metadata

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.

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:

Using Python Code

To use the model in Python, follow these steps:

Install the required libraries:

pip install scikit-learn pandas numpy joblib