Iowa, Ames ML group project
We analyzed the Ames, Iowa housing data set available on Kaggle, to understand the nuances of house pricing with the intention to build machine learning models that predict the prices and future sales in comparable markets. We build on the knowledge and experience of successful developers and real estate agents and are able to provide a more holistic and accurate approach to evaluation, with the ultimate goal of helping our clients make better informed investments of their time and money.
Wildcards_Working_Doc.pdf for a presentation of our results
for a collection of data visualizations
for Nillia’s contributions
for Kisaki’s contributions
for Colin’s contributions
for Annette’s contributions
Submission3.csv, submission2 582D .csv, submission1.csv for predictions submitted to Kaggle to score, predicted in ames.MLRheavy.R
Model2light_df.csv for data set resulting from cleaning in model2_lightexport.ipynb and used to train ames.MLRlight.R
ames.MLRheavy.R for multiple linear regression model trained on data set cleaned in model2_heavyexport.ipynb
ames.treemodel.R and ames.tree2.R for decision trees and random forest based models
inr_housingeda.R for EDA in R
housing.EDA.ipynb for EDA in Python