Process of a Machine Learning Project

The complete process of a machine learning project.

If you find it difficult to work on machine learning projects as a beginner, it will be good for you to break the entire process of a machine learning project into small steps. This will help you focus on all the steps while solving a problem, and in the end, you will end up with a complete machine learning project. So, if you want to follow a process while working on a machine learning project, this article is for you. In this article, I will walk you through the entire process of a machine learning project that you can follow as a beginner.

Process of a Machine Learning Project

Below is the complete process that you can follow while working on a machine learning project:

1. Understanding the problem
2. Collection of data
3. Data Exploration
4. Data preparation
5. Choosing an Algorithm
6. Training a Model
7. Testing and Evaluating the Model
8. End-to-end Deployment

Let’s go through all these steps one by one to understand the process of a machine learning project.

Understanding the Problem:

Before deciding which dataset or algorithm you should use to solve a machine learning problem, it is very important to understand what the problem statement is. That is why this is the first step, here you have to read the problem statement or understand what is the problem that a business is facing. If you can figure out the problem easily, the next steps will be easy for you.

Collection of Data:

After understanding the problem, your next step is to collect the most appropriate dataset to solve the problem. Here you can either use your web scraping skills to collect data or find a dataset from various data sources on the internet such as:

1. Kaggle
2. Data.gov
3. Data.gov.uk
4. open.canada.ca
5. census.gov
6. nasa.gov
7. data.worldbank.org

Data Exploration:

Now the next step is to explore the data that you are using for solving the problem. Here your task is to understand:

1. whether your data contains any missing values
2. how to treat the missing values
3. descriptive statistics
4. data visualisation of all the important features
5. correlation
6. understanding the relationship between the features and labels

Data Preparation:

After exploring the dataset, you will find a lot of information that will help you prepare your data. One of the most important steps in data preparation is determining whether your dataset needs normalization or standardization. If the dataset you are using is already in a normal distribution, you need to standardize the values of the features, and if they are not in a normal distribution, you need to normalize the values of the features.

Choosing an Algorithm:

The next step is to determine which machine learning algorithm you should use to train a model that can find the relationship between features and labels with high accuracy. If you don’t understand how to choose a machine learning algorithm, you can find some amazing tips here.

Training the Model:

The next step now is to train a machine learning model using a machine learning algorithm. Here you need to divide the data into training and test sets first, and then train a model on the training set. For better training of a machine learning model, it is necessary to divide the training data with more numbers (70 to 80%) of samples and the test set with 20 to 30% of the dataset depending on the size of the data.

Testing and Evaluating the Model:

The next step is now to test the performance of your model on unseen data. Here you can either use the test set or use another dataset with the same kind of features or taking user input in real-time. And then, to evaluate your model’s performance, you can use any performance evaluation metric. You can find some of the best and easiest to understand performance measurement metrics from here.

End-to-end Deployment:

It is not compulsory to always deploy your model as an end to end application. If you think that your machine learning model can help more if it is used as an end to end application, then you should create an end to end interface and deploy your model so that it can be used in real-time by a user. Otherwise, it is not always necessary to create end to end applications for your models. You can go through some of the end to end machine learning projects to understand how you can deploy your models into end to end applications from here.

Summary

While working on a machine learning project, if you have followed the above process step by step, you will end up having an amazing machine learning project, which will be very valuable both for your CV and your experience as a beginner. Hope you liked this article on the complete process of a machine learning project. Please feel free to ask your valuable questions in the comments section below.