README.md

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.

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:

1. Install the required libraries:

   pip install scikit-learn pandas numpy joblib