The Pay Equity Analysis Tool is a software designed to analyze payroll data sets spanning five years for any company, including the current year. It provides insightful visualizations and statistical analysis to help those companies understand, make informed decisions and address pay disparities within the organization. This not only fosters a fair workplace culture but also encourages the participation of all, regardless of their gender which positively impacts our society.
Analyzes job distribution, pay distribution (displayed as a histogram), and various factors influencing pay such as gender, age, experience, performance score, and education.
Utilizes scatter plots to compare pay differentials between males and females when compared based on various aspects. Displays estimated pay gap percentage for the current year.
Provides visualizations comparing pay gap differences between the current and previous years.
Utilizes linear regression models to predict future pay gap trends based on historical data.
The data set has been taken from glassdoor and focuses on income for various job titles based on gender. As there have been many studies showcasing that women are paid less than men for the same job titles, this data set will be helpful in identifying the depth of the gender-based pay gap. The features of the data set to be uploaded are:
Job Title : Depending on the company
Gender : Male/ Female
Age : from 18 onwards
PerfEval : Performance evaluation (ranges from 1 to 5)
Education : Highest degree of education (High School, College, Masters, PhD)
Dept : Departments available in the company
Seniority : Seniority level (ranges from 1-5)
Base Pay : Basic salary received
Bonus : Extra salary received
Input - Upload payroll datasets for the current and past four years.
Analysis - Run the software to generate insights and visualizations.
Interpretation - Gain valuable insights into pay disparities and trends within the organization.
Action - Utilize findings to implement strategies for addressing and mitigating pay inequalities.
Pandas
Numpy
Plotly
Matplotlib
Seaborn
Scikit-learn
Statsmodels
Gradio
There are two .ipynb files:
payEquitySoftware.ipynb :
Contains full project code including gradio module for user interface. The code for linear regression model (Pay gap trend prediction) utilises sklearn library only and not the one provided by OneAPI (sklearnex)
RegressionModelOneAPI .ipynb:
Contains code for the linear regression model that has been employed for predicting the trend of pay gap using the extension for sklearn (sklearnex) provided by OneAPI.
The following graph depicts the comparison of pay gap on the basis of performance level
Here, the blue bars represent the positive value of pay gap, which means, on an average, men earn more than women for that year whereas some of the coral coloured bars represent the negative value of pay gap indicating that women earn more than men on the basis of performance level.
Vidisha Desai (@VidishaDesai)
Lavanya Vasudevan (@LaviVasudevan)
Aashika Jetti