Mobile Phone Classification

Tree Ensembles

In this project I focused on exploring the different variations of tree models and techniques such as bagging, boosting, random forests, and a simple decision tree. For this particular dataset I compared models and found that the bagging technique performed the best.

Model Selection

This project highlights the effects of using different machine learning techniques on the same dataset. The results in this analysis are specific to the data we have access to and even though I was able to compare and contrast models, no general statements could be made about which one performs better; each dataset is different!

Real-World Application

In this project I presented a hypothetical scenario using the data provided to make predictions about the price category of a particular mobile phone. The intention of this analysis was to give business owners a tool to help them determine competitive pricing based on the features that their phone models advertised.