Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import joblib
+from xgboost import XGBRegressor
+from sklearn.preprocessing import StandardScaler, OneHotEncoder
+import shap
+from streamlit_shap import st_shap
+
+# Page configuration
+st.set_page_config(
+    page_title="Airbnb Price Prediction in Copenhagen",
+    page_icon="🏠")
+
+st.title('Predict Airbnb Prices in Copenhagen')
+
+# Display an image
+st.image('https://gdkfiles.visitdenmark.com/files/382/164757_Nyhavn_Jacob-Schjrring-og-Simon-Lau.jpg?width=987', caption='Copenhagen', use_column_width=True)
+
+# Load model and preprocessing objects
+@st.cache_resource
+def load_model_objects():
+    model_xgb = joblib.load('model_xgb.joblib')
+    scaler = joblib.load('scaler.joblib')
+    ohe = joblib.load('ohe.joblib')
+    return model_xgb, scaler, ohe
+
+model_xgb, scaler, ohe = load_model_objects()
+
+# Create SHAP explainer
+explainer = shap.TreeExplainer(model_xgb)
+
+# App description
+with st.expander("What's this app?"):
+    st.markdown("""
+    This app helps you determine an appropriate nightly rate for your Airbnb listing in Copenhagen.
+    We've trained an AI model on successful listings in Copenhagen to provide pricing suggestions based on a few key inputs.
+    Our recommendation is to adjust the suggested price by about ±350 DKK depending on your specific amenities and the quality of your place.
+    As a bonus feature 🌟, we've included an AI explainer 🤖 to help you understand the factors influencing the predicted price.
+    """)
+
+st.subheader('Describe your place')
+
+# User inputs
+col1, col2 = st.columns(2)
+
+with col1:
+    n_hood = st.selectbox('Neighborhood', options=ohe.categories_[0])
+    room_type = st.radio('Room Type', options=ohe.categories_[1])
+    instant_bookable = st.checkbox('Instant Booking Available')
+    accommodates = st.number_input('Maximum Guests', min_value=1, max_value=16, value=2)
+
+with col2:
+    bedrooms = st.number_input('Number of Bedrooms', min_value=0, max_value=10, value=1)
+    beds = st.number_input('Number of Beds', min_value=1, max_value=16, value=1)
+    min_nights = st.number_input('Minimum Nights Stay', min_value=1, max_value=30, value=1)
+
+# Prediction button
+if st.button('Predict Price 🚀'):
+    # Prepare categorical features
+    cat_features = pd.DataFrame({'neighbourhood_cleansed': [n_hood], 'room_type': [room_type]})
+    cat_encoded = pd.DataFrame(ohe.transform(cat_features).todense(), 
+                               columns=ohe.get_feature_names_out(['neighbourhood_cleansed', 'room_type']))
+    
+    # Prepare numerical features
+    num_features = pd.DataFrame({
+        'instant_bookable': [instant_bookable],
+        'accommodates': [accommodates],
+        'bedrooms': [bedrooms],
+        'beds': [beds],
+        'minimum_nights_avg_ntm': [min_nights]
+    })
+    num_scaled = pd.DataFrame(scaler.transform(num_features), columns=num_features.columns)
+    
+    # Combine features
+    features = pd.concat([num_scaled, cat_encoded], axis=1)
+    
+    # Make prediction
+    predicted_price = model_xgb.predict(features)[0]
+    
+    # Display prediction
+    st.metric(label="Predicted price per night", value=f'{round(predicted_price)} DKK')
+    
+    # Calculate and display price range
+    lower_range = max(0, round(predicted_price - 350))
+    upper_range = round(predicted_price + 350)
+    st.write(f"Suggested price range: {lower_range} - {upper_range} DKK")
+    
+    # SHAP explanation
+    st.subheader('Price Factors Explained 🤖')
+    shap_values = explainer.shap_values(features)
+    st_shap(shap.force_plot(explainer.expected_value, shap_values, features), height=400, width=600)
+    
+    st.markdown("""
+    This plot shows how each feature contributes to the predicted price:
+    - Blue bars push the price lower
+    - Red bars push the price higher
+    - The length of each bar indicates the strength of the feature's impact
+    """)
+
+# Footer
+st.markdown("---")
+st.markdown("Developed with ❤️ using Streamlit and AI")