A Practical guide on Deploying Machine Learning Project 101

A practical student guide on How to deploy your first ML project on Heroku.

In this article, you will be learning the approach for How to build a machine learning model, pack it flask, and deploy it on the cloud.

This article is divided into two parts —

1. Building ML Model
2. Deploying it on cloud

Building Machine Learning Model

I am not going to dig deeper into the process of preprocessing, feature engineering, building a generalized model, and other fine tuning steps, rather I’ll take the data that I had used in my project and build a model onto that. So that I can focus on the main task.

So we built a reliable machine learning model to predict CO2 Emissions in different type of Cars and the dateset was taken from Kaggle.

For building the model we used Linear Regression algorithm, we trained the model on the data which we have preprocessed and fine-tuned as needed for the model.

You can download the data from Kaggle and make it ready for training the model once the data set is ready you can go through the following code: read more