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:

• Name - Ganesh Chowdhary P
• Email - [email protected]

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