Uploaded main files

app.py

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+from sklearn.pipeline import Pipeline 
+from sklearn.compose import ColumnTransformer
+from sklearn.preprocessing import StandardScaler, OneHotEncoder
+import streamlit as st
+import pandas as pd
+import joblib
+
+# Load data and model
+df = pd.read_csv('House_Rent_Dataset.csv')
+df['Extracted Floor'] = df['Floor'].str.extract(r'^(\d{1,2}|[A-Za-z]+)', expand=False)
+
+def map_floors(floor):
+    if floor.startswith('Ground'):
+        return 0
+    elif floor.startswith('Upper'):
+        return 0
+    elif floor.startswith('Lower'):
+        return -1
+    else:
+        return floor
+
+df['Extracted Floor'] = df['Extracted Floor'].apply(map_floors)
+
+# Load the trained model
+model = joblib.load('best_regression_model.pkl')
+
+# Define the preprocessor and pipeline
+preprocessor = ColumnTransformer(
+    transformers=[
+        ("num", StandardScaler(), ["BHK", "Size", 'Bathroom', 'Extracted Floor']),
+        ("cat", OneHotEncoder(), ["Area Type", "Area Locality", "City", 'Furnishing Status', 'Tenant Preferred', 'Point of Contact'])
+    ]
+)
+pipeline = Pipeline(steps=[("preprocessor", preprocessor), ("regressor", model)])
+
+# Fit the pipeline on the data (optional, if you want to refit with the data)
+pipeline.fit(df[['BHK', 'Size', 'Area Type', 'Area Locality', 'City',
+                 'Furnishing Status', 'Tenant Preferred', 'Bathroom', 'Point of Contact', 'Extracted Floor']], df["Rent"])
+
+# Function to predict the price
+def price_prediction(bhk, size, area_type, area_locality, city, furnishing_status, tenant_preferred, bathroom, point_of_contact, floor):
+    input_data = pd.DataFrame({
+        "BHK": [bhk],
+        "Size": [size],
+        "Area Type": [area_type],
+        "Area Locality": [area_locality],
+        "City": [city],
+        "Furnishing Status": [furnishing_status],
+        "Tenant Preferred": [tenant_preferred],
+        "Bathroom": [bathroom],
+        "Point of Contact": [point_of_contact],
+        "Extracted Floor": [floor]
+    })
+    prediction = pipeline.predict(input_data)[0]
+    return prediction
+
+# Main function to render the Streamlit app
+def main():
+    st.set_page_config(page_title="House Rent Prediction in India", layout="wide")
+    
+    # App title and description
+    st.title("🏠 House Rent Prediction in India")
+    st.markdown("""
+    **Enter the house features** below to predict the rent. 
+    Adjust the inputs to see how different characteristics affect the rent.
+    """)
+    
+    # Add custom CSS for styling
+    st.markdown("""
+    <style>
+    .stButton>button {
+        background-color: #28a745;
+        color: white;
+        font-size: 18px;
+        border-radius: 5px;
+        padding: 10px 20px;
+        margin-top: 20px;
+    }
+    .stButton>button:hover {
+        background-color: #218838;
+    }
+    .stText {
+        font-size: 16px;
+        color: #333;
+    }
+    .stTitle {
+        color: #007bff;
+    }
+    </style>
+    """, unsafe_allow_html=True)
+
+    # Side bar inputs (Better structure and user-friendly)
+    st.sidebar.header("Enter House Details")
+    
+    city = st.sidebar.selectbox("City", df["City"].unique())
+    bhk = st.sidebar.number_input("Number of Bedrooms, Hall, Kitchen", int(df["BHK"].min()), int(df["BHK"].max()))
+    size = st.sidebar.number_input("Size of the House in Square Feet", min_value=float(df["Size"].min()), max_value=float(df["Size"].max()), step=10.0)
+    bathroom = st.sidebar.number_input("Bathroom Number", 0, step=1)
+    floor = st.sidebar.number_input("Extracted Floor", -1, step=1)
+
+    # Dynamic options based on city selection
+    area_type = st.sidebar.selectbox("Area Type", df[df['City'] == city]['Area Type'].unique())
+    area_locality = st.sidebar.selectbox("Area Locality", df[df['City'] == city]['Area Locality'].unique())
+    tenant_preferred = st.sidebar.selectbox("Tenant Preferred", df["Tenant Preferred"].unique())
+    furnishing_status = st.sidebar.selectbox("Furnishing Status", df["Furnishing Status"].unique())
+    point_of_contact = st.sidebar.selectbox("Point of Contact", df["Point of Contact"].unique())
+    
+    # Prediction button
+    if st.sidebar.button("Predict Rent"):
+        price = price_prediction(bhk, size, area_type, area_locality, city, furnishing_status, tenant_preferred, bathroom, point_of_contact, floor)
+        price = float(price)
+        
+        # Display the result with enhanced visualization
+        st.subheader("Predicted Rent: 💲 **${:,.2f}**".format(price))
+        st.markdown("""
+        This is the estimated price based on the characteristics you provided. 
+        Please note that the actual market rent may vary.
+        """)
+
+if __name__ == "__main__":
+    main()

best_regression_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:cd04a521b67d7be76cfdc9257d32c72e8a573e19cbf959418a7f03cf74f914ac
+size 268997

requirements.txt

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+scikit-learn
+streamlit
+joblib