the process of creating a mathematical representation of a real-world process or system. In data science, model building involves selecting and training a machine learning algorithm on a dataset to create a predictive model. This process includes data cleaning, feature engineering, selecting appropriate algorithms, and tuning hyperparameters to optimize model performance. The goal of model building is to create a model that accurately predicts outcomes on new, unseen data. Model building is an iterative process that involves testing and refining the model until it meets the desired level of accuracy and performance.
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