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Health Check ✔ is a Machine Learning Web Application made using Flask that can predict mainly three diseases i.e. Diabetes, Heart Disease, and Cancer.

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Health Check ✔

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👀 Introduction

This is a Machine Learning Web Application which can predict three diseases i.e. Diabetes, Heart disease and Cancer. The datasets have been taken from Kaggle. And overall analysis and model building for the diseases have been done in respective Jupyter Notebooks.

👩‍💻 Technology Stack

Tools

🚀 Quick Start :

Step 1: Forking the repository :

To work on an open-source project, you will first need to make your copy of the repository. To do this, you should fork the repository and then clone it so that you have a local working copy.

Get your own Fork/Copy of repository by clicking Fork button right upper corner.

Step 2: Clone the Forked Repository

After the repository is forked, you can now clone it so that you have a local working copy of the codebase.

To make your local copy of the repository follow the steps:

  • Open the Command Prompt
  • Type this command:
For Flask:
$ git clone https://github.com/<your-github-username>/HealthCheck
For Flutter App Develpment:
$ git clone -b flutter https://github.com/<your-github-username>/HealthCheck

Step 3: Creating a new branch (IMP)

This is one of the very important step that you should follow to contribute in Open Source. A branch helps to manage the workflow, isolate your code and does not creates a mess. To create a new branch:

$ git branch <name_of_branch>
$ git checkout <name_of_branch>

Imp. Setting up upstream:

git remote add upstream https://github.com/kritikaparmar-programmer/HealthCheck
git remote -v

Keep your cloned repo upto date by pulling from upstream (this will also avoid any merge conflicts while committing new changes)

git pull upstream main

Step 4: Setting up Project

For Flask:

1. Create a Virtual Environment

  • On macOS and Linux:
      python3 -m venv env
  • Windows
      py -m venv env

2. Activate the Virtual Environment

  • On Windows
    .\env\Scripts\activate
  • On macOS and Linux:
    source env/bin/activate

3. Install dependencies using

pip install -r requirements.txt

4. Run server using

flask run

5. Go to http://127.0.0.1:5000/ and enjoy the application.

For Flutter:

1. Open Android Studio or Visual Studio Code.

2. Click Open Folder option in the File section and Navigate to ./HealthCheck/HealthCheck_App and select it.

3. Open Terminal in the editor.

$ flutter packages get

Step 5: Contribute

Make relevant changes according to the issue that you were assigned on. Contribute in any way you feel like :)

Step 6: Commiting and Pushing

Once you have modified an existing file or added a new file to the project, you can add it to your local repository, which we can do with the git add command.

git add .

With our file staged, we’ll want to record the changes that we made to the repository with the git commit command.

The commit message is an important aspect of your code contribution; it helps the other contributors fully understand the change you have made, why you made it, and how significant it is.

git commit -m "useful commit message"

At this point you can use the git push command to push the changes to the current branch of your forked repository:

git push origin <branch-name>

Step 7: Create Pull Request

Now, you are ready to make a pull request to the original repository.

You should navigate to your forked repository, and press the "Compare & pull request" button on the page.

GitHub will alert you that you can merge the two branches because there is no competing code. You should add in a title, a comment, and then press the “Create pull request” button.

⚙ Contributing Guidelines

Please go through the Contributing guidelines here.

📖 Code Of Conduct

You can find the Code of Conduct here.

📜 LICENSE

GitHub license

Credit goes to these people: ✨

Open Source Programs the project have been associated with

GSSOC

Drop a ⭐ on the Github Repository if you like it.

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Health Check ✔ is a Machine Learning Web Application made using Flask that can predict mainly three diseases i.e. Diabetes, Heart Disease, and Cancer.

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