Template Credit: Adapted from template made available by Dr. Jason Brownlee of Machine Learning Mastery.
Data Set Description: https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Original)
Benchmark References: https://www.kaggle.com/uciml/breast-cancer-wisconsin-data
Modeling Approach: binary classification, converting categorical to numerical attributes
Working through machine learning problems from end-to-end requires a structured modeling approach. Working problems through a project template can encourage you to think about the problem more critically, to challenge your assumptions, and to get good at all parts of a modeling project.
We will compare several different algorithms and determine which one would yield the best results. The project aims to touch on the following areas:
- Document a classification predictive modeling problem end-to-end.
- Explore data transformation options for improving model performance
- Explore algorithm tuning techniques for improving model performance
For this “Take-2” version of the project, we added the ensemble models to the exploration.
- Explore using and tuning ensemble methods for improving model performance
The HTML formatted report can be found here on GitHub.