https://github.com/sharmas1ddharth/housing-price-prediction
https://github.com/sharmas1ddharth/housing-price-prediction
Last synced: 8 months ago
JSON representation
- Host: GitHub
- URL: https://github.com/sharmas1ddharth/housing-price-prediction
- Owner: sharmas1ddharth
- License: mit
- Created: 2021-10-22T14:56:04.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2022-01-05T11:48:59.000Z (almost 4 years ago)
- Last Synced: 2025-01-11T12:48:03.620Z (9 months ago)
- Language: Jupyter Notebook
- Size: 876 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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Table of Contents
## About The Project
[![Product Name Screen Shot][product-screenshot]](https://example.com)
Here's a blank template to get started: To avoid retyping too much info. Do a search and replace with your text editor for the following: `github_username`, `repo_name`, `twitter_handle`, `linkedin_username`, `email`, `email_client`, `project_title`, `project_description`
### Built With
* [Python](https://www.python.org/)
* [Pandas](https://pandas.pydata.org/)
* [Scikit-learn](https://scikit-learn.org/)
* [Numpy](https://numpy.org/)
* [Matplotlib](https://matplotlib.org/)
Project Organization
------------
├── LICENSE
├── Makefile <- Makefile with commands like `make data` or `make train`
├── README.md <- The top-level README for developers using this project.
├── data
│ ├── external <- Data from third party sources.
│ ├── interim <- Intermediate data that has been transformed.
│ ├── processed <- The final, canonical data sets for modeling.
│ └── raw <- The original, immutable data dump.
│
├── docs <- A default Sphinx project; see sphinx-doc.org for details
│
├── models <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
│ the creator's initials, and a short `-` delimited description, e.g.
│ `1.0-jqp-initial-data-exploration`.
│
├── references <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports <- Generated analysis as HTML, PDF, LaTeX, etc.
│ └── figures <- Generated graphics and figures to be used in reporting
│
├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt`
│
├── setup.py <- makes project pip installable (pip install -e .) so src can be imported
├── src <- Source code for use in this project.
│ ├── __init__.py <- Makes src a Python module
│ │
│ ├── data <- Scripts to download or generate data
│ │ └── make_dataset.py
│ │
│ ├── features <- Scripts to turn raw data into features for modeling
│ │ └── build_features.py
│ │
│ ├── models <- Scripts to train models and then use trained models to make
│ │ │ predictions
│ │ ├── predict_model.py
│ │ └── train_model.py
│ │
│ └── visualization <- Scripts to create exploratory and results oriented visualizations
│ └── visualize.py
│
└── tox.ini <- tox file with settings for running tox; see tox.readthedocs.io
## Getting Started
This is an example of how you may give instructions on setting up your project locally.
To get a local copy up and running follow these simple example steps.
### Prerequisites
This is an example of how to list things you need to use the software and how to install them.
* Pandas
```
pip install pandas
````
* Scikit-learn
```sh
pip install sklearn
```
* Matplotlib
```
pip install matplotlib
```
### Installation
1. Clone the repo
```sh
git clone https://github.com/github_username/repo_name.git
```
2. Install `requirements.txt`
```sh
pip install -r requirements.txt
```
## Usage
Use this space to show useful examples of how a project can be used. Additional screenshots, code examples and demos work well in this space. You may also link to more resources.
## Contributing
Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are **greatly appreciated**.
If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement".
Don't forget to give the project a star! Thanks again!
1. Fork the Project
2. Create your Feature Branch (`git checkout -b feature/AmazingFeature`)
3. Commit your Changes (`git commit -m 'Add some AmazingFeature'`)
4. Push to the Branch (`git push origin feature/AmazingFeature`)
5. Open a Pull Request
## License
Distributed under the MIT License. See `LICENSE.txt` for more information.
## Contact
Your Name - [@twitter_handle](https://twitter.com/twitter_handle) - email@email_client.com
Project Link: [https://github.com/github_username/repo_name](https://github.com/github_username/repo_name)
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[forks-shield]: https://img.shields.io/github/forks/github_username/repo_name.svg?style=for-the-badge
[forks-url]: https://github.com/github_username/repo_name/network/members
[stars-shield]: https://img.shields.io/github/stars/github_username/repo_name.svg?style=for-the-badge
[stars-url]: https://github.com/github_username/repo_name/stargazers
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[issues-url]: https://github.com/github_username/repo_name/issues
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[license-url]: https://github.com/github_username/repo_name/blob/master/LICENSE.txt
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[linkedin-url]: https://linkedin.com/in/linkedin_username
[product-screenshot]: images/screenshot.png
