# README
Linear Regression App with Go
Table of Contents
General Info
This project exists because I felt like smashing ML, backend, and software development into one messy repository. The project is split into two main sections, and guess what, each of those is split again. Brilliant, right?
First Section
Linear Regression Algorithm
The logic for this algorithm came straight out of the book "Applied Regression Analysis: A Research Tool, Second Edition" by John O. Rawlings, Sastry G. Pantula, and David A. Dickey. I translated it into Golang code because, well, why the hell not? Everything you need to know is in the first chapter, so just get on with it.
Read the CSV
The data I'm serving in this application is some stuff I downloaded from Kaggle. You can swap out the CSVs with whatever floats your boat. Reading these CSVs is dead simple—I skimmed the documentation and asked ChatGPT a couple of questions. Easy peasy.
Second Section
Backend
The backend is dead simple, too. There's a server document that kicks things off, and three handlers (only two are actually necessary) doing all the logic gymnastics with the data in the 'DB'. Check out the required JSON for the backend dance:
/predict/candy
REQUEST
{
"chocolate" uint
"fruity" uint
"caramel" uint
"peanutyalmondy" uint
"nougat" uint
"crispedricewafer" uint
"hard" uint
"bar" uint
"pluribus" uint
"sugarpercent" float64
"pricepercent" float64
}
RESPONSE (Predict)
{
"winpercent" float64
}
/predict/heart
REQUEST
{
"age" uint
"sex" uint
"cp" uint
"trestbps" uint
"chol" uint
"fbs" bool
"restecg" uint
"thalach" uint
"exang" uint
"oldpeak" float64
"slope" uint
"ca" uint
"thal" uint
}
RESPONSE (Predict)
{
"target" uint
}
/predict/inurance
REQUEST
{
"age" uint
"bmi" float64
"children" uint
}
RESPONSE (Predict)
{
"charges" float64
}
/train/candy
REQUEST
{
"chocolate" uint
"fruity" uint
"caramel" uint
"peanutyalmondy" uint
"nougat" uint
"crispedricewafer" uint
"hard" uint
"bar" uint
"pluribus" uint
"sugarpercent" float64
"pricepercent" float64
"winpercent" float64
}
/train/heart
REQUEST
{
"age" uint
"sex" uint
"cp" uint
"trestbps" uint
"chol" uint
"fbs" bool
"restecg" uint
"thalach" uint
"exang" uint
"oldpeak" float64
"slope" uint
"ca" uint
"thal" uint
"target" uint
}
/train/inurance
REQUEST
{
"age" uint
"bmi" float64
"children" uint
"charges" float64
}
Database
The database is dirt poor—three CSV files with the Intercept taking the lead. It's always '[0]', followed by the coefficients. I did it because I'm all about keeping things simple and efficient.
Some Things to Add
I reckon there are two main things to tack on here. Firstly, a specialized DB for ML would be nifty. Secondly, and perhaps more importantly, an authentication and authorization protocol. Picture a world where only the cool kids with authenticated passes can ask for predictions, and the authorized folks get to scribble in the 'DB'. I didn't add it, not because I couldn't, but simply because I couldn't be bothered.
Execution Instructions
Here's a step-by-step guide to running this application:
Prerequisites
Make sure you have Go installed on your system. You can get it here.
Installing Dependencies
-
Clone this repository:
git clone https://github.com/F-Dupraz/LinearRegressionApp.git cd LinearRegressionApp -
Install any necessary dependencies (if applicable):
# Example: Installing Go packages github.com/gorilla/mux v1.8.1 github.com/joho/godotenv v1.5.1 github.com/rs/cors v1.10.1 -
Run the server:
go run processHandler.go go run main.go -
Done! The API is now available at http://localhost:8000.
LICENSE
This project is under the MIT License. Check out LICENSE.md for the nitty-gritty details.
Contributions and Known Issues
We welcome contributions! If you encounter any issues or have enhancements in mind, please open an issue or submit a pull request.
Enjoy using this application and have fun exploring the world of linear regression with Go! 😎🚀