https://github.com/mxsaad/house-price-predictor

Linear regression model to predict the price of houses in Ames, Iowa based on 79 explanatory variables.
https://github.com/mxsaad/house-price-predictor

jupyter-notebook kaggle-competition linear-regression machine-learning

Last synced: 15 days ago
JSON representation

Linear regression model to predict the price of houses in Ames, Iowa based on 79 explanatory variables.

• Host: GitHub
• URL: https://github.com/mxsaad/house-price-predictor
• Owner: mxsaad
• License: mit
• Created: 2023-03-15T20:14:50.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2023-10-07T04:34:37.000Z (about 1 year ago)
• Last Synced: 2024-11-09T03:53:06.595Z (about 2 months ago)
• Topics: jupyter-notebook, kaggle-competition, linear-regression, machine-learning
• Language: Jupyter Notebook
• Homepage: https://github.com/mxsaad/house-price-predictor/blob/main/model.ipynb
• Size: 1.34 MB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
# House Price Predictor

## Project Overview

This repository contains the code for a House Price Predictor, a Python-based project that predicts house prices with 87% accuracy. It utilizes linear regression and handles incomplete data effectively.

## Tech Stack

- **Python**: The project is developed in Python.

- **NumPy**: Used for numerical computations.

- **Matplotlib**: Used for data visualization.

- **Seaborn**: Used for advanced data visualization.

- **Pandas**: Used for data manipulation.

## Getting Started

To run the House Price Predictor locally:

1. Clone this repository to your local machine.

2. Navigate to the project directory.

3. Install required Python libraries:

```bash

pip install numpy matplotlib seaborn pandas scikit-learn

```

4. Run the predictor script:

```bash

python house_price_predictor.py

```

## Contributing

Contributions are welcome; please follow standard Git and GitHub contribution workflows.

## License

This project is licensed under the [MIT License](LICENSE).