Gold BlogEnd To End Guide For Machine Learning Projects

Let’s imagine you are attempting to work on a machine learning project. This article will provide you with the step to step guide on the process that you can follow to implement a successful project.

It’s very hard to find a succinct article providing an end-to-end guide to implement a machine learning project. We find many informative articles online providing an in-depth coverage of how we need to implement parts of a machine learning/data science project but at times, we just need high level steps offering clear guidance.

When I was new to machine learning and data science, I used to seek articles that clearly outlined the steps stating what I need to do to get my project done.

This article aims to provide an end-to-end guide for getting a successful machine learning project implemented.


With That In Mind, Let’s Start


In a nutshell, a machine learning project has three main parts: Data Understanding, Data Gathering & Cleaning, And Finally Model Implementation And Tuning. Usually, Data Understanding, Gathering And Cleaning Takes 60–70% Of The Time. And For That, We Need A Domain Expert.



Let’s imagine you are attempting to work on a machine learning project. This article will provide you with the step to step guide on the process that you can follow to implement a successful project.

In the beginning, there are multiple questions arising in our brain

Data Science Is Trial And Error, It’s Research And Recursive, It’s Practical And Theoretical, It Requires Domain Knowledge, It Boosts Your Strategic Skills, You Learn About Statistics And Master Programming Skills. But Most Importantly, It Teaches You To Remain Patient As You Are Always Close To Finding A Better Answer.



Two Pre-requisite Steps:

1. Make sure you understand what machine learning is and its three key areas. Click to read:

Machine Learning In 8 Minutes
Machine learning is the present and the future. All technologists, data scientists and financial experts can benefit…

2. Choose your target language. Get familiar with Python. Click to read:

Python From Scratch
Python is one of the most popular programming language for data analysis and Machine Learning. Additionally a large…


Let’s Start The Implementation

1. Choose appropriate machine learning algorithm. Click to read:

Machine Learning Algorithms Comparison
There are a large number of Machine Learning (ML) algorithms available. In this article, I am going to describe and…

By now, you would have understood if it’s a supervised or unsupervised problem that you are attempting to resolve.

There is always a potential to find another right answer. There are often multiple right answers in a forecasting problem.

2. If it’s a supervised machine learning problem then ensure you understand if it’s regression or classification problem. Click to read:

Supervised Machine Learning: Regression Vs Classification
In this article, I will explain the key differences between regression and classification supervised machine learning…

3. If it is a time-series regression problem then make the time series data stationary before forecasting it. Click to read:

How Do I Predict Time Series?
Forecasting, modelling and predicting time series is increasingly becoming popular in a number of fields. Time series…

4. Figure out a way to measure the performance of your algorithm up-front. Click to read:

Must Know Mathematical Measures For Every Data Scientist
There are a large number of mathematical measures that every data scientist needs to be aware of. This article outlines…

5. Measure Performance Of Your Time Series Regression Model. Click to read:

How Good Is My Predictive Model — Regression Analysis
Forecasting is an important concept in econometric and data science. It is also widely used in artificial intelligence…

6. Investigate if you need to use ARIMA model. Click to read:

Understanding Auto Regressive Moving Average Model — ARIMA
In my article “How Do I Predict Time Series?”, I provided an overview of time series analysis. Core of the article…

7. If it is a unsupervised machine learning problem then understand how clustering works and is implemented. Click to read:

Unsupervised Machine Learning: Clustering and K-Means
In this article, I want to explain how clustering works in unsupervised machine learning. In particular, I want to…

8. Explore Neural Networks And Deep Learning To See If It Works For Your Problem. Click to read:

Understanding Neural Networks: From Activation Function To Back Propagation
This article aims to provide an overview of neural networks. It outlines fundamental concepts of neural

9. Enrich Your Feature Sets, Rescale, Standardise And Normalise Them. Click to read:

Processing Data To Improve Machine Learning Models Accuracy
Occasionally we build a machine learning model, train it with our training data, and when we get it to predict future…

Clean data in = Good results out.

10. Reduce Features Dimensions Space. Click to read:

What Is Dimension Reduction In Data Science?
We have access to a large amounts of data now. The large amount of data can lead us to create a forecasting model where…

If after enriching your features and reducing the dimensions, your model does not yield accurate results then look to tune the model parameters.

11. Fine-Tune your machine learning model parameters. Click to read:

How To Fine Tune Your Machine Learning Models To Improve Forecasting Accuracy?
Fine tuning machine learning predictive model is a crucial step to improve accuracy of the forecasted results. In the…

Always ensure you are not over-fitting or under-fitting

12. Finally, Repeat These Steps Until You Get Accurate Results:

  1. Enrich Features
  2. Fine Tune Model Parameters

Always analyse your data set and see if you are missing any important information, resolve the problems when you see them but always take a back up and save your work as you might be required to go back to the previous step.

Machine Learning Is Recursive In Nature



I wanted a simple page that listed out the steps which we need to follow to implement a machine learning model. This article aimed to provide an end-to-end guide for getting a successful machine learning project implemented.

Hope it helps.

Bio: Farhad Malik explains complex mathematical, financial and technological concepts in simple terms. Editor of FinTechExplained. Contact:

Original. Reposted with permission.