[45] - Farming Profit Prediction

Inspiration

Our inspiration for this project was when we realized that there was no way to compare the costs of growing different crops easily in one simple place, making it more difficult for new farmers to start up.

What it does

It calculates and compares the predicted profits and revenue for different crops based on the state you live in. It also has the ability to give you a comparison bar chart that compares the profits between the other crops.

How we built it

We built this through streamlit and used numpy, pandas, matplotlib. We used all of these libraries through streamlit to create an interactive site. These tools allow the user to customize the graphs and give them the ability to adjust the numbers to make.

Challenges we ran into

In the beginning we struggled to figure out what we wanted to use for our front end and backend. We wanted to find a way to connect what we were doing for our frontend with our python in the backend. We also ran into many problems when it came to our github branches. We struggled to deal with merge conflicts leading to the delay of our finished product. We also ran into many problems with pip, which we had to deal with as we went. The actual programming went very smoothly.

Accomplishments that we're proud of

We are proud of the many things that we have accomplished. Some of these include a calculator that takes fertilizer cost, pesticide cost, land cost, and tool cost. I am proud of the linear regressions we were able to accomplish in order to predict the yield of certain crop.

What we learned

We learned many things including the importance of planning and preparation. Our team spent hours figuring out how we were going to accomplish our tasks and then downloading the proper IDE and packages. We learned many different python libraries and learned how to use git as a team without causing merge conflicts.

What's next for Farming Profit Prediction

We can now expand this to more states and crops. We can also add many new features and visualizations to offer farmers a more comprehensive data tool. We hope to one day convert this to an app.