BetterShopping

Inspiration

We wanted to optimize grocery store shopping for the university student faced with a tight budget.

What it does

The user inputs the items they want to buy, as well as the item's price and the utility. Based on the Cobb Douglas utility function, we generate a utility function modeling the grocery items, taking the form U = x^util1 * y^util2 * z^util3, and so on. Then we take the gradient of the function and equate MUi/Pi for each variable (MUx/Px = MUy/Py = MUz/Pz=and so on). We then input the solutions derived above into the budget constraint. Ultimately, the program outputs how much the user should buy of each item based on the utility to the user.

How we built it

We used sympy to generate the utility function and used sympy's diff to take the partial derivatives of utility function for n variables. Then we used sympy's solve to equate the marginal utility/item price for each of the variables input by the user.

Challenges we ran into

We weren't certain how to go about generated the utility function for a variable number of items n, so we decided to set the grocery items to 10. We also faced some challenges importing Sympy. Although our program isn't functional, we learned some valuable information about doing symbolic math in Python.