Update app.py

app.py

@@ -7,32 +7,32 @@ from sklearn.preprocessing import StandardScaler
 from sklearn.linear_model import LinearRegression
 from sklearn.metrics import mean_squared_error, r2_score
 
-# # Load the California Housing dataset
-# data = fetch_california_housing(as_frame=True)
-# X = data.data
-# y = data.target
+# Load the California Housing dataset
+data = fetch_california_housing(as_frame=True)
+X = data.data
+y = data.target
 
-# # Split the dataset into training and test sets
-# X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+# Split the dataset into training and test sets
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
 
-# # Standardize features
-# scaler = StandardScaler()
-# X_train = scaler.fit_transform(X_train)
-# X_test = scaler.transform(X_test)
+# Standardize features
+scaler = StandardScaler()
+X_train = scaler.fit_transform(X_train)
+X_test = scaler.transform(X_test)
 
-# # Train the model
-# model = LinearRegression()
-# model.fit(X_train, y_train)
+# Train the model
+model = LinearRegression()
+model.fit(X_train, y_train)
 
-# # Make predictions on the test set
-# y_pred = model.predict(X_test)
+# Make predictions on the test set
+y_pred = model.predict(X_test)
 
-# # Evaluate the model
-# mse = mean_squared_error(y_test, y_pred)
-# r2 = r2_score(y_test, y_pred)
+# Evaluate the model
+mse = mean_squared_error(y_test, y_pred)
+r2 = r2_score(y_test, y_pred)
 
-# print(f"Mean Squared Error: {mse:.2f}")
-# print(f"R-squared Score: {r2:.2f}")
+print(f"Mean Squared Error: {mse:.2f}")
+print(f"R-squared Score: {r2:.2f}")
 
 sentiment_pipeline = pipeline("sentiment-analysis")