---
language: en
tags:
- machine-learning
- regression
- house-price-prediction
- sklearn
- knn
datasets:
- house-prices-dataset
url: "https://www.kaggle.com/datasets/manutrex78/houses-prices-according-to-location"  
metrics:
- r2_score
- mean_absolute_error
- root_mean_squared_error
license: creativeml-openrail-m
---

# House Price Prediction Model

This is a **K-Nearest Neighbors (KNN) Regressor** model trained to predict house prices based on features such as the number of rooms, distance to the city center, country, and build quality.

## Model Details

- **Model Type**: K-Nearest Neighbors Regressor (KNN)
- **Training Algorithm**: Scikit-learn's `KNeighborsRegressor`
- **Number of Neighbors**: 5
- **Input Features**:
  - Number of Rooms
  - Distance to Center (in km)
  - Country (Categorical)
  - Build Quality (1 to 10)
- **Target Variable**: House Price



### Using Gradio Interface

You can interact with the model using the Gradio interface hosted on Hugging Face Spaces:

[![Gradio App](https://img.shields.io/badge/🤗%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/your-username/your-space-name)

### Using Python Code

To use the model in Python, follow these steps:

1. Install the required libraries:
   ```bash
   pip install scikit-learn pandas numpy joblib