Update README.md

README.md

@@ -4,8 +4,12 @@ Milestone-3 notebook: https://colab.research.google.com/drive/17-7A0RkGcwqcJw0Ic
 
 Hugging Face App: 
 
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 Results:
 
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 XGBoost Model's RMSE: 28986  (Milestone-2)
 
 Baseline LGBM's RMSE: 26233
@@ -44,6 +48,37 @@ min_child_samples :	 1
 
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+Documentation
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+Dataset: https://www.kaggle.com/competitions/house-prices-advanced-regression-techniques/overview
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+Data Processing and Feature Selection:
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+For the feature selection, I started by dropping columns with a low correlation (< 0.4) with SalePrice. I then dropped columns with low variances (< 1). After that, I checked the correlation matrix between columns to drop selected columns that have a correlation greater than 0.5 but with consideration for domain knowledge. After that, I checked for NAs in the numerical columns. Then, based on the result, I used domain knowledge to fill the NAs with appropriate values. In this case, I used 0 to fill the NAs as it was the most relevant value. As for the categorical NAs, they were replaced with ‘None’. Once, all the NAs were taken care of, I used LabelEncoder to encode the categorical values. I, then, checked for a correlation between columns and dropped them based on domain knowledge.
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+Here are the 10 features I selected:
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+ 'OverallQual',
+ 'YearBuilt',
+ 'TotalBsmtSF',
+ 'GrLivArea',
+ 'MasVnrArea',
+ 'BsmtFinType1',
+ 'Neighborhood',
+ 'GarageType',
+ 'SaleCondition',
+ 'BsmtExposure'
+
+All the attributes are encoded and normalized before splitting into train and test with 80% train and 20% test.
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+**Milestone 2: 
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+For milestone 2, I ran an XGBoost Model with objective="reg:squarederror" and max_depth=3. The RMSE score is 28986.
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+**Milestone 3:
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 Reference:
 
 https://github.com/adhok/streamlit_ames_housing_price_prediction_app/tree/main