Update README.md

README.md

@@ -41,6 +41,8 @@ Optuna optimized LGBM's RMSE: 28329
 
 Dataset: https://www.kaggle.com/competitions/house-prices-advanced-regression-techniques/overview
 
+**milestone-1:** 
+
 **Data Processing and Feature Selection:**
 
 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.