A machine learning model for predicting the emissions of a car given some of its features. This was an interesting project, as it showed how a robust dataset can carry a model even on weak algorithms.
Each member worked together to help complete this project working on individual parts. For all members, each provided around 3-4 papers for the related works. Individual contributions are listed below.
Worked on data preparation, Logistic Regression, and SVM Linear and RBF Comparisons. Methods for metrics and K-Folds validation. On the report provided proof reading, modifications, and reworked analysis of models.
Worked on SVM, Decision Tree, and Random Forest. On the report provided much of the initial writing for results, most of the introduction and parts of the conclusions.
Worked on proof reading of report and colab file. On the report provided some of the works cited.
Our dataset was found online from the following source: https://www.kaggle.com/datasets/debajyotipodder/co2-emission-by-vehicles This dataset pulls information from the Government of Canada's offical site. This presented some issues which are discussed in the paper.
The report can be found in this repository.