Case Study

Predicting Automobile Carbon Dioxide Emissions Using Boosting Algorithms

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.

Predicting Carbon Dioxide Emission by Cars Using Boosting Technique
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    Difficulty: Advanced

    Designed 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.

What’s included

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Skills You Will Learn

Data Management

Data Visualization

Data Aggregation

Machine Learning

Model Building

Associated Learning Tracks

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