Riassunto analitico
Machine learning has emerged as a powerful tool in solving complex real-world problems, and classification analysis remains a fundamental task within this domain.
This master's thesis offers a comprehensive exploration of the development and application of machine learning predictive models specifically designed for binary classification problems. Executed using the Python programming language, the research endeavors to bridge the theoretical understanding of classification algorithms with their practical implementation, providing an extensive guide tailored for data scientists and engineers venturing into the creation of effective classification models.
The foundational section of the thesis provides a thorough overview of various classification algorithms, delineating their primary features. Subsequently, the study introduces crucial concepts, particularly delving into the intricacies of the data processing phase. The culmination of this exploration involves the presentation and in-depth discussion of results derived from the application of these models to a real-world case study.
Central to the thesis is the detailed examination of the design, implementation, and evaluation of binary classification models. The roster includes a diverse array of algorithms such as logistic regression, decision trees, random forests, neural networks, and support vector machines. This assortment undergoes rigorous comparison and evaluation through extensive experimentation. Beyond the algorithms, the study delves into advanced techniques like feature selection and hyperparameter tuning to optimize the overall performance of the models.
The practical application of this acquired knowledge unfolds in a real-world case study: predicting turbo-compressor degradation in an F1 car. This application serves as a compelling demonstration of the versatility of the developed predictive model. Its potential to yield valuable insights for decision-makers is underscored through the successful interpretation of the intricate interplay between the models and the unique challenges posed by the specific case study.
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