Update app.py

app.py

@@ -5,19 +5,11 @@ import xgboost as xgb
 from sklearn.metrics import mean_squared_error
 from sklearn.model_selection import train_test_split
 import optuna
-
-# Load the data
 path = "train.csv"
 data = pd.read_csv(path)
-
-# Get features
 y = data['SalePrice']
 X = data[["LotArea","OverallQual", "OverallCond", "YearBuilt","TotRmsAbvGrd","GarageArea"]]
-
-# Split the data
 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
-
-# Load the XGBoost model
 model = xgb.XGBRegressor(objective ='reg:squarederror', 
                  colsample_bytree = 1, 
                  eta=0.3, 
@@ -26,7 +18,6 @@ model = xgb.XGBRegressor(objective ='reg:squarederror',
                  alpha = 10, 
                  n_estimators = 700)
 model.fit(X_train, y_train)
-# Create a sidebar with sliders for each feature
 sidebar = st.sidebar
 sidebar.title("Input Features")
 lot_area = sidebar.slider("Lot Area", 1300, 215245, 1300)
@@ -35,7 +26,6 @@ overall_cond = sidebar.slider("Overall Condition", 1, 10, 6)
 year_built = sidebar.slider("Year Built", 1872, 2010, 1980)
 tot_rooms_above_grade = sidebar.slider("Total Rooms Above Grade", 2, 14, 5)
 garage_area = sidebar.slider("Garage Area", 0, 1418, 462)
-# Create a Pandas DataFrame with the user's input
 input_df = pd.DataFrame({
     "LotArea": [lot_area],
     "OverallQual": [overall_qual],
@@ -44,7 +34,5 @@ input_df = pd.DataFrame({
     "TotRmsAbvGrd": [tot_rooms_above_grade],
     "GarageArea": [garage_area]
 })
-# Use the XGBoost model to predict the house price range for the user's input
 prediction = model.predict(input_df)
-# Display the predicted house price range to the user
-st.write(f"The estimated house price range is ${prediction[0]:,.2f}")
+st.write(f"The estimated house price range is ${prediction[0]:,.2f}")