Upload 3 files

Elite27.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7e4c0b163f0bccb4a44521b1172f60e10d8a7726ad67d7342f021f764da421d9
+size 633

app.py

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+import streamlit as st
+import pickle
+import numpy as np
+from sklearn.linear_model import LinearRegression
+import pickle
+import pandas as pd
+# Load the trained model
+with open("Elite27.pkl", "rb") as f:
+    model = pickle.load(f)
+
+st.title("🏡 House Price Prediction App")
+
+# User input fields
+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:Rural  1:Semi Urban   2: Urban:", [0, 1, 2])
+year_built = st.number_input("Enter Year Built:", min_value=1900, max_value=2025)
+
+# Predict price
+if st.button("Predict Price 💰"):
+    user_data = np.array([[square_feet, bedrooms, bathrooms, neighborhood, year_built]], dtype=object)
+    prediction = model.predict(user_data)
+    st.success(f"🏠 Estimated House Price: ${prediction[0]:,.2f}")
+
+

requirements.txt

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+streamlit
+pickle
+numpy
+scikit-learn
+pandas