End to end Machine Learning Project with Deployment

The primary goal of this project is to deploy a Machine Learning solution using various different tools for deployment.

About the project

This is a dataset about student performance in math, reading and writing based on several factors such as gender, race/ethnicity, parental level of education, access to lunch, access to a test preparatory course. For the sake of this project we will use the math scores as a target variable whose values need to be predicted and the rest of the fields will be treated as dependent variables. Since this is going to be a regression problem, we will use traditional Machine Learning algorithms like Linear Regression, Lasso, Ridge, K-Neighbors Regressor, Decision Tree Regressor, Random Forest Regressor, XGBRegressor, CatBoosting Regressor and Adaboost Regressor. We will perform an analysis of the predictions of each of these algorithms and choose the one that gives us the best accuracy score after hyper-parameter tuning of the model during training.

Deployed model

Before going further check out the model and its performance. I have implemented two different implementations of the deployment. One to Amazon Web Services using Elactic Beanstalk and another to Microsoft Azure cloud. Below are the links to both of them. Note: This is a simple HTML template with no extra features and beautications to keep the deployment simple.

• Link to the AWS Deployment
• Link to the Azure Cloud Deployment

Its usage is pretty simple, just enter any values into the fields and you can get the appropriate resulting score.

Project Guidelines

1. Good code with a high degree of readability with comments
2. Well-structured code following software engineering principles
3. Modular code structure to replicate industry grade code
4. Deployment ready code for any platform

Project Roadmap

Here are a few milestones in the project roadmap which should give an idea of how this project is structured and built.

1. First we need to build the barebones of the project itself in a Jupyter notebook. This will serve two purposes. We can see if our implementaiton of the code works. We can do all necessary tweaking to our models, hyperparameters etc. We can also visualize our code. This also ensures that the datasets doesn't throw any curveballs at us during production. Going further, when writing modular code, we will spimply split all the functionality of the notebook into different scripts for better use.