A breif description of my senior project.
I work for a solar and energy efficiency company called Shine Solar. We strive to provide a world class user experience to those that interact with our products. Shine provides solar energy, energy efficiency, HVAC, HVAC Preventative Maintenance services, and batteries to the greater Arkansas, Oklahoma, Missouri, and Tennessee area. The main office is located in Northern Arkansas, but we have offices in Rexburg and Utah as well. I work on the Software Development team that is based Rexburg as a data scientist. They initially hired me on as an intern, but I have since been extended a full time position so WHOO!
Way back in February, my manager, we shall call him Mr. Pickett, talked to me about a new assignment he wanted me to work on. He wanted to know if I knew a way to predict which leads, a person talking to sales representatives that is not a customer, will turn into a customer. And me, having just started my machine learning class, was more than HAPPY to indulge him in this new project. So I went to work!
I AM SO GLAD YOU ASKED
I mean… it is just my name for the machine learning model I created… nothing special.
BUT
There is a lot that went into it and more in depth descriptions will be on other pages but here is the TL;DR of my project.
I need to take data about leads; living area, amount of time talking to reps, the electric company they are under, how many appointments they set up with a sales rep, etc. Once I have a neat little (or large) dataset of usable lead data, I will run that through a predictive model and output an array of numbers, from \(0\) to \(1\), giving a percentage chance on how likely a certain lead will become a customer.
Now that you have the basic gist of my project you have enough background to understand some of the vernacular I will be using in my more in depth blog posts.
Read on, dear viewer. Read on.
print("One last thing. This project is done 95% in Python, so, yay.")One last thing. This project is done 95% in Python, so, yay.