Explains How To Build A Successful Machine Learning Model In Simple Steps
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:
2. Choose your target language. Get familiar with Python. Click to read:
Let’s Start The Implementation
1. Choose appropriate machine learning algorithm. Click to read:
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:
3. If it is a time-series regression problem then make the time series data stationary before forecasting it. Click to read:
4. Figure out a way to measure the performance of your algorithm up-front. Click to read:
5. Measure Performance Of Your Time Series Regression Model. Click to read:
6. Investigate if you need to use ARIMA model. Click to read:
7. If it is a unsupervised machine learning problem then understand how clustering works and is implemented. Click to read:
8. Explore Neural Networks And Deep Learning To See If It Works For Your Problem. Click to read:
9. Enrich Your Feature Sets, Rescale, Standardise And Normalise Them. Click to read:
Clean data in = Good results out.
10. Reduce Features Dimensions Space. Click to read:
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:
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.