Practical Algorithms : Linear Regression

Scraping data using puppeteer and building a linear regression model
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Table of Contents

1. About The Project
   • Built With
2. Getting Started
   • Prerequisites
   • Installation
3. Roadmap
4. Contributing
5. License
6. Contact
7. Acknowledgments

About The Project

During the session we will cover how to scrape data from websites using a practical example. We will then use the data to model a linear regression algorithm to help predict the price of a car given several attributes. The session will flow as follows:

• A background on what machine learning is

• How linear regression works

• Web scraping using a headless browser (puppeteer)

• Concurrency when scraping to speed up the process.

• Exporting the data

• Importing the data in our Jupyter notebook

• Data cleaning

• Data exploration

• Data modeling

• Challenge

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Built With

To ensure you gain the most out of this session please make sure you have the following installed:

• Node js

• Python

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Getting Started

To run the webscrapper

Prerequisites

• Node js

• Python

Installation

1. Clone the repo

   git clone https://github.com/wamaithanyamu/SBT-Japan-linear-regression-model.git

2. Install NPM packages

   npm install

3. Run the webscrapping script

   node webScraper.js

4. Run the jupyter notebook using anaconda or upload it to google collab or kaggle.
5. Use the data final.json to run the notebook

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Roadmap

• Web scrapping ✅
• Data cleaning ✅
• EDA ✅
• Build Linear regression model ✅
  • Hyper parameter tuning
• Serve model
  • Build API
  • Frontend

See the open issues for a full list of proposed features (and known issues).

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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 MIT License. See LICENSE.txt for more information.

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Contact

Tweet me at- @wamaithanyamu or shoot me an email at hello@wamaithanyamu.com

Project Link: https://github.com/wamaithanyamu/SBT-Japan-linear-regression-model

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Acknowledgments

• Lux Academy
• DataScience East Africa

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