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---
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.
## 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:
[](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
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