Retirement planning is often overlooked until it's too late. The inspiration behind GoldenYears is to simplify the retirement planning process and make it less stressful and confusing for the end user.
What it does
GoldenYears simplifies the retirement planning process by helping the user gauge whether or not they are on track to retire when they want, how they want. GoldenYears utilizes data from the Social Security Administration’s life expectancy tables to estimate the user's life expectancy and find the years their retirement savings will need to cover. We used general rules of thumb, inspired by sites like Fidelity,to develop algorithms and gauge whether or not a user’s planned retirement savings and retirement age are realistic, and then provide the user with more realistic suggestions. Once the life expectancy is found, the difference between it and retirement age became the namesake “golden years,” or life after retirement. This was used to estimate the maximum expenditures a user could make after retirement, and for how many years they could spend this.
How we built it
The back-end functions of GoldenYear were created using Python and Pandas to read in Excel files, and the front-end was created using HTML, CSS, JQuery, Flask, Python, Visual Studio Code and Bootstrap. The guideline for retirement savings was gathered from researching best practices from multiple reputed sources like Fidelity, and ultimately a multiple of average salary was used along with the data from the Social Security Administration life expectancy databases.
Challenges we ran into
The major challenge for our team was creating an attractive and intuitive-to-use webpage. We had trouble with Flask API because it was the first time we were building a web application and we wasted a lot of time trying to choose what framework to use for it. Getting data from an HTML form and then trying to convert that data into python variables was also tough. User data is important, however, overly extensive information defeats the purpose of removing the stress from retirement planning. We had to have the right balance of level of detail to give personalized results, but not so specific that it would overwhelm the user.
What we learned
A lot. We worked as a partnership and only one of us came with a Computer Science background. It was great to learn from each other as one of us was a Finance and Math major so he helped with best practices, data collection, research, and building the algorithms. The other one of us helped bring it to life by making a web interface for it and converting the data to a UI. Almost all of what we did was new for us, especially creating a webpage and designing UX. Teaching ourselves Python and Pandas dataframes was also important.
What's next for GoldenYears
More financial data. Social Security benefits are variable from person to person, so a rough estimation had to be used due to time constraints. Compounding interest, investments, inflation, different types of retirement plans (ie 401k, Roth IRA), and other financial information would help make the outputs GoldenYears gives more useful to the user. Also more user information for life expectancy outside of just age and gender.