/seattle-house-price-predictor

Welcome to the King County House Sale Prices Analysis project! This repository is designed to guide you through a comprehensive exploration of house sale prices data for King County, which includes Seattle. The dataset spans one year, from May 2014 to May 2015.

Primary LanguageJupyter NotebookApache License 2.0Apache-2.0


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King County´s Real State: Price Predictor

Welcome to the King County House Sale Prices Analysis project!
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Table of Contents
  1. About The Project
  2. Getting Started
  3. Usage
  4. License
  5. Contact
  6. Acknowledgments

About The Project

Welcome to the King County House Sale Prices Analysis project! This repository is designed to guide you through a comprehensive exploration of house sale prices data for King County, which includes Seattle. The dataset spans one year, from May 2014 to May 2015.

Mission Statement:

Our mission is to embark on an analytical journey through the housing market of King County. By delving into this dataset, we aim to enhance our Python programming skills, deepen our understanding of real estate financial data, and refine our analytical prowess.

Project Goals:

Explore and analyze the trends and patterns in house sale prices data. Perform data loading, preprocessing, and visualization to gain insights. Calculate returns and conduct portfolio analysis tailored to the real estate domain. Develop Python coding skills and apply them in a practical, real-world context.

Are You Ready to Get Started? Prepare yourself to dive into the rich world of real estate data analysis! Let's equip ourselves with the necessary tools and techniques to navigate through this intriguing dataset and extract meaningful insights about the housing market in King County.

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Installation

You may find the following Python libraries useful for data analysis, visualization, and basic machine learning:

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Installation

Below is an example of how you can instruct your audience on installing and setting up your app. This template doesn't rely on any external dependencies or services.

  1. Download King_count_houses repository https://www.kaggle.com/datasets/minasameh55/king-country-houses-aa

  2. Clone the repo

    git clone https://github.com/yourusername/king-county-house-sale-prices-analysis.git

  3. Install NPM packages

    npm install

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Usage

Heatmap

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Option 1:
Multicolinearidad = sqt_above, grade, sqt_living Unnecesary = condition, yr_built, long

Option 2: Multicolinearidad = sqft_living15 Unnecesary = yr_built

Option 3 (Change and add): Return sqt_above, pull out sqt_living and sqt_living15 Unnecesary = zipcode

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Roadmap

  • This section is underconstruction.

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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

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License

Distributed under the Apache 2.0 License. See LICENSE.txt for more information.

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Contact

Feel free to reach out to me via email, LinkedIn, or Reddit for any inquiries, contributions to projects, or potential job opportunities:

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