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app.py

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+import streamlit as st
+import pickle
+import numpy as np
+
+# Load Model
+with open("house_price_model.pkl", "rb") as f:
+    model = pickle.load(f)
+
+# Streamlit App
+st.title("🏠 House Price Prediction")
+st.write("Enter house details to predict price")
+
+# Input Fields
+CRIM = st.number_input("Crime Rate", 0.0, 100.0, step=0.1)
+ZN = st.number_input("Proportion of Residential Land", 0.0, 100.0, step=0.1)
+INDUS = st.number_input("Proportion of Non-Retail Business Acres", 0.0, 50.0, step=0.1)
+CHAS = st.selectbox("Charles River (1: Yes, 0: No)", [0, 1])
+NOX = st.number_input("Nitrogen Oxide Concentration", 0.0, 1.0, step=0.01)
+RM = st.number_input("Average Rooms per Dwelling", 1.0, 10.0, step=0.1)
+AGE = st.number_input("Proportion of Owner-Occupied Units Built Before 1940", 0.0, 100.0, step=0.1)
+DIS = st.number_input("Weighted Distance to Employment Centers", 0.0, 10.0, step=0.1)  # Missing Feature Added ✅
+
+# Prediction
+if st.button("Predict"):
+    features = np.array([[CRIM, ZN, INDUS, CHAS, NOX, RM, AGE, DIS]])  # Now 8 features ✅
+    prediction = model.predict(features)
+    st.success(f"Estimated House Price: ${prediction[0]:,.2f}")

house_price_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c6b674affa293155e18bffdb6764ae6ed41de7f461d64bd23a0d97d958d1355f
+size 709

model.py

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+import pandas as pd
+import numpy as np
+import pickle
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LinearRegression
+from sklearn.datasets import fetch_california_housing
+
+# Load California Housing Dataset
+data = fetch_california_housing()
+df = pd.DataFrame(data.data, columns=data.feature_names)
+df['PRICE'] = data.target
+
+# Prepare Data
+X = df.drop(columns=['PRICE'])
+y = df['PRICE']
+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)
+
+# Save Model
+with open("house_price_model.pkl", "wb") as f:
+    pickle.dump(model, f)
+
+print("✅ Model trained and saved as 'house_price_model.pkl'")