Here's a README template for your salary prediction project:
This is a Flask web application for predicting salaries based on years of experience using a trained machine learning model.
-
Clone the repository to your local machine:
git clone https://github.com/your-username/salary_prediction_app.git
-
Navigate to the project directory:
cd salary_prediction_app
-
Set up a virtual environment (optional but recommended):
python3 -m venv venv
-
Activate the virtual environment:
-
On Windows:
venv\Scripts\activate
-
On macOS and Linux:
source venv/bin/activate
-
-
Install the required dependencies:
pip install -r requirements.txt
-
Ensure that you have your trained machine learning model saved as
model.pkl
in theapp/
directory. -
Run the Flask app:
python run.py
-
Access the app in your web browser at http://127.0.0.1:5000/.
-
Enter the years of experience in the input field and click the "Predict" button to get the predicted salary.
- GET /: Home page of the web application.
- POST /predict: Endpoint for salary prediction.
- Request:
- Method: POST
- Content-Type: application/x-www-form-urlencoded
- Body:
experience=<years_of_experience>
- Response:
- Content-Type: text/html
- Body: HTML page with the predicted salary.
- Request:
salary_prediction_app/
│
├── app/
│ ├── __init__.py
│ ├── model.pkl
│ ├── routes.py
│ └── static/
│ └── styles.css
│ └── templates/
│ └── index.html
│
├── venv/
│
├── requirements.txt
└── run.py
- app/: Directory containing the Flask application.
- model.pkl: Trained machine learning model file.
- routes.py: Flask routes for the web application.
- static/: Static files (e.g., CSS).
- styles.css: CSS file for styling.
- templates/: HTML templates for the web pages.
- index.html: HTML template for the UI.
- venv/: Virtual environment directory.
- requirements.txt: File containing all Python dependencies.
- run.py: Script to run the Flask app.
Feel free to customize this README template according to your project's specific details and requirements!