Spaces:
Sleeping
Sleeping
File size: 3,516 Bytes
f0745d3 |
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 |
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error, r2_score
import gradio as gr
# URL to the Excel dataset on Hugging Face
data_url = "https://huggingface.co/datasets/leadingbridge/flat/resolve/main/NorthPoint30.xlsx"
# Load dataset (using openpyxl as the engine)
df = pd.read_excel(data_url, engine="openpyxl")
# Drop columns that are not needed for prediction
cols_to_drop = ['Usage', 'Address', 'PricePerSquareFeet', 'InstrumentDate', 'Floor', 'Unit']
df.drop(columns=cols_to_drop, inplace=True, errors='ignore')
# Rename useful columns for consistency
df.rename(columns={"Floor.1": "Floor", "Unit.1": "Unit"}, inplace=True)
# Ensure the dataset has the necessary columns
required_columns = ['District', 'PriceInMillion', 'Longitude', 'Latitude', 'Floor', 'Unit', 'Area', 'Year', 'WeekNumber']
if not all(col in df.columns for col in required_columns):
raise ValueError("Dataset is missing one or more required columns.")
# Define features and target variable
feature_names = ['District', 'Longitude', 'Latitude', 'Floor', 'Unit', 'Area', 'Year', 'WeekNumber']
X = df[feature_names]
y = df['PriceInMillion']
# Split into training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Define a parameter grid for RandomForestRegressor and perform grid search
rf_param_grid = {
'n_estimators': [50, 100, 150],
"max_depth": [4, 6, 8],
"max_features": ['sqrt', 'log2', 3],
"random_state": [42]
}
rf_grid = GridSearchCV(RandomForestRegressor(), rf_param_grid, refit=True, verbose=1, cv=5, error_score='raise')
rf_grid.fit(X_train, y_train)
# Use the best estimator
model = rf_grid.best_estimator_
# Print model performance on the test set
rf_pred = model.predict(X_test)
rf_rmse = np.sqrt(mean_squared_error(y_test, rf_pred))
rf_r2 = r2_score(y_test, rf_pred)
print("Random Forest RMSE: ", rf_rmse)
print("Random Forest R2: ", rf_r2)
def price_prediction(model, feature_names, new_data):
new_data_df = pd.DataFrame([new_data], columns=feature_names)
prediction = model.predict(new_data_df)
return prediction[0]
def predict(district, longitude, latitude, floor, unit, area, year, weeknumber):
new_data = [district, longitude, latitude, floor, unit, area, year, weeknumber]
prediction = price_prediction(model, feature_names, new_data)
return f"${prediction:,.2f} Million"
# Gradio Interface
iface = gr.Interface(
fn=predict,
inputs=[
gr.Dropdown(choices=list(range(1, 9)), label='District (1 = Taikoo Shing, 2 = Mei Foo Sun Chuen, 3 = South Horizons, 4 = Whampoa Garden)'),
gr.Number(label='Longitude'),
gr.Number(label='Latitude'),
gr.Dropdown(choices=list(range(1, 71)), label='Floor'),
gr.Dropdown(choices=list(range(1, 31)), label='Unit (e.g., A=1, B=2, C=3, ...)'),
gr.Slider(minimum=137, maximum=5000, step=1, label='Area (in sq. feet)'),
gr.Dropdown(choices=[2024, 2025], label='Year'),
gr.Dropdown(choices=list(range(1, 53)), label='Week Number')
],
outputs=gr.Textbox(label='Price Prediction'),
title="PROPERTY PRICE PREDICTION TOOL (Larry Pang Final Year Project)",
description="Predict the price of a new property based on District, Longitude, Latitude, Floor, Unit, Area, Year, and Week Number."
)
if __name__ == "__main__":
iface.launch()
|