Update app.py

app.py

@@ -1,4 +1,50 @@
 import streamlit as st
+import pandas as pd
+import numpy as np
+import xgboost as xgb
+from sklearn.metrics import mean_squared_error
+from sklearn.model_selection import train_test_split
+import optuna
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
+# 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, 
+                 learning_rate = 0.01,
+                 max_depth = 5, 
+                 alpha = 10, 
+                 n_estimators = 500)
+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, 50000)
+overall_qual = sidebar.slider("Overall Quality", 1, 10, 5)
+overall_cond = sidebar.slider("Overall Condition", 1, 10, 5)
+year_built = sidebar.slider("Year Built", 1872, 2010, 1950)
+tot_rooms_above_grade = sidebar.slider("Total Rooms Above Grade", 2, 14, 7)
+garage_area = sidebar.slider("Garage Area", 0, 1418, 500)
+# Create a Pandas DataFrame with the user's input
+input_df = pd.DataFrame({
+    "LotArea": [lot_area],
+    "OverallQual": [overall_qual],
+    "OverallCond": [overall_cond],
+    "YearBuilt": [year_built],
+    "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}")