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Software Developer Salary Predictor

Overview

This project is a web application that predicts the salary of software developers based on their country, education level, and years of experience. The application uses a Random Forest model trained on data from the Stack Overflow Developer Survey 2023.

Project Structure

  • app.py: The main entry point of the application, where users can choose to explore data or predict salaries.
  • predict_page.py: Contains the functionality to gather user input and predict salaries.
  • explore_page.py: Provides an interactive exploration of the salary data.
  • Salary_prediction.ipynb: A Jupyter Notebook used for data analysis, feature engineering, and model training.
  • Salary_Prediction/models/: Directory where the trained model and encoders are saved.

Features

  • Salary Prediction: Predicts the salary of a software developer based on their country, education level, and years of experience.
  • Data Exploration: Visualizes salary data by country and experience level, using charts like bar charts, line charts, and pie charts.

Getting Started

Prerequisites

  • Python 3.8 or higher
  • Required Python libraries listed in requirements.txt

Installation

  1. Clone the repository:

    git clone https://github.com/Ganesh2409/Salary_Prediction.git
cd Salary_Prediction

  2. Install the required packages:

    pip install -r requirements.txt

  3. Run the application:

    streamlit run app.py

Usage

  1. Predict Salary: Navigate to the "Predict" page, select your country, education level, and years of experience, then click "Calculate Salary". The application will display the predicted salary.

  2. Explore Data: On the "Explore" page, you can visualize salary distributions by country and experience.

Model Information

  • Data: The model is trained on data from the Stack Overflow Developer Survey 2023.
  • Algorithms: The application uses a Random Forest Regressor to predict salaries.
  • Preprocessing:
    • Label Encoding: For the country column.
    • Ordinal Encoding: For the education level column.

Model Improvement:

  • Try Different Algorithms: Experiment with other machine learning models like Gradient Boosting, XGBoost, or Neural Networks to see if they offer better performance than Random Forest.
  • Hyperparameter Tuning: Perform more advanced hyperparameter tuning using techniques like Bayesian optimization or Genetic Algorithms to further improve the model's accuracy.

Contributing

Contributions are welcome! Please fork this repository and submit a pull request with your changes.

Contact

For any questions or feedback, please contact:

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