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apartment_price_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f4ad8f12166bee59259972339e137a6ede2a2922cf4c39f210e164e2ea950b49
+size 11976223

app.py

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+from pathlib import Path
+import pickle
+
+import gradio as gr
+import pandas as pd
+
+
+BASE_DIR = Path(__file__).resolve().parent
+MODEL_PATH = BASE_DIR / "apartment_price_model.pkl"
+DATA_PATH = BASE_DIR / "data" / "raw" / "original_apartment_data_analytics_hs24.csv"
+
+
+with MODEL_PATH.open("rb") as f:
+    model = pickle.load(f)
+
+
+df = pd.read_csv(DATA_PATH)
+municipality_columns = ["town", "postalcode", "pop", "pop_dens", "frg_pct", "emp", "tax_income"]
+municipality_df = df[municipality_columns].dropna().copy()
+municipality_df["town"] = municipality_df["town"].astype(str).str.strip()
+municipality_df = municipality_df.drop_duplicates(subset=["town"]).sort_values("town")
+MUNICIPALITY_DATA = municipality_df.set_index("town").to_dict("index")
+
+
+def _build_features(rooms: float, area: float, town: str) -> tuple[pd.DataFrame, dict]:
+    municipality = MUNICIPALITY_DATA[town]
+
+    pop = float(municipality["pop"])
+    pop_dens = float(municipality["pop_dens"])
+    frg_pct = float(municipality["frg_pct"])
+    emp = float(municipality["emp"])
+    tax_income = float(municipality["tax_income"])
+
+    municipality_area_proxy = pop / pop_dens
+    emp_per_resident = emp / pop
+    foreigner_count_est = pop * (frg_pct / 100)
+
+    features = pd.DataFrame(
+        [
+            {
+                "rooms": rooms,
+                "area": area,
+                "pop": pop,
+                "pop_dens": pop_dens,
+                "frg_pct": frg_pct,
+                "emp": emp,
+                "tax_income": tax_income,
+                "municipality_area_proxy": municipality_area_proxy,
+                "emp_per_resident": emp_per_resident,
+                "foreigner_count_est": foreigner_count_est,
+            }
+        ]
+    )
+
+    return features, municipality
+
+
+def predict_price(rooms: float, area: float, town: str) -> tuple[str, str]:
+    features, municipality = _build_features(rooms=rooms, area=area, town=town)
+    prediction = float(model.predict(features)[0])
+
+    pop = float(municipality["pop"])
+    pop_dens = float(municipality["pop_dens"])
+    frg_pct = float(municipality["frg_pct"])
+    emp = float(municipality["emp"])
+    
+    municipality_area_proxy = pop / pop_dens
+    emp_per_resident = emp / pop
+    foreigner_count_est = pop * (frg_pct / 100)
+
+    details = (
+        f"Ort: {town} (PLZ {int(municipality['postalcode'])})\n\n"
+        f"Gemeindedaten:\n"
+        f"  Bevölkerung: {int(pop):,}\n"
+        f"  Bevölkerungsdichte: {pop_dens:.1f}/km²\n"
+        f"  Ausländeranteil: {frg_pct:.1f}%\n"
+        f"  Beschäftigte: {int(emp):,}\n"
+        f"  Steuerbares Einkommen: CHF {municipality['tax_income']:,.0f}\n\n"
+        f"Zusätzliche berechnete Features:\n"
+        f"  Gemeindegröße: {municipality_area_proxy:.2f} km²\n"
+        f"  Arbeitsplatzquote: {emp_per_resident:.3f}\n"
+        f"  Ausländerpopulation: {int(foreigner_count_est):,}"
+    )
+
+    return f"CHF {prediction:,.2f} pro Monat", details
+
+
+demo = gr.Interface(
+    fn=predict_price,
+    inputs=[
+        gr.Slider(1, 8, value=3, step=0.5, label="Zimmer"),
+        gr.Slider(20, 250, value=80, step=1, label="Wohnfläche (m²)"),
+        gr.Dropdown(choices=list(MUNICIPALITY_DATA.keys()), value="Zürich", label="Gemeinde"),
+    ],
+    outputs=[
+        gr.Textbox(label="Geschätzte Miete"),
+        gr.Textbox(label="Verwendete Gemeindedaten"),
+    ],
+    examples=[
+        [2.5, 60, "Zürich"],
+        [3.5, 90, "Winterthur"],
+        [4.5, 120, "Uster"],
+    ],
+    title="Apartment Price Prediction – Kanton Zürich",
+    description=(
+        "Vorhersage der monatlichen Wohnungsmiete mit einem Random-Forest-Regressionsmodell. "
+        "Die App nutzt Basismerkmale und zusätzliche Feature-Engineering-Variablen "
+        "(municipality_area_proxy, emp_per_resident, foreigner_count_est)."
+    ),
+)
+
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+scikit-learn