Update app.py

app.py

@@ -7,6 +7,8 @@ from sklearn.preprocessing import StandardScaler
 from sklearn.linear_model import LinearRegression
 from sklearn.metrics import mean_squared_error, r2_score
 
+st.write("begin of house prediction")
+st.write("load dataset")
 # Load the California Housing dataset
 data = fetch_california_housing(as_frame=True)
 X = data.data
@@ -15,24 +17,33 @@ 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)
 
+st.write("standardize")
 # Standardize features
 scaler = StandardScaler()
 X_train = scaler.fit_transform(X_train)
 X_test = scaler.transform(X_test)
 
+st.write("train")
 # Train the model
 model = LinearRegression()
 model.fit(X_train, y_train)
 
+st.write("make predictions")
 # Make predictions on the test set
 y_pred = model.predict(X_test)
 
+st.write("evaluate")
 # 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}")
+st.write(f"Mean Squared Error: {mse:.2f}")
+st.write(f"R-squared Score: {r2:.2f}")
+         
+# print(f"Mean Squared Error: {mse:.2f}")
+# print(f"R-squared Score: {r2:.2f}")
+
+st.write("end of house prediction")
 
 sentiment_pipeline = pipeline("sentiment-analysis")