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This chapter focuses on data exploration and preprocessing—key steps for ensuring data quality and accuracy. These tasks are iterative and often require repetition, utilizing techniques such as summary statistics, data visualization, and data profiling. The discussion extends to data modeling, including the creation and application of models to enhance understanding of observed data. Additionally, the chapter provides an overview of the differences between artificial intelligence, machine learning, and deep learning, highlighting widely used and effective algorithms. The second part includes practical exercises with a dataset on “Annual Working Hours vs. GDP per Capita,” covering tasks such as handling missing values, identifying outliers, addressing data quality issues, filtering data, standardizing it, and creating plots—all within the RStudio environment.

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Data Exploration, Preprocessing, and Modeling

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  • 发表日期：

    2025年

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  • Fatwa Ramdani

摘要

This chapter focuses on data exploration and preprocessing—key steps for ensuring data quality and accuracy. These tasks are iterative and often require repetition, utilizing techniques such as summary statistics, data visualization, and data profiling. The discussion extends to data modeling, including the creation and application of models to enhance understanding of observed data. Additionally, the chapter provides an overview of the differences between artificial intelligence, machine learning, and deep learning, highlighting widely used and effective algorithms. The second part includes practical exercises with a dataset on “Annual Working Hours vs. GDP per Capita,” covering tasks such as handling missing values, identifying outliers, addressing data quality issues, filtering data, standardizing it, and creating plots—all within the RStudio environment.