<![CDATA[Predictive modeling has become increasingly important in various fields such as data science, artificial intelligence, finance, healthcare, and many others. In this context, supervised learning has emerged as one of the most commonly used approaches to building predictive models. By employing supervised learning algorithms, models can be used to classify data into appropriate categories or make numerical predictions. In the realm of predictive modeling, the Python programming language has become a primary choice for practitioners and researchers. This research aims to provide a comprehensive understanding of supervised learning techniques that can be applied using Python. Qualitative research methods with a case study approach are employed to gain in-depth insights into the specific context and challenges. The researcher conducts experiments using real-world datasets relevant to predictive modeling to test the effectiveness of implemented supervised learning techniques. The findings of this research are expected to offer practical guidance to researchers and practitioners interested in leveraging supervised learning with Python to build efficient and reliable predictive models.]]>
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