MIT License

项目介绍

SahilChachra/Medical-Cost-Prediction 是一个托管在 GitHub 上的开源项目,主要使用 Jupyter Notebook 编写,采用 MIT License 许可证,目前获得 96 个 Star、46 次 Fork。

仓库全名
SahilChachra/Medical-Cost-Prediction
开源许可证
MIT License
Star 数
96
Fork 数
46
未关闭 Issue
8

README 摘要

Medical Insurance Cost Prediction Medical Insurance Cost Prediction using Random Forest Regressor.                                     :innocent: Motivation To predict things have been never so easy. I used to wonder how Insurance amount is…

完整内容见 GitHub 原始 README 或本页 。

常见问题

Medical-Cost-Prediction 用什么语言开发?

主要使用 Jupyter Notebook 编写,采用 MIT License 许可证,目前有 96 个 Star。

Medical-Cost-Prediction 可以免费商用吗?

仓库采用 MIT License 许可证,可免费使用与修改,商业使用请遵守许可证条款(例如保留版权声明与许可声明)。

如何获取 Medical-Cost-Prediction 的源码?

可以直接克隆仓库:git clone https://github.com/SahilChachra/Medical-Cost-Prediction.git,默认分支为 master;安装与运行步骤见仓库 README。

Medical-Cost-Prediction 最近还在维护吗?

仓库最近一次更新为 2024年8月25日,当前有 8 个未关闭 Issue;Star 数为 96,可作为社区活跃度的参考。

关于

Predict your Medical insurance cost!

描述取自 GitHub 官方 API,中文内容由 GitRead 中文社区 编辑团队整理。

简要分析

热度分
189
价值分
71
活跃状态
活跃

语言占比

HTML
Jupyter Notebook
Procfile
Python

README

Medical Insurance Cost Prediction

Medical Insurance Cost Prediction using Random Forest Regressor.

                                   

:innocent: Motivation

To predict things have been never so easy. I used to wonder how Insurance amount is charged normally. So, in the mean time I came across this dataset and thought of working on it! Using this I wanted to know how few features determine our insurance amount!

:hourglass: Some Screenshots

:star: Features

  1. Exploring the dataset
  2. Converting Categorical values to Numerical
  3. Plotting Heatmap to see dependency of Dependent valeu on Independent features
  4. Data Visualization (Plots of feature vs feature)
  5. Plotting Skew and Kurtosis
  6. Data Preparation
  7. Prediction using Linear Regression
  8. Prediction using SVR
  9. Prediction using Ridge Regressor
  10. Prediction using Random Forest Regressor
  11. Performing Hyper tuning for above mentioned models
  12. Plotting Graph for all Models to compare performance
  13. Preparing model for deployment
  14. Deployed model using Flask

:key: Results

Model gave 86% accuracy for Medical Insurance Amount Prediction using Random Forest Regressor

:file_folder: Dataset

The dataset used can be downloaded here (Kaggle) - Click to Download

:heart: Owner

Made with :heart:  by Sahil Chachra

:eyes: License

MIT © Sahil Chachra

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