Télécharger le Modèle pour faire des prédictions

', unsafe_allow_html=True) uploaded_model = st.file_uploader("", type=["pkl", "joblib", "sav"]) if uploaded_model is not None: st.success("✅ Modèle téléchargé avec succès!") # Confidence interval settings st.markdown('', unsafe_allow_html=True) st.markdown('

', unsafe_allow_html=True) # Add confidence level settings confidence_level = st.slider( "Niveau de confiance (%)", min_value=50, max_value=99, value=95, step=5, help="Niveau de confiance pour l'intervalle de prédiction" ) # Add error margin slider error_margin_percent = st.slider( "Marge d'erreur estimée (%)", min_value=5, max_value=30, value=15, step=5, help="Pourcentage d'erreur estimée pour le modèle" ) st.markdown('

', unsafe_allow_html=True) # Dataset Statistics if sample_data is not None: # Preview st.markdown('', unsafe_allow_html=True) # Features stats st.markdown('

', unsafe_allow_html=True) st.markdown('', unsafe_allow_html=True) # Price stats (highlighted) if 'price' in sample_data.columns: price_stats = sample_data['price'].describe() st.markdown("""

Prix (Target Variable)

Min: ${:,.2f} | Max: ${:,.2f}

Mean: ${:,.2f} | Median: ${:,.2f}

""".format( price_stats['min'], price_stats['max'], price_stats['mean'], price_stats['50%'] ), unsafe_allow_html=True) # Other features num_cols = sample_data.select_dtypes(include=['int64', 'float64']).columns for col in num_cols: if col != 'id' and col != 'price': # Exclude ID and target variable stats = sample_data[col].describe() st.markdown(f"""

{col}
Min: {stats['min']:.2f} | Max: {stats['max']:.2f}
Mean: {stats['mean']:.2f}

""", unsafe_allow_html=True) st.markdown('

', unsafe_allow_html=True) # Main content st.markdown('

Détails de la Propriété

', unsafe_allow_html=True) # Initialize model model = None if uploaded_model is not None: try: model = load_model(uploaded_model) # Success message is now shown in the sidebar except Exception as e: st.error(f"Erreur lors du chargement du modèle : {e}") st.info("Veuillez vérifier que le modèle est un fichier XGBoost valide au format .pkl") # Define all available features all_features = { 'bedrooms': "Bedrooms", 'bathrooms': "Bathrooms", 'sqft_living': "Living Area (sqft)", 'sqft_lot': "Lot Size (sqft)", 'floors': "Floors", 'waterfront': "Waterfront", 'view': "View", 'condition': "Condition", 'grade': "Grade", 'yr_built': "Year Built", 'yr_renovated': "Year Renovated", 'zipcode': "Zipcode", 'lat': "Latitude", 'long': "Longitude", 'sqft_above': "Square Feet Above Ground", 'sqft_basement': "Square Feet Basement" } # Multi-select to choose fields to omit - styling with custom container st.markdown('

', unsafe_allow_html=True) st.subheader("Sélectionnez les champs à omettre pour la prédiction") omitted_fields = st.multiselect( "", options=list(all_features.values()) ) st.markdown('

', unsafe_allow_html=True) # Create lookup from display name to field name display_to_field = {v: k for k, v in all_features.items()} # Get the field names that are omitted omitted_field_names = [display_to_field[field] for field in omitted_fields] # Create form for user inputs with st.form("property_form"): # Apply CSS to the form st.markdown(""" """, unsafe_allow_html=True) # Wrap the form in a styled container st.markdown('

', unsafe_allow_html=True) # Create columns for a cleaner form layout col1, col2, col3 = st.columns(3) # Initialize input variables with None bedrooms = bathrooms = sqft_living = sqft_lot = floors = None waterfront = view = condition = grade = yr_built = None yr_renovated = zipcode = lat = long = sqft_above = sqft_basement = None # Column 1 features with col1: if "Bedrooms" not in omitted_fields: st.markdown('

Chambres

', unsafe_allow_html=True) bedrooms = st.number_input("Chambres", min_value=0, max_value=10, value=3, step=1, label_visibility="collapsed") if "Bathrooms" not in omitted_fields: st.markdown('

Salles de bain

', unsafe_allow_html=True) bathrooms = st.number_input("Salles de bain", min_value=0.0, max_value=10.0, value=2.0, step=0.25, label_visibility="collapsed") if "Living Area (sqft)" not in omitted_fields: st.markdown('

Surface habitable (pieds carrés)

', unsafe_allow_html=True) sqft_living = st.number_input("Surface habitable", min_value=200, max_value=10000, value=1500, step=100, label_visibility="collapsed") if "Lot Size (sqft)" not in omitted_fields: st.markdown('

Taille du lot (pieds carrés)

', unsafe_allow_html=True) sqft_lot = st.number_input("Taille du lot", min_value=500, max_value=200000, value=5000, step=500, label_visibility="collapsed") if "Floors" not in omitted_fields: st.markdown('

Étages

', unsafe_allow_html=True) floors = st.number_input("Étages", min_value=1.0, max_value=4.0, value=1.0, step=0.5, label_visibility="collapsed") # Column 2 features with col2: if "Waterfront" not in omitted_fields: st.markdown('

Vue sur l\'eau

', unsafe_allow_html=True) waterfront = st.selectbox("Vue sur l'eau", options=[0, 1], format_func=lambda x: "Oui" if x == 1 else "Non", label_visibility="collapsed") if "View" not in omitted_fields: st.markdown('

Vue

', unsafe_allow_html=True) view = st.selectbox("Vue", options=[0, 1, 2, 3, 4], format_func=lambda x: {0: "Aucune", 1: "Passable", 2: "Moyenne", 3: "Bonne", 4: "Excellente"}[x], label_visibility="collapsed") if "Condition" not in omitted_fields: st.markdown('

État

', unsafe_allow_html=True) condition = st.selectbox("État", options=[1, 2, 3, 4, 5], format_func=lambda x: {1: "Mauvais", 2: "Passable", 3: "Moyen", 4: "Bon", 5: "Excellent"}[x], label_visibility="collapsed") if "Grade" not in omitted_fields: st.markdown('

Qualité de construction

', unsafe_allow_html=True) grade = st.selectbox("Qualité", options=list(range(1, 14)), format_func=lambda x: { 1: "Très mauvaise", 2: "Mauvaise", 3: "Mauvaise", 4: "Moyenne inférieure", 5: "Moyenne", 6: "Moyenne", 7: "Bonne", 8: "Bonne", 9: "Meilleure", 10: "Meilleure", 11: "Excellente", 12: "Excellente", 13: "Luxe" }[x], label_visibility="collapsed") if "Year Built" not in omitted_fields: st.markdown('

Année de construction

', unsafe_allow_html=True) yr_built = st.number_input("Année de construction", min_value=1900, max_value=2023, value=1980, step=1, label_visibility="collapsed") # Column 3 features with col3: if "Year Renovated" not in omitted_fields: st.markdown('

Année de rénovation (0 si aucune)

', unsafe_allow_html=True) yr_renovated = st.number_input("Année de rénovation", min_value=0, max_value=2023, value=0, step=1, label_visibility="collapsed") if "Zipcode" not in omitted_fields: st.markdown('

Code postal

', unsafe_allow_html=True) zipcode = st.number_input("Code postal", min_value=98000, max_value=99000, value=98000, step=1, label_visibility="collapsed") if "Latitude" not in omitted_fields: st.markdown('

Latitude

', unsafe_allow_html=True) lat = st.number_input("Latitude", min_value=47.0, max_value=48.0, value=47.5, step=0.01, format="%.4f", label_visibility="collapsed") if "Longitude" not in omitted_fields: st.markdown('

Longitude

', unsafe_allow_html=True) long = st.number_input("Longitude", min_value=-123.0, max_value=-121.0, value=-122.0, step=0.01, format="%.4f", label_visibility="collapsed") if "Square Feet Above Ground" not in omitted_fields: st.markdown('

Surface au-dessus du sol (pieds carrés)

', unsafe_allow_html=True) sqft_above = st.number_input("Surface au-dessus du sol", min_value=200, max_value=10000, value=1000, step=100, label_visibility="collapsed") if "Square Feet Basement" not in omitted_fields: st.markdown('

Surface du sous-sol (pieds carrés)

', unsafe_allow_html=True) sqft_basement = st.number_input("Surface du sous-sol", min_value=0, max_value=5000, value=0, step=100, label_visibility="collapsed") # Close the container div st.markdown('

', unsafe_allow_html=True) # Submit button with custom styling col1, col2, col3 = st.columns([1, 2, 1]) with col2: submitted = st.form_submit_button("Prédire le Prix", use_container_width=True) # Add additional styling to the button st.markdown(""" """, unsafe_allow_html=True) # Process the form submission if submitted: if model is None: st.error("Veuillez télécharger un modèle d'abord.") else: try: # Get the confidence level and error margin from the sidebar conf_level = confidence_level / 100.0 error_margin = error_margin_percent / 100.0 # Prepare input data input_data = { 'bedrooms': bedrooms, 'bathrooms': bathrooms, 'sqft_living': sqft_living, 'sqft_lot': sqft_lot, 'floors': floors, 'waterfront': waterfront, 'view': view, 'condition': condition, 'grade': grade, 'sqft_above': sqft_above, 'sqft_basement': sqft_basement, 'yr_built': yr_built, 'yr_renovated': yr_renovated, 'zipcode': zipcode, 'lat': lat, 'long': long, 'sqft_living15': sqft_living, # Using the same value as sqft_living as an approximation 'sqft_lot15': sqft_lot # Using the same value as sqft_lot as an approximation } # Track omitted features omitted_features = {k: None for k, v in input_data.items() if v is None} # Preprocess inputs processed_input = preprocess_inputs(input_data) # Make prediction with confidence interval predicted_price, interval_lower, interval_upper = predict_price( model, processed_input, with_confidence_interval=True, confidence_level=conf_level, error_margin=error_margin ) # Display prediction with custom styling st.markdown(f'

Prix Prédit: ${predicted_price:,.2f}

', unsafe_allow_html=True) # Display confidence interval st.markdown(f"""

Intervalle de confiance ({confidence_level}%): ${interval_lower:,.2f} - ${interval_upper:,.2f}

""", unsafe_allow_html=True) # Show input summary with styled container with st.expander("📋 Résumé des détails de la propriété"): st.markdown('

', unsafe_allow_html=True) # Show which features were used as provided by user st.subheader("🔹 Caractéristiques fournies par l'utilisateur") provided_features = {k: v for k, v in input_data.items() if v is not None} if provided_features: st.json(provided_features) else: st.info("Aucune caractéristique n'a été fournie par l'utilisateur.") # Show which features were omitted and filled with default values if omitted_features: st.subheader("🔸 Caractéristiques omises (remplies avec des valeurs par défaut)") st.write("Les caractéristiques suivantes ont été omises et remplies avec des valeurs par défaut du jeu de données:") # Create columns for better display omitted_cols = st.columns(3) for i, feature in enumerate(omitted_features): col_idx = i % 3 with omitted_cols[col_idx]: st.markdown(f"**{feature}**: {processed_input[feature].values[0]:.2f}") # Show the final processed input used for prediction st.subheader("📊 Données d'entrée finales utilisées") st.dataframe(processed_input, use_container_width=True) # Show confidence interval details st.subheader("📈 Détails de l'intervalle de confiance") st.write(f"Niveau de confiance: {confidence_level}%") st.write(f"Marge d'erreur estimée: {error_margin_percent}%") st.write(f"Intervalle: ${interval_lower:,.2f} - ${interval_upper:,.2f}") st.markdown('

', unsafe_allow_html=True) except Exception as e: st.error(f"Une erreur s'est produite lors de la prédiction: {e}") st.error("Détails: " + str(e)) # More detailed error message # Add a footer st.markdown('', unsafe_allow_html=True)

🏠 Prédiction de Prix Immobilier

Télécharger le Modèle pour faire des prédictions

⚙️ Paramètres

📊 Aperçu du Dataset

Caractéristiques Numériques

Prix (Target Variable)

Détails de la Propriété