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.gitignore

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+venv/

app.py

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+# %%
+import gradio as gr
+from sklearn.ensemble import RandomForestRegressor
+import numpy as np
+import pandas as pd
+import pickle
+# Define model filename
+model_filename = "random_forest_regression_custom_feature.pkl"
+
+try:
+    # Try to load the model
+    with open(model_filename, 'rb') as f:
+        model_data = pickle.load(f)
+        if isinstance(model_data, dict) and 'model' in model_data:
+            random_forest_model = model_data['model']
+except Exception as e:
+    print("Error")
+    exit(1)
+
+# Load and prepare BFS data
+df_bfs_data = pd.read_csv('bfs_municipality_and_tax_data.csv', sep=',', encoding='utf-8')
+df_bfs_data['tax_income'] = df_bfs_data['tax_income'].str.replace("'", "").astype(float)
+df_bfs_data['proximity_to_public_transportation'] = 500  # Default value in meters
+
+
+# %%
+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
+}
+def predict_apartment(rooms, area, proximity, town):
+    bfs_number = locations[town]
+    df = df_bfs_data[df_bfs_data['bfs_number']==bfs_number].copy()
+    df.reset_index(inplace=True)
+    df.loc[0, 'rooms'] = rooms
+    df.loc[0, 'area'] = area
+    df.loc[0, 'proximity_to_public_transportation'] = proximity  # Use user input instead of default
+    
+    if len(df) != 1:
+        return -1
+        
+    features = ['rooms', 'area', 'pop', 'pop_dens', 'frg_pct', 'emp', 'tax_income', 'proximity_to_public_transportation']
+    X = df[features].values
+    prediction = random_forest_model.predict(X)
+    return np.round(prediction[0], 0)
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=predict_apartment,
+    inputs=[
+        gr.Number(label="Number of Rooms"),
+        gr.Number(label="Area"),
+        gr.Slider(minimum=0, maximum=2000, value=500, step=50, 
+                 label="Distance to Public Transportation (meters)"),
+        gr.Dropdown(choices=locations.keys(), label="Town", type="value")
+    ],
+    outputs=[gr.Number(label="Predicted Price (CHF)")],
+    examples=[
+        [5.5, 110, 300, "Bülach"], 
+        [3, 90, 800, "Bassersdorf"]
+    ],
+    description="Predict apartment prices in Zürich based on rooms, area, proximity to public transportation, and location."
+)
+iface.launch()

random_forest_regression_custom_feature.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b568b08ae4f77ccc8ca6410a8a29b1c36b0b59e9ffed320d810a6d48322613a7
+size 1432809