End To End Guide For Machine Learning Project

Explains How To Build A Successful Machine Learning Model In Simple Steps

Farhad Malik
Dec 28, 2018 · 7 min read

Sometimes we just need clearly outlined steps instructing on how to implement a machine learning or data science project.

Why This Article Is Important?

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.

With That In Mind, Let’s Start

Scenario

Steps

A1. Make sure you understand what machine learning is and its three key areas. Click to read:

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

Clean data in = Good results out.

2. Check Their Probability Distribution, Correlation Matrix, Ensure They Are Key Inputs To Your Model.

2.1 Potentially Lose Features That Do Not Add Value

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:

3.1 Understand What Normal Distribution Is As It Is Most Widely Used Distribution

4. Figure out how you want to measure the performance of your forecasting. Click to read:

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

6. Do you need to use ARIMA model for your time series data? Click to read:

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

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

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

Now you are ready to load the appropriate libraries and implement the model.

9.1 Understand Neural Networks Activation Types

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

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:

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

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

11.1 Ensure Your Model Is Not Overfitting

11.2 Try Ensembler

11.3 Have A Look On Bagging As It Can Also Help With Improving Your Model

11.4 Boosting Can Also Used To Further Enhance Accuracy

12. Finally, Repeat These Steps Until You Get Accurate 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

13.1 If You Want To Speed Up Your Python Code Then Read

Summary

FinTechExplained

This blog aims to bridge the gap between technologists, mathematicians and financial experts and helps them understand how fundamental concepts work within each field. Articles

Farhad Malik

Written by

Explaining complex mathematical, financial and technological concepts in simple terms. Contact: FarhadMalik84@googlemail.com

FinTechExplained

This blog aims to bridge the gap between technologists, mathematicians and financial experts and helps them understand how fundamental concepts work within each field. Articles