TouchBistro Inflation Watch

Inspiration

What it does

This project provides insights into how inflation affects the restaurants.

How we built it

I built it with Dask so that my gaming laptop wouldn't overheat more than it already does. I additionally used the typical Pandas and Matplotlib manipulate and visualize the dataset.

Challenges we ran into

The first main challenge was getting into exploring the dataset, as there was so many variables and things to account for, it was incredibly daunting. This is why the best thing to do when it comes to projects with seemingly massive amount of breadth is to just started, and work your way through it, like a diagonally cut sanwich (I have not slept).

The second main challenge was actually exploring the dataset, as there were so many factors to account for, such as what should I remove? What algorithm should I use to remove the outliers? What should I keep?

The last main challenge was learning times series, as I knew little about it before this project, and so I spent alot of time understanding the different techniques and why they used them.

Accomplishments that I'm proud of

The biggest accomplishment I was proud of was getting through an entire dataset from exploring and cleaning it to actually making insights. This is probably my first time doing it from complete scratch like this.

What I learned

I learned a whole lot, exploring, cleaning, and especially about time series data!

What's next for TouchBistro Inflation Watch :Smiling Face with Horns:

I think next, I want to try tackling classification data, as that's still a pretty big grey box for me.

Built With

• dask
• matplotlib
• pandas
• statsmodels