Any AI-powered tool follows the following set of principles to build a predictive model: Define the problem and desired outcome. Collect relevant data. Preprocess the data by cleaning, handling missing values, and scaling. Select or engineer important features. Choose an appropriate model based on the problem and data. Train the model on a training set. Evaluate the model’s performance using appropriate metrics. Refine the model by adjusting parameters or trying different algorithms. Deploy the model to make predictions on new data. Monitor and maintain the model’s performance, updating it as needed.