Description
Description:
The project is split into three phases that match the learning outcomes throughout the
course. Each phase accounts for 10% of your total grade.
Guidelines:
The aim of this project is to demonstrate your ability to apply and discuss the outcomes
of various data mining techniques on a problem and a dataset of your interest.
The dataset must include quantitative and qualitative attributes.
Your work should not be limited to what you learn in the practical sessions of the course.
You must submit an R markdown, knitted as a pdf file, for every phase.
You can work in a group of two – same group in all phases.
Your grade will be subject to a 5% penalty for every day of submission delay.
– Phase III: (10%) due Wednesday, Dec. 7, 11:59pm.
Use the dataset that you picked in Phase 2 or choose a new dataset – discuss your choice
with me in that case. (1%)
N.B. Your dataset should not be associated with any existing work related to the
required tasks – e.g., on kaggle, Github, …
Apply tree-based approaches including decision trees, random forest, bagging, and boosting.
(4%)
Apply unsupervised techniques including k-means and hierarchical clustering, as well as
principal component analysis. Analyze and comment on your results. (6%)
For each phase, make sure to highlight the following in your R markdown pdf file:
Dataset description including context and features
Data mining tasks
Model performance
Results
Comparison of results
Comments and interpretation
Name of your R markdown pdf file following this template: NameOfTeamMember1-
NameOfTeamMember2_Phase PhaseNumber.
