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app.py

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+# %%
+import gradio as gr
+import pandas as pd
+import pickle
+
+# Load model from file
+model_filename = "random_forest_model.pkl"
+with open(model_filename, mode="rb") as f:
+    random_forest_model = pickle.load(f)
+#import dataset
+df = pd.read_csv('apartments_data_enriched_with_new_features.csv')
+
+locations = {
+    "Zürich": 261,
+    "Kloten": 62,
+    "Uster": 198,
+    "Illnau-Effretikon": 296,
+    "Feuerthalen": 27,
+    "Pfäffikon": 177,
+    "Ottenbach": 11,
+    "Dübendorf": 191,
+    "Richterswil": 138,
+    "Maur": 195,
+    "Embrach": 56,
+    "Bülach": 53,
+    "Winterthur": 230,
+    "Oetwil am See": 157,
+    "Russikon": 178,
+    "Obfelden": 10,
+    "Wald (ZH)": 120,
+    "Niederweningen": 91,
+    "Dällikon": 84,
+    "Buchs (ZH)": 83,
+    "Rüti (ZH)": 118,
+    "Hittnau": 173,
+    "Bassersdorf": 52,
+    "Glattfelden": 58,
+    "Opfikon": 66,
+    "Hinwil": 117,
+    "Regensberg": 95,
+    "Langnau am Albis": 136,
+    "Dietikon": 243,
+    "Erlenbach (ZH)": 151,
+    "Kappel am Albis": 6,
+    "Stäfa": 158,
+    "Zell (ZH)": 231,
+    "Turbenthal": 228,
+    "Oberglatt": 92,
+    "Winkel": 72,
+    "Volketswil": 199,
+    "Kilchberg (ZH)": 135,
+    "Wetzikon (ZH)": 121,
+    "Zumikon": 160,
+    "Weisslingen": 180,
+    "Elsau": 219,
+    "Hettlingen": 221,
+    "Rüschlikon": 139,
+    "Stallikon": 13,
+    "Dielsdorf": 86,
+    "Wallisellen": 69,
+    "Dietlikon": 54,
+    "Meilen": 156,
+    "Wangen-Brüttisellen": 200,
+    "Flaach": 28,
+    "Regensdorf": 96,
+    "Niederhasli": 90,
+    "Bauma": 297,
+    "Aesch (ZH)": 241,
+    "Schlieren": 247,
+    "Dürnten": 113,
+    "Unterengstringen": 249,
+    "Gossau (ZH)": 115,
+    "Oberengstringen": 245,
+    "Schleinikon": 98,
+    "Aeugst am Albis": 1,
+    "Rheinau": 38,
+    "Höri": 60,
+    "Rickenbach (ZH)": 225,
+    "Rafz": 67,
+    "Adliswil": 131,
+    "Zollikon": 161,
+    "Urdorf": 250,
+    "Hombrechtikon": 153,
+    "Birmensdorf (ZH)": 242,
+    "Fehraltorf": 172,
+    "Weiach": 102,
+    "Männedorf": 155,
+    "Küsnacht (ZH)": 154,
+    "Hausen am Albis": 4,
+    "Hochfelden": 59,
+    "Fällanden": 193,
+    "Greifensee": 194,
+    "Mönchaltorf": 196,
+    "Dägerlen": 214,
+    "Thalheim an der Thur": 39,
+    "Uetikon am See": 159,
+    "Seuzach": 227,
+    "Uitikon": 248,
+    "Affoltern am Albis": 2,
+    "Geroldswil": 244,
+    "Niederglatt": 89,
+    "Thalwil": 141,
+    "Rorbas": 68,
+    "Pfungen": 224,
+    "Weiningen (ZH)": 251,
+    "Bubikon": 112,
+    "Neftenbach": 223,
+    "Mettmenstetten": 9,
+    "Otelfingen": 94,
+    "Flurlingen": 29,
+    "Stadel": 100,
+    "Grüningen": 116,
+    "Henggart": 31,
+    "Dachsen": 25,
+    "Bonstetten": 3,
+    "Bachenbülach": 51,
+    "Horgen": 295
+}
+
+# Define the core prediction function
+def predict_apartment(rooms, area, town, tax_income, luxurious, temporary, furnished, room_per_m2, zurich_city):
+    bfs_number = locations[town]
+    df1 = df[df['bfs_number']==bfs_number].copy()
+    df1.reset_index(inplace=True)
+    df1.loc[0, 'rooms'] = rooms
+    df1.loc[0, 'area'] = area
+    df1.loc[0, 'tax_income'] = tax_income
+    df1.loc[0, 'luxurious'] = luxurious
+    df1.loc[0, 'temporary'] = temporary
+    df1.loc[0, 'furnished'] = furnished
+    df1.loc[0, 'room_per_m2'] = room_per_m2
+    df1.loc[0, 'zurich_city'] = zurich_city
+    
+    if len(df1) > 1:  # if there are more than one record with the same bfs_number, calculate the mean price
+        df1[0, 'price'] = df1['price'].mean()
+        
+    prediction = random_forest_model.predict(df1[['rooms', 'area', 'pop', 'pop_dens', 'frg_pct', 'emp', 'tax_income', 'room_per_m2', 'luxurious', 'temporary', 'furnished', 'area_cat_ecoded', 'zurich_city']])
+    return np.round(prediction[0], 0)
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=predict_apartment,
+    inputs=[
+        gr.Number(label="Rooms"),
+        gr.Number(label="Area"),
+        gr.Dropdown(choices=list(locations.keys()), label="Town"),
+        gr.Number(label="Tax Income"),
+        gr.Checkbox(label="Luxurious"),
+        gr.Checkbox(label="Temporary"),
+        gr.Checkbox(label="Furnished")
+    ],
+    outputs=gr.Number(),
+    examples=[
+        [4.5, 120, "Dietlikon", 90000, 2000, True, False, True],
+        [3.5, 60, "Winterthur", 85000, 1500, False, True, False],
+        [2.5, 40, "Zürich", 110000, 5000, True, True, True],
+    ]
+)
+
+iface.launch()

random_forest_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9d863df2d1203ffa5af8797ef4c62d7a5a505959710479727c1e1c8d97e24c32
+size 3518996

requirements.txt

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+scikit-learn