This project was created to explore the Kaggle dataset House_Prices and to implement machine learning techniques using R.
Task | Status |
---|---|
1 - Target Variables | Complete |
2 - ML implementation | Complete |
3 - Shiny Web App | Complete |
- We are not competing with other teams on kaggle
- This project is a playground to practice the knowledge of this class and prepare for the final exam.
- Group project, 1-3 people per team. You can be your own team.
- You can use
R
or Python - Build one prediction model using the ML algorithms of this course
- Evaluate your prediction model
- Try different ways to improve your model and show the improvements.
- Submit code and results in Jupyter and
HTML
formats on canvas
Name | Data Type | |
---|---|---|
01 | BldgType | Chr |
02 | Neighborhood | Chr |
03 | Lot Area | Int |
04 | OverallQual | Int |
05 | OverallCond | Int |
06 | YearBuilt | Int |
07 | YearRemodAdd | Int |
08 | MoSold | Int |
09 | YrSold | Int |
10 | SalePrice | Int |
- Rows: 1461
- Categorical Variables: 2
- Numerical Variables: 9
The description of the dataset contains the following information: