File size: 1,157 Bytes
bf61eb5 598d29f bf61eb5 598d29f bf61eb5 8e9ff0f bf61eb5 | 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 | import gradio as gr
import pandas as pd
from xgboost import XGBRegressor
from sklearn.model_selection import train_test_split
# Load dataset
data = pd.read_csv("house_price_dataset.csv")
data = data.dropna()
# Features and target
x = data.drop("price", axis=1)
y = data["price"]
# Train-test split
xtrain, xtest, ytrain, ytest = train_test_split(x, y, test_size=0.2, random_state=42)
# Train model
model = XGBRegressor()
model.fit(xtrain, ytrain)
# Gradio Interface (inline prediction)
interface = gr.Interface(
fn=lambda bed, bath, size, loc, age: f"Predicted House Price: {model.predict([[bed, bath, size, loc, age]])[0]:,.2f}",
inputs=[
gr.Number(label="Number of Bedrooms", value=3),
gr.Number(label="Number of Bathrooms", value=2),
gr.Number(label="Size (sq feet)", value=1200),
gr.Slider(1, 10, step=1, label="Location (1-10)", value=5),
gr.Number(label="Age of the House", value=5),
],
outputs=gr.Textbox(label="Prediction"),
title="House Price Prediction by ",
description="Enter house details to predict price using XGBoost."
)
if __name__ == "__main__":
interface.launch() |