Data Science Salary Guide Prediction

Overview

Data Science Salary Guide Prediction is a project designed to predict salaries for data science and related roles in the year 2025. It uses six robust models to calculate salary projections, including:

1. BERT (Bidirectional Encoder Representations from Transformers) - Extracts structured information (roles, levels, and salary ranges) from unstructured job descriptions.
2. CAGR (Compound Annual Growth Rate) - Predicts steady growth in salaries based on historical data.
3. Inflation Adjustment Model - Accounts for changes in purchasing power over time.
4. Skills Premium Weighting Model - Adds salary adjustments based on high-demand skills (e.g., Python, SQL, Machine Learning).
5. Demand and Geographic Growth Model - Factors in job market demand and regional salary variations.
6. Weighted Regression Model - Refines predictions using statistical analysis based on experience, skills, and location.

This repository provides implementations in Python, JavaScript, and Java, each placed in dedicated folders.

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Project Structure

data-science-salary-guide-prediction/
├── python/                 # Python implementation
│   └── main.py             # Python code for salary prediction
├── javascript/             # JavaScript implementation
│   └── main.js             # JavaScript code for salary prediction
├── java/                   # Java implementation
│   └── Main.java           # Java code for salary prediction
└── README.md               # Project documentation

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Installation and Usage

Python

1. Navigate to the python folder:

   cd python

2. Install dependencies:

   pip install -r requirements.txt

3. Run the script:

   python main.py

JavaScript

1. Navigate to the javascript folder:

   cd javascript

2. Install dependencies:

   npm install

3. Run the script:

   node main.js

Java

1. Navigate to the java folder:

   cd java

2. Compile the Java program:

   javac Main.java

3. Run the program:

   java Main

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Features

• Predicts salaries for entry-level, mid-level, and senior-level roles.
• Integrates multiple models for highly accurate predictions.
• Fetches real-time data from APIs (when available).
• Multi-language support for accessibility across platforms.

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Technologies Used

• Python: For advanced modeling and data analysis.
• JavaScript: For web-based or server-side implementations.
• Java: For enterprise-level applications.

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Contributing

Contributions are welcome! Please follow these steps:

1. Fork the repository.
2. Create a new branch:

   git checkout -b feature-name

3. Commit your changes:

   git commit -m "Added feature-name"

4. Push to your branch:

   git push origin feature-name

5. Open a pull request.

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License

This project is licensed under the MIT License. See LICENSE for details.

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Contact

For questions or feedback, please open an issue or reach out via email at [[email protected]].