Pay Gap Estimator

Inspiration

Even in the 21st century, women still face pay inequality compared to their male counterparts. It's an issue that impacts countless women globally. Women, who have the same education, job title, and experience as men, still face pay disparities.

What it does

It is a web application that not only predicts expected salaries but also exposes the persistent pay gap. It does so by considering key factors such as age, education level, job title, and years of experience for both men and women.

How we built it

We utilized Scikit-learn to create a regression model that predicts salaries using other features using RandomForestRegressor, and we obtained our salary dataset from Kaggle. With the help of Flask and Pickle, we connected the model to our web application.

Challenges we ran into

We were new to the tools we utilized, especially Scikit-learn and Flask. Combining the HTML website page and the machine learning program was also challenging.

Accomplishments that we're proud of

In the end, everything came together. We are proud of building a website that effectively allows users to interact with a big data set in a way that can help women in the real workplace.

What we learned

We learned how to implement machine learning with a user-friendly website. We learned how to use Kaggle, Flask, Scikit-learn, and Pickle. We also got to practice with pandas, numpy, Matlablib, etc. But most importantly we learned that the gender pay gap is real and the significance of salary negotiations in real life.

What's next for Pay Gap Estimator

We can add more factors for a more accurate prediction of the salary and the pay gap estimates. We can also develop this website so that it can update with more data automatically and periodically.