jiaxin-yyjx / house_price_prediction-statistical_learning

Machine Learning Project

Statistical Learning Project

Introduction

The project will involve investigating the following dataset: The Ames Iowa housing dataset.

The main question of interest in this dataset is How do home features add up to its price tag? This analysis involves forming a predictive model for the response variable, SalePrice, as a function of the 79 predictor variables. The 79 predictor variables are described in the file data description.txt.

Methods

R Markdown

• explore data analysis
• collinear problems
• feature selection
• dimension reduction
• regression models (Ridge/Lasso/PCR/PLS)

Files

• data_description.txt: source variables description
• train.csv: the dataset of all training data for SalePrice
• test_new.csv: the dataset of all testing data for SalePrice
• House Price Prediction.Rmd: the code in R MarkDown
• House Price Prediction.pdf: the final report
• Final Presentation.pptx: the presentation slides
• README.md