Update app.py

app.py

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+import streamlit as st
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LinearRegression
+
+# Load the dataset
+@st.cache
+def load_data():
+    url = "https://raw.githubusercontent.com/selva86/datasets/master/BostonHousing.csv"
+    data = pd.read_csv(url)
+    return data
+
+# App title
+st.title("House Price Prediction")
+
+# Load data
+data = load_data()
+st.write("Dataset", data)
+
+# Feature selection
+st.sidebar.header("Configure Input Features")
+selected_features = st.sidebar.multiselect("Select features", data.columns[:-1])
+
+if selected_features:
+    X = data[selected_features]
+    y = data["medv"]  # Median value of homes
+
+    # Split data
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+    # Train model
+    model = LinearRegression()
+    model.fit(X_train, y_train)
+
+    # Prediction
+    y_pred = model.predict(X_test)
+
+    # Display results
+    st.write("Selected Features", selected_features)
+    st.write("Model Coefficients", model.coef_)
+    st.write("Predictions", y_pred)
+    st.write("Actual Values", y_test.values)
+
+    # Model performance
+    from sklearn.metrics import mean_squared_error, r2_score
+    st.write("Mean Squared Error", mean_squared_error(y_test, y_pred))
+    st.write("R-squared Score", r2_score(y_test, y_pred))