Gender Pay Gap Analysis and Prediction

Overview

This project aims to analyze the gender pay gap in a given dataset and develop predictive models to understand and predict pay disparities between genders. We will explore various aspects of pay, including Base Pay, Total Pay, and Bonus, to gain insights into the gender pay gap within different job titles.

Table of Contents

• Data
• Analysis
• Prediction
• Results
• Dependencies
• Usage
• Contributing
• License

Data

The dataset used for this analysis contains information about employees, their job titles, and pay components, such as Base Pay, Total Pay, and Bonus. The dataset is provided as a CSV file and is loaded for analysis.

Analysis

We perform exploratory data analysis (EDA) to understand the distribution of pay components among genders and job titles. We calculate and visualize the gender pay gap for each pay component and job title.

Prediction

In the prediction phase, we build machine learning models to predict pay disparities based on various features such as job title, experience, and education. We evaluate model performance and use the models to make predictions on pay gaps.

Results

The results of this analysis and prediction are presented in visualizations and summary statistics. We provide insights into the factors contributing to the gender pay gap and the effectiveness of our prediction models.

Dependencies

This project uses the following Python libraries:

• Pandas
• NumPy
• Matplotlib
• Plotly
• Scikit-Learn

You can install these libraries using the following command:

pip install pandas numpy matplotlib plotly scikit-learn

Usage

Clone the repository:

git clone https://github.com/034adarsh/Gender-Pay-Gap-Analysis-And-Prediction.git

Contributions

Contributions to this project are always welcome! If you find any issues or have ideas for improvements, please open an issue or submit a pull request.

Team Member

• Jaideep Singh Garlyal

License

This project is licensed under the MIT License.