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https://github.com/the-developer-306/house-price-predictor
House Price Predictor: Harnessing machine learning algorithms to forecast housing prices in Boston, empowering buyers and sellers with accurate predictions based on key factors like location, crime rate, rooms, accessibility, and more.
https://github.com/the-developer-306/house-price-predictor
csv ipynb-jupyter-notebook joblib matplotlib numpy pandas python scikit-learn
Last synced: about 2 months ago
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House Price Predictor: Harnessing machine learning algorithms to forecast housing prices in Boston, empowering buyers and sellers with accurate predictions based on key factors like location, crime rate, rooms, accessibility, and more.
- Host: GitHub
- URL: https://github.com/the-developer-306/house-price-predictor
- Owner: the-developer-306
- Created: 2023-05-20T19:29:11.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2024-09-21T20:11:00.000Z (4 months ago)
- Last Synced: 2024-11-13T08:42:00.721Z (about 2 months ago)
- Topics: csv, ipynb-jupyter-notebook, joblib, matplotlib, numpy, pandas, python, scikit-learn
- Language: Jupyter Notebook
- Homepage:
- Size: 27.6 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# House Price Predictor
Harnessing machine learning algorithms to forecast housing prices in Boston, empowering buyers and sellers with accurate predictions based on key factors like location, crime rate, rooms, accessibility, and more.
## Table of Contents
- [Introduction](#introduction)
- [Features](#features)
- [Installation](#installation)
- [Usage](#usage)
- [Model](#model)
- [Contributing](#contributing)
- [License](#license)
- [Contact](#contact)
## Introduction
House Price Predictor utilizes machine learning to predict housing prices in Boston. The project includes data preprocessing, model training, and evaluation to provide reliable price predictions.
## Features
- Data preprocessing and cleaning
- Feature engineering
- Model training and evaluation
- Price prediction
## Installation
Clone the repository and install the necessary dependencies:
```
git clone https://github.com/the-developer-306/House-Price-Predictor.git
cd House-Price-Predictor
pip install -r requirements.txt
```
## Usage
1. **Prepare the data**: Ensure your dataset is formatted correctly. Use the provided dataset or your own.
2. **Open and run the following notebooks in Jupyter**:
- DRAGON REAL ESTATES (House Price Predictor).ipynb
- Making Price Predictions for DRAGON REAL ESTATES.ipynb
## Model
The project employs several regression algorithms to predict house prices. The models explored include:
- **Linear Regression**: A basic approach to model the relationship between input features and the target variable.
- **Decision Tree Regressor**: A non-linear model that splits the data into subsets based on feature values.
- **Random Forest Regressor**: An ensemble method that combines multiple decision trees to improve predictive performance.
Performance is evaluated using metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE).
## Contributing
Feel free to fork the repository and submit pull requests. For major changes, please open an issue first to discuss what you would like to change.
## License
This project is licensed under the MIT License.
## Contact
For any questions or suggestions, feel free to reach out:
- GitHub: [the-developer-306](https://github.com/the-developer-306)
- Email: [[email protected]](mailto:[email protected])