Predictive analytics is the use of data, algorithms, and models to make predictions about future outcomes. It helps organizations make better decisions by forecasting and predicting customer behavior, market trends, and more. Predictive analytics can be used for a variety of applications including risk management, insurance claims analysis, fraud detection, marketing segmentation, forecasting demand, customer lifetime value estimation, and beyond. As a Data Scientist looking to gain an edge over the competition and stay ahead of the curve, here are five predictive analytics techniques every Data Scientist should know.
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