ml-housing

Machine learning project that predicts house values in Ames, Iowa and calculates the most profitable ways to flip homes. Uses dataset from the following Kaggle competition: https://www.kaggle.com/c/house-prices-advanced-regression-techniques/overview

Name		Name	Last commit message	Last commit date
Latest commit History 83 Commits
.ipynb_checkpoints		.ipynb_checkpoints
bsmt_and_misc		bsmt_and_misc
garage_house_qualities		garage_house_qualities
house-prices-advanced-regression-techniques		house-prices-advanced-regression-techniques
land_ext		land_ext
utitilies_house_struc		utitilies_house_struc
.gitignore		.gitignore
EDAandManipulateTrainData.ipynb		EDAandManipulateTrainData.ipynb
Gradient_Boosting.ipynb		Gradient_Boosting.ipynb
Multiple_LR_Analysis.ipynb		Multiple_LR_Analysis.ipynb
Predictions_Gradient_Boosting.csv		Predictions_Gradient_Boosting.csv
README.md		README.md
Selected_Columns_EDA_Code.ipynb		Selected_Columns_EDA_Code.ipynb
after_lasso.csv		after_lasso.csv
after_lasso_test.csv		after_lasso_test.csv
compiled_features.csv		compiled_features.csv
compiled_features2.csv		compiled_features2.csv
compiled_features_edit1.ipynb		compiled_features_edit1.ipynb
compiled_with_month.csv		compiled_with_month.csv
compiled_with_season.csv		compiled_with_season.csv
compiling_features.ipynb		compiling_features.ipynb
data_selected_columns.csv		data_selected_columns.csv
decision_tree_manual_feature_selection.R		decision_tree_manual_feature_selection.R
decision_tree_unaltered.R		decision_tree_unaltered.R
encoded_selected_columns.csv		encoded_selected_columns.csv
encoding_categories_code.ipynb		encoding_categories_code.ipynb
gridsearch_lasso.ipynb		gridsearch_lasso.ipynb
gridsearch_lassov2.ipynb		gridsearch_lassov2.ipynb
test_data_formatting.ipynb		test_data_formatting.ipynb

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