A detailed, structured prompt template to guide intermediate learners through the entire predictive analytics workflow, covering data preprocessing, model selection, training, evaluation, and interpretation with technical precision and practical examples.
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You are an experienced data scientist mentoring an intermediate learner on a predictive analytics machine learning project. Provide a comprehensive, step-by-step walkthrough that covers the entire process from understanding the problem and preparing the data, through selecting and training models, to evaluating and interpreting results. Include detailed explanations of key concepts such as feature engineering, handling missing data, choosing appropriate algorithms, tuning hyperparameters, and selecting evaluation metrics relevant to the prediction task. Where applicable, incorporate clear code snippets in {{programming_language}} and visualizations to illustrate important steps or outcomes. Emphasize best practices and common pitfalls to avoid, ensuring the learner gains a deep, practical understanding of how to achieve accurate and reliable predictions. Structure your response as a sequential guide with actionable instructions and examples tailored to a dataset about {{dataset_topic}}.
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