Overview

Teaching: 10 min
Exercises: 180 min

Questions

• What do you learn from Machine Learning workshop using R caret?

Objectives

• Learn how to apply Machine Learning into your real project

15. MINI PROJECT

Description of Mini Project:

• This Mini Project evaluates the ability of you working on a sample Data Science project from scratch.
• The project is about Supervised Machine Learning only
• It requires you to download data, clean data, split into training/testing and apply any machine learning model and analyse the output.

Requirement:

• You can write the script in RStudio (on Palmetto’s Open OnDemand or your PCs/Macs) and save it as your_username.R and send it to my email: tuev@clemson.edu. You can also upload to your github page and share to me
• Note: in order to get the Certificate of Attendence, you should send me the R script on or before 09/30/2021.

Data

• You can use any data in your field of expertise (most preferred method). If so, please send along the dataset with the Rscript
• You can also use Kaggle data which can be found here (second preferred method). Please elaborate in the ipynb how did you retrieve the data
• You can use use Titanic data from our repo. Description for Titanic data can be found here (third preferred method)

Method

In the R solution file I would like to see the following:

• Clearly state the objective of the mini-project on Supervised Machine Learning
• Brief explanation about the data that you will be using: source, predictors, predictand
• Type of ML model output: Continuous or Classification?
• Read in the data
• Clean & Standardized the input data if needed
• Split data to training/testing (You can also use Cross Validation if needed, not required)
• You can use any Regularization (variable selection) or PCA if needed (not required)
• Construct Machine Learning model to training set and explain why do you want to use that algorithm (any model is fine for me)
• Apply Machine Learning model to predict the output from testing set
• Evaluate the output using any of the given method in chapter 4
• Confirm if your ML model is good or bad?

Key Points

• Self-project