The first step is to understand the project's needs. Clearly defining the goal and the questions to be answered will help us define the key metrics that will be used to evaluate the project.
Data collection is a key element in any Data Science project. This includes identifying relevant data sources, and collecting and modifying them for subsequent use.
Data is often raw and disorganized, and therefore preprocessing is a vital step in achieving accurate results. This includes cleaning, treating, and even normalizing them.
At this step, the model that best fits the project's needs and available data must be selected. This also includes the selection of algorithms, model validation, and parameter tuning.
At this point, the selected model must be trained and its performance evaluated using the metrics defined in the first step. Cross-validation and data splitting are some of the techniques used.
Finally, the model is deployed in a real-time production environment. This includes integrating the model with client applications and establishing automated workflows for data collection, preprocessing, and updating.
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