File size: 3,841 Bytes
b36eee9 0a14a79 b36eee9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 | 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 / "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()
|