MIT License

项目介绍

Aditya-Mankar/Diabetes-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 171 个 Star、83 次 Fork。

仓库全名
Aditya-Mankar/Diabetes-Prediction
开源许可证
MIT License
Star 数
171
Fork 数
83
未关闭 Issue
8

README 摘要

Diabetes Predictor Predict Diabetes using Machine Learning. In this project, our objective is to predict whether the patient has diabetes or not based on various features like Glucose level, Insulin, Age, BMI. We will perform all the steps from Data gathering to Model deployment. During Model evaluation, we compare various machine learning algorithms on the basis of accuracyscore metric and find the best one. Then…

完整内容见 GitHub 原始 README 或本页 。

常见问题

Diabetes-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 171 个 Star。

Diabetes-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 Diabetes-Prediction 的源码?

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

Diabetes-Prediction 最近还在维护吗?

仓库最近一次更新为 2023年5月2日,当前有 8 个未关闭 Issue;Star 数为 171,可作为社区活跃度的参考。

关于

Predict Diabetes using Machine Learning.

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
340
价值分
127
活跃状态
活跃

语言占比

CSS
HTML
Jupyter Notebook
Python

贡献者

README

Diabetes Predictor

Predict Diabetes using Machine Learning.

In this project, our objective is to predict whether the patient has diabetes or not based on various features like Glucose level, Insulin, Age, BMI. We will perform all the steps from Data gathering to Model deployment. During Model evaluation, we compare various machine learning algorithms on the basis of accuracy_score metric and find the best one. Then we create a web app using Flask which is a python micro framework.

Read more about it in my Blogpost.

Screenshot

Installation

  • Clone this repository and unzip it.

  • After downloading, cd into the flask directory.

  • Begin a new virtual environment with Python 3 and activate it.

  • Install the required packages using pip install -r requirements.txt

  • Execute the command: python app.py

  • Open http://127.0.0.1:5000/ in your browser.

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