Inspiration
What inspired our team was finding effective ways to validate or invalidate data based on the company's priorities.
What it does
Our tool takes mock "Hertz" customers' data and invalidates, creating a new file where all invalid entries are initialized to 0 or INV.
How we built it
We built it by generating a mock data set that contains values we expect Hertz customers to include. The data set would include invalid entries and duplicates. We used the pandas library to take any invalid data and set the value equal to 0 or INV. Then we exported the new csv file to include the invalid entries, allowing a person to see who and what data is missing from customers' bookings.
Challenges we ran into
Challenges we ran into included the fact that most of us were complete beginners and that we were not very familiar with the language we were using, Python.
Accomplishments that we're proud of
Accomplishments that we're proud of include our ability to quickly absorb information and apply what we learned effectively to complete our tasks.
What we learned
We learned how to use Python, work within a team to complete a task more efficiently, and manage our time effectively.
What's next for Data Validater
Graphs showing which car models are the most popular.
