End To End Guide For Machine Learning Project
Explains How To Build A Successful Machine Learning Model In Simple Steps
Sometimes we just need clearly outlined steps instructing on how to implement a machine learning or data science project.
This article aims to provide an end-to-end guide for implementing a successful machine learning project.
It can be over-whelming to write the entire guide as one article. Keeping that in mind, I have written a number of easy-to-understand articles and provided their links here so that the readers can understand the steps and navigate to the appropriate article if required.
Why This Article Is Important?
We find many informative articles online that provide 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.
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 Closer To Finding An Accurate Answer.
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With That In Mind, Let’s Start
In a nutshell, a machine learning project has three main parts:
- Data Understanding, Data Gathering & Cleaning,
- Model Selection & Implementation
- Model Parameters & Data 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
A1. 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…
A2. Choose your target language. Get familiar with Python. Click to read:
A2.1 Also Quickly Review The New Python Features
A2.2 Pandas And Numpy Are Absolutely Amazing
A3. Ensure You Design Your Application Correctly As It Can Save You Time And Effort In The Future
Let’s Start The Real Work
1. Understand And Load The Data Into Appropriate Data Structures
Make sure you find which features you want to gather data for. Load the data into appropriate data structures. Lose the features which do not add any value.
Enrich Your Feature Sets, Rescale, Standardise And Normalise Them:
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.
2. Check Their Probability Distribution, Correlation Matrix, Ensure They Are Key Inputs To Your Model.
Understanding Value Of Correlations In Data Science Projects
Explore The Heart Of Data Science. It’s Crucial To Understand The Significance Of Calculating Correlations
2.1 Potentially Lose Features That Do Not Add Value
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.
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…
3.1 Understand What Normal Distribution Is As It Is Most Widely Used Distribution
Ever Wondered Why Normal Distribution Is So Important?
Explaining the reasons why Gaussian distribution is so successful and widely used probability distribution
4. Figure out how you want to measure the performance of your forecasting. 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. 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.
6. Do you need to use ARIMA model for your time series data? 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’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…
8. If it is an 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…
9. 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 networks.
Now you are ready to load the appropriate libraries and implement the model.
9.1 Understand Neural Networks Activation Types
Neural Network Activation Function Types
Understanding what really happens in a neural network
9.2 Understand What Weights And Biases Are
9.3 Get Familiar With What Neural Network Neurons Are
9.4 Ensure You Understand What Hidden Layers Are
What Are Hidden Layers?
Important Topic To Understand When Working On Machine Learning Models
9.5 Potentially Have a Look At Neural Network Usecases So You Can Get A Stronger Grip On The Concepts
10. Implement The Chosen Models. Load The Data Into The Models. Finally, Measure The Forecasting Accuracy Of Your 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…
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
11.1 Ensure Your Model Is Not Overfitting
The Problem Of Overfitting And How To Resolve It
Explaining How To Fix The Curse Of Overfitting In Python
11.2 Try Ensembler
Let’s Talk About Machine Learning Ensemble Learning In Python
Build Better Predictive Models By Efficiently Combining Classifiers Into A Meta-Classifier
11.3 Have A Look On Bagging As It Can Also Help With Improving Your Model
Machine Learning Bagging
Explaining How Accuracy Of Unstable Machine Learning Models Can Be Improved Via Bagging Technique
11.4 Boosting Can Also Used To Further Enhance Accuracy
Machine Learning Boosting Via Adaptive Boosting
Understand the most widely used ensemble method that learns from its mistakes
12. Finally, Repeat These Steps Until You Get Accurate Results:
- Save Your Model
How To Save Trained Machine Learning Models?
Save & Reload Your Trained Machine Learning Models In Python
- 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.
How I Improved Accuracy Of My Machine Learning Project?
Follow these tips to get better results
Machine Learning Is Recursive In Nature
13. Finally if you want to make a business our of your machine learning model then you can make it public so that wider audience can use it
Flask — Host Your Python Machine Learning Model On Web
Learn How To Make A Business Out Of Your Machine Learning Model Using Python Flask
13.1 If You Want To Speed Up Your Python Code Then Read
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.