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Models are created using data to make predictions from "summary" of Data Science for Business by Foster Provost,Tom Fawcett
Models in data science are built by analyzing data in order to make predictions. The process of creating models begins with collecting relevant data and then using that data to train the model. This training process involves feeding the model with examples of input data and the corresponding correct output. Through this iterative process, the model learns patterns and relationships within the data that can be used to make predictions on new, unseen data. Data is a fundamental component in the creation of models because it serves as the building blocks for training and evaluating the model. The quality, quantity, and relevance of the data used to train the model directly impact the accuracy and effectiveness of the predictions it can make. Therefore, it is crucial for data scientists to carefully select and preprocess the data before feeding it into the model. Once the model is trained, it can be used to make predictions on new data points by applying the patterns and relationships it has learned from the training data. The goal of these predictions is to provide insights, identify trends, or solve problems based on the patterns discovered in the data. It is important to note that models are not static entities but rather dynamic tools that need to be continuously monitored and updated as new data becomes available. This process, known as model maintenance, ensures that the model remains accurate and reliable over time. By continuously refining and improving the model based on new data, data scientists can ensure that it continues to make accurate predictions and provide valuable insights.- The creation of models using data to make predictions is a fundamental concept in data science. By leveraging data to train, evaluate, and update models, data scientists can harness the power of data to drive decision-making, solve complex problems, and uncover valuable insights in various domains.
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