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app.py

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+import pickle
+import streamlit as st
+import numpy as np
+
+# ✅ Load the trained model in binary mode ('rb')
+model_path = r"C:/Users/DELL/Downloads/3746233-Week_1/elite27.pkl"
+
+try:
+    with open(model_path, "rb") as f:
+        model = pickle.load(f)
+    st.success("✅ Model loaded successfully!")
+except Exception as e:
+    st.error(f"❌ Error loading model: {e}")
+    st.stop()  # Stop execution if model can't be loaded
+
+# ✅ Input fields
+st.title("🏡 House Price Prediction App")
+square_feet = st.number_input("Enter Square Feet:", min_value=500, max_value=10000)
+bedrooms = st.number_input("Enter Number of Bedrooms:", min_value=1, max_value=10)
+bathrooms = st.number_input("Enter Number of Bathrooms:", min_value=1, max_value=10)
+neighborhood = st.selectbox("Select Neighborhood", [0, 1, 2], format_func=lambda x: ["Rural", "Semi Urban", "Urban"][x])
+year_built = st.number_input("Enter Year Built:", min_value=1900, max_value=2025)
+
+# ✅ Predict price
+if st.button("Predict Price 💰"):
+    try:
+        user_data = np.array([[square_feet, bedrooms, bathrooms, neighborhood, year_built]], dtype=float)
+        prediction = model.predict(user_data)
+        st.success(f"🏠 Estimated House Price: ${prediction[0]:,.2f}")
+    except Exception as e:
+        st.error(f"❌ Prediction error: {e}")

elite27.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:895e1c0f50832213cac2303d43b8da918cd412d82a52aa5b98b96c570531146f
+size 633