tScanalytics

Project Objective

Using datasets from Tractor Supply Co. we faced a question: How could we modernize economic engagement with the market accounting for multiple factors & provide leeway for efficient supply chain?

We strove to answer this question through a predictive analysis based on machine learning in which would take data sets, analyze and predict based on variables given

Our specific project focuses on predicting sales of the product in consideration with variables such as date, state with neural networks that have accessed historical data.

Epistemological Process

It’s always important to think before doing or actualizing a program

Our orientation towards our project started with an epistemological question: How is the knowledge of our program going to affect the world around us, and what forms of methodology does it justify?

We wanted to make a project that could benefit society both economically and environmentally in an ethical fashion.

Tractor Supply Co. fell into our vision of ethicality, providing materials for those not just in rural communities, but allowing people to manage their agricultural resources effectively, thereby effectively allowing communities and agriculture to become more sustainable. We wanted to take it to the next step, making the supply chain more effective. This would, in theory, give people more jobs, give customers cheaper prices, allowing space for poverty alleviation and movements for economic growth.

Development Process

We realized that machine learning and deep learning could be effectively employed to develop a relationship between the predictor variables and the categorical output variable
There are only two predictors (state location and date of transaction), so we realized that there ideally should be a small number of layers and nodes and low general complexity
Realized that we could use multiple APIs to create an appealing and informative front-end interactive dashboard that draws data from trained learning model

Accessibility

Our project is not just limited to computers, but rather any device with access to the internet because most of the work is predicated on our server.

This means there can be "Analysis on the Go":

Any form of smart-device can access our project and analyze likely patterns of buying and selling because all the heavy calculations are done through our server.

This also means that we can pave the way for other forms of machine learning - these forms of analytical software grant people access to massive amounts of data

WolframAlpha is one example, but we can still apply this methodology to many other needs.

Procedural Possibilities for tScanalytics

There are innate advantages to analyzing and predicting trends through machine learning that can accelerate efficiency. Good predictions pave the leeway for many ethical benefits.
Good predictions make supply chain a lot easier. This means products are cheaper for consumers while companies can make more money and therefore offer more jobs. Lower prices and increase in the job market can play into effect for poverty alleviation.
Environmentalism and Sustainability through efficient production - less loss and less wasted due to incorrect predictions, as machine learning allows us to churn through a lot of data and come up with reasonable answers every time. Tractor Supply Co. supports the FFA and can take steps for innovation for environmental issues, and with this predictive learning, the future can be mapped for immeasurable amounts of innovations.

The focus of a project is not just what the project is by itself, but rather the methodology people approach it by.

Applicable Audiences

Our program is not just solely for Tractor Supply Co., because it is applicable to any field - from research to large or small businesses.

Predictions serve as a valuable asset to all companies, whether economically or productively

These forms of deep analysis can also perhaps help us measure structural inequality or other forms of violence with insight into numbers of how trends seem to be, with predictions of how they will continue to be contextualized in specific forms of policy and states of the economy.

The potential for efficiency and ethicality are limitless.