Car Price Evaluator

Inspiration

My car shopping experience last year, where many dealerships were marking up the prices of their cars. I thought: "I wonder if there was a way to determine whether I am actually overpaying without bias?"

What it does

This program tells consumers whether or not they are getting price gouged by dealerships based on the MSRP (Manufacturer's Suggested Retail Price).

How we built it

We used Python and Google Sheets API to read and write data with spreadsheets. This data consists of more than 2600 unique cars (each having make, model and price and some having trims) for sale in the U.S. in 2023.

Challenges we ran into

We had difficulty in determining the user input, since the dataset imported had most cars with trim-level data and few that didn't. A car's trim-level would also change the MSRP of the car, as the higher the trim-level, the higher the MSRP. Therefore, there would be confusion as to whether or not the consumer has to enter trim-level data.

Accomplishments that we're proud of

• Our program compares user input to a huge dataset with over 2600 elements.
• We found a dataset that covers all the new cars listed for sale in the U.S. in 2023.

What we learned

• Using GoogleAPI to read and write data from spreadsheets
• Python syntax
• Set up code-environment

What's next for Car Price Evaluator

• We desire much more accuracy, where all of the cars in the dataset have trim-levels and users can enter in trim-level data for all cars.
• We want to create a more advanced UI and make this into a website.

Built With

• google
• python
• visual-studio-code