Everyone wants to save money, but we hardly take a scientific approach to it. Do you spend more or less than the average person in your demographic on your cell phone? How about clothing? How about food? Where are we doing well in finding deals and where are we getting ripped off? In any other area of life, we would take a scientific approach to solving it. Why would be not take such an approach here?

What it does

Spend Analytic takes your transaction data from your credit and debit cards and compares it to the data provided by other users. It then provides you with how much you are spending in any given category relative to others with a similar demographic profile as well as the dataset as a whole. From that, it generates a list of potential recommendations for savings.

How I built it

Spend Analytics is built using Flask and Python, with the intention of integrating various Python data analytics tools into the backend.

Challenges I ran into

I spent a lot of time coding and then realized that I could not do much data analysis as I only had my own data to work with. That turned the focus more to the presentation of the idea rather than the actual construction as that become somewhat impossible. Reference data for how much any group generally spends on X was difficult to find.

What's next for SpendAnalytic

This is a project with substantial potential real world application. I am not sure what I will do with it next.

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