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The case study introduces to the main types of Boosting Algorithms: as well as demonstrate how these models can be used to predict the carbon dioxide emission of various cars and implement a proper maintenance plan.
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Difficulty: AdvancedDesigned for those with a technical background or industry experience
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Duration: Approximately 3 hours
Case Overview
- The case familiarizes the learner to apply Boosting techniques to improve the predictive performance of the Machine learning model which can be used to minimize the emission of harmful gases.
- Understand key insights about the data by implementing univariate and bivariate visualization methods.
- The case helps to gain a comprehensive understanding of Ensemble methods that combine several base models to produce to build robust machine learning models.
- The case provides working understanding of boosting techniques such as Adaboost, Gradient boost, XG boost to predict the C02 emission.
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