GithubHelp home page GithubHelp logo

mohammadmoradpoor / insuranceaccidentestimate Goto Github PK

View Code? Open in Web Editor NEW
3.0 1.0 0.0 87 KB

๐Ÿš—๐Ÿ’ฅ Accident damage estimation for insurance companies using the CatBoost algorithm. ๐Ÿ“Š๐Ÿ“ˆ This advanced technique utilizes machine learning to provide accurate estimates for assessing damages caused by accidents. ๐Ÿ’ฏ๐Ÿ’ผ Enhance efficiency and accuracy in insurance claim processes with the power of CatBoost! ๐Ÿค–๐Ÿ“‰

Jupyter Notebook 100.00%
catboost dataanalysis datascience jupyternotebook machinelearning python accidentdamageestimation regressionmodel

insuranceaccidentestimate's Introduction

InsuranceAccidentEstimate

InsuranceAccidentEstimate is a repository that contains a Python notebook named CatBoostInsuranceRegression.ipynb. This notebook solves the problem of estimating accident damages for an insurance company using the CatBoost algorithm.

Introduction

Accurate estimation of accident damages is crucial for insurance companies to assess claims and determine appropriate coverage. The CatBoostInsuranceRegression.ipynb notebook provides a solution to this problem by leveraging the CatBoost algorithm, which is a gradient boosting framework.

Installation & Usage

To use the CatBoostInsuranceRegression.ipynb notebook, follow these steps:

  1. Clone the repository to your local machine:

    git clone https://github.com/MohamadsalehMoradpoor/InsuranceAccidentEstimate.git
  2. Install the required dependencies:

    pip install -r requirements.txt
  3. Launch Jupyter Notebook:

    jupyter notebook
  4. In the Jupyter Notebook interface, open the CatBoostInsuranceRegression.ipynb notebook.

  5. Follow the instructions provided in the notebook to execute the code cells and run the CatBoost regression model for accident damage estimation.

Note: Make sure you have the required dataset or data files available in the appropriate location. The notebook may expect specific data files or data preprocessing steps. Adjust the code accordingly to fit your dataset and requirements.

Contributing

If you'd like to contribute to this repository, you can follow these steps:

  1. Fork the repository on GitHub.
  2. Clone the forked repository to your local machine.
  3. Create a new branch and make your modifications.
  4. Commit and push your changes to your forked repository.
  5. Submit a pull request, describing your changes and the motivation behind them.

Contact

If you have any questions or suggestions regarding this repository, please feel free to contact the repository owner.

Thank you for using the InsuranceAccidentEstimate repository! We hope it proves to be useful for your insurance estimation needs.

insuranceaccidentestimate's People

Contributors

mohammadmoradpoor avatar

Stargazers

 avatar  avatar  avatar

Watchers

 avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.