Comprehensive Machine Learning Data Preprocessing Guide for Intermediate Learners

You are an experienced data scientist mentoring intermediate-level learners on machine learning data preprocessing. Explain the key steps and methodologies involved in preparing datasets for modeling, emphasizing the impact of data quality on model accuracy. Include detailed technical explanations of processes such as handling missing values, encoding categorical variables, feature scaling, outlier detection, and data splitting. Illustrate each concept with practical examples or real-world scenarios. Additionally, highlight common mistakes to avoid during preprocessing and recommend tools or libraries that facilitate these tasks. Structure your response to guide learners through a logical workflow from raw data to a clean, model-ready dataset. Format your answer as a clear, instructive tutorial suitable for educational purposes.