/Salary-Prediction

predicts annual salary

Primary LanguageJupyter Notebook

Salary Prediction ML Project

Overview

This project aims to predict salary based on various features using a Machine Learning model. It utilizes a Random Forest Regressor trained on a dataset containing over 10,000 data points. The app.py file serves as the main entry point for accessing the prediction model.

Project Structure

The project structure is organized as follows:

- data/                # Folder containing the dataset
    - salary_dataset.csv   # Dataset used for training and testing
- models/              # Folder containing trained model(s)
    - random_forest_regressor.pkl # Pre-trained Random Forest Regressor model
- app.py                # Main application file for prediction
- requirements.txt      # List of project dependencies
- README.md             # Documentation (You are reading this!)
- EDA_SalaryModel.ipynb
- Salary_prediction.ipynb

Getting Started

  1. Clone the Repository:

    git clone https://github.com/chandankr014/Salary-Prediction.git

  2. Install Dependencies:

    pip install -r requirements.txt

  3. Run the Application:

    To start the salary prediction application, use the following command:

    python app.py

  4. Access the Application:

    Open a web browser and navigate to http://localhost:5000 to access the salary prediction interface.

Usage

  1. Input Data:

    Provide the necessary input features such as years of experience, min salary, maximum salary, etc., in the provided input fields.

  2. Predict Salary:

    Click the "Predict" button to generate a salary prediction based on the input data.

  3. View Result:

    The predicted salary will be displayed on the screen.

Model Retraining

If you wish to retrain the model with updated data, follow these steps:

  1. Place the updated dataset in the csv files.

  2. Modify the data loading and preprocessing steps in the app.py file to use the new dataset.

  3. Retrain the model using the updated data.

  4. Save the trained model as random_forest_regressor.pkl in the models/ directory.

Dependencies

  • Python 3.x
  • scikit-learn
  • Flask (for the web application)

License

This project is licensed under the MIT License - see the LICENSE file for details.

Author