update housing

main_text.py

@@ -1,8 +1,32 @@
-pip install -U sentence-transformers
-from sentence_transformers import SentenceTransformer
+import pandas as pd
+from sklearn.model_selection import train_test_split
 
-model = SentenceTransformer("osanseviero/full-sentence-distillroberta3")
-sentence = ['This framework generates embeddings for each input sentence']
+# Read the data
+X_full = pd.read_csv('../input/train.csv', index_col='Id')
+X_test_full = pd.read_csv('../input/test.csv', index_col='Id')
 
-embedding = model.encode(sentence)
-print(embedding)
+# Remove rows with missing target, separate target from predictors
+X_full.dropna(axis=0, subset=['SalePrice'], inplace=True)
+y = X_full.SalePrice
+X_full.drop(['SalePrice'], axis=1, inplace=True)
+
+# Break off validation set from training data
+X_train_full, X_valid_full, y_train, y_valid = train_test_split(X_full, y, 
+                                                                train_size=0.8, test_size=0.2,
+                                                                random_state=0)
+
+# "Cardinality" means the number of unique values in a column
+# Select categorical columns with relatively low cardinality (convenient but arbitrary)
+categorical_cols = [cname for cname in X_train_full.columns if
+                    X_train_full[cname].nunique() < 10 and 
+                    X_train_full[cname].dtype == "object"]
+
+# Select numerical columns
+numerical_cols = [cname for cname in X_train_full.columns if 
+                X_train_full[cname].dtype in ['int64', 'float64']]
+
+# Keep selected columns only
+my_cols = categorical_cols + numerical_cols
+X_train = X_train_full[my_cols].copy()
+X_valid = X_valid_full[my_cols].copy()
+X_test = X_test_full[my_cols].copy()