--- 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. House Price Prediction Model ## **Prediction Results** The model provides an estimated house price based on the inputs, as shown in the image. ![House Price Prediction](https://huggingface.co/Tahani1/Houses-Prices-Prediction/resolve/main/C90E9037-01EB-44E8-AAD9-4197D181AEA2.jpeg) ## 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 ## Training Data The model was trained on a dataset containing house prices along with the following features: - **Number of Rooms**: The number of rooms in the house. - **Distance to Center**: The distance from the house to the city center in kilometers. - **Country**: The country where the house is located. - **Build Quality**: A subjective measure of the build quality of the house, ranging from 1 to 10. The dataset used for training is `Prices house.csv`. ### 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