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10 Step Ultimate Guide For Machine Learning And Data Science Projects!

Discussion on the best approaches for the construction of your machine learning and data science projects in detail. Utilize these 10…

Photo by XPS on Unsplash
Photo by XPS on Unsplash

Data science and machine learning projects are some of the most interesting and engrossing pieces of craft to work on.

Initially, when I started out on my Artificial Intelligence journey, I was fascinated with the tremendous opportunities and projects that were at my disposal.

I was excited and wanted to do all of them.

However, I would pick a few of these projects and start them one at a time or sometimes even together, but I had a bit of a struggle in retrieving the right path towards completing them successfully.

I found out most beginners had a similar issue as well and struggled with the orientation and assembly while starting their projects as well. This article will cover the complete roadmap to follow in order to structure your project in the right manner.

It is essential to follow the presented roadmap exactly the way each step is written in this article (apart from a few exceptions). Similar to how you can’t eat food without cooking or ordering it, you can’t deploy your models without actually constructing them.

With that analogy out of the way, let us look at the stepwise procedure for developing awesome and cool machine learning/Data Science projects.


1. Selection and Formulation of Problem Statement:

Photo by 🇸🇮  Janko Ferlič on Unsplash
Photo by 🇸🇮 Janko Ferlič on Unsplash

Research, Research, and Research.

The most important step to any Machine Learning or data science project is to make sure you have one problem statement in your mind. Then research continuously with regards to the same.

Select your problem statement that you feel is a small step above your skill level. If your just a beginner starting out your data science journey, then pick a slightly complex beginner-level project. Let us say something like a simple linear regression project should also suffice.

If you have already completed some basic beginner-level projects, then aiming to shoot a bit higher for some intermediate-level projects should be a good idea. Understand your skills and keep working on improving them.

Please work on one project at a time. Make sure you have done extensive research on the project you choose to take up and don’t overwhelm yourself.

At the same time, don’t quit right after starting the project. Just remember that nobody, absolutely nobody gets everything on their first try. So don’t give up and persevere until you finish your machine learning or data science projects.

2. Developing and Strategizing your Plan:

Photo by Austin Distel on Unsplash
Photo by Austin Distel on Unsplash

Now that we have a clear image of the project ideas to implement, it is essential to formulate your strategy and plan accordingly.

One strategy I would highly recommend is reading more via research papers, google searches, especially on Google Scholar, or learn more by watching YouTube videos online.

It is always better to have more information and knowledge regarding your project before moving further onto the next steps and starting the actual implementation of your projects.

Build an approximate estimate for each of the tasks and applications to perform. Take into consideration the number of resources and time that might be required for the computation process. And don’t worry, this estimation does not need to be 100% accurate.

Just a simple brief idea about the topic of how you plan to execute it should suffice for this step.

3. Collection of Data:

Screenshot By Author
Screenshot By Author

The next step after analyzing your plan is to collect some data so that you can start the implementation of your data science or machine learning project.

Data collection is the process of gathering and measuring information on targeted variables in an established system, which then enables one to answer relevant questions and evaluate outcomes.

Google searching is obviously the best way to look for new resources. Kaggle offers some of the best data and datasets options available for each of the specific competitions that it holds. Sometimes very interesting datasets can also be found on GitHub as well.

If you are looking to do some natural language processing projects, then you can also make use of Wikipedia or other similar sites to extract data by web scraping.

The UCI Machine Learning Repository and Data.gov are other awesome websites that have the availability of a wide array of resourceful dataset options.

If you are wondering about the above image used with the faces dataset, then feel free to visit my other article that covers a deep learning end to end project. The link is provided below.

Human Emotion and Gesture Detector Using Deep Learning: Part-1

4. Exploratory Data Analysis:

Screenshot By Author
Screenshot By Author

Visualizations are a significant aspect of any data science project.

In statistics, exploratory data analysis is an approach to analyzing data sets to summarize their main characteristics, often with visual methods. A statistical model can be used or not, but primarily EDA is for seeing what the data can tell us beyond the formal modeling or hypothesis testing task.

The role of exploratory data analysis in the field of data science and machine learning projects is to be able to get a detailed understanding of the data at hand.

Exploratory data analysis offers many plots and varieties to visualize and analyze the data available. It provides a brief understanding and idea of how to proceed further.

Matplotlib.pyplot and seaborn are the two best library modules for visualization and performing exploratory data analysis tasks.

Feel free to visit my other article that covers the second part of the deep learning end to end project from the link provided below. The article in the below link goes into further detail on the exploratory data analysis and is a real-life problem for understanding this step to step guide better.

Human Emotion and Gesture Detector Using Deep Learning: Part-2

5. Pre-processing your Datasets:

Photo by Battlecreek Coffee Roasters on Unsplash
Photo by Battlecreek Coffee Roasters on Unsplash

Pre-processing of the datasets you have is a quintessential part of data science.

The data available to us may not always be "clean." The meaning of clean in this reference is the selective data that is of use to the task. In the naturally available data, there are a lot of redundancies that must be removed to have an overall clean dataset to work with.

Data preprocessing is an important step in the data mining process. The phrase "garbage in, garbage out" is particularly applicable to data mining and machine learning projects.

Data-gathering methods are often loosely controlled, resulting in out-of-range values, impossible data combinations, and missing values, etc.

For the simplification of natural language processing tasks, it is best to make use of the regular expressions module that is available as an in-built module in python. For further information on this, then refer to the article link provided below.

Natural Language Processing Made Simpler with 4 Basic Regular Expression Operators!

The pandas module must be highly considered for glancing at the datasets we have. The regular expression module is also of great importance for pre-processing as well.

6. Constructing your structure:

Photo by Christopher Burns on Unsplash
Photo by Christopher Burns on Unsplash

After the pre-processing step is done, then the next step is to construct the structure for the model you plan to build.

This step can stand for various things.

If you are working on a machine learning problem, then you want to make sure you complete computing all the essential parameters as other requirements.

The other requirements could be one hot encoding the variables, feature scaling, or other modeling requirements like splitting of the data into train, test, or validation accordingly, choosing the hyperparameters, tuning the model, etc.

Decide the entire structure and the process that will be used for this overall implementation. Design your modeling approach and build a final pipeline for which you can start the development of your models.

You can also choose which algorithm would be the best fit for the task at hand. This could vary from a simple linear regression algorithm or a complex deep learning methodology.

Let us now move ahead to the interesting part, which is the development of models.

7. Developing your Machine Learning or Deep Learning model:

Screenshot By Author
Screenshot By Author

After the visualizations, pre-processing, and construction steps, we can finally move over to the more interesting parts of developing the models.

Designing the appropriate model for the better performance of your task is the most significant aspect of machine learning and data science.

It is extremely important to choose the right algorithm for a particular task as well as design a concise architecture that can solve the problem and prove to be a best-fit approach.

In machine learning, whether supervised or unsupervised, you have a bunch of options to choose from. If you have the time and resources and you are not too sure which algorithm would perform best, you can try out all the algorithms and then decide which model works perfectly for your problem statement.

In deep learning, you can construct an architecture by either sequential, functional, or custom methods to build your own custom model from scratch, or you can utilize the many transfer learning models available to you and try to simplify your task.

The development of the models is the most significant step as this model will be trained, tested, and deployed. So it is extremely important to get this step right.

Let us now move ahead to the training step.

8. Training/Fitting your model:

Screenshot By Author
Screenshot By Author

Once the model is built, we can move ahead to the training step.

Training the model is making sure that we find an almost perfect fit with low losses and higher accuracy. It is also making sure that there is not underfitting or overfitting.

Underfitting means that the model built is underperforming, and it is not able to classify things accordingly and solve the problem as required.

Overfitting is a situation where your model is fitting so well that it is even considering the outliers and noise points, causing inaccuracies in the prediction task.

The learning algorithm finds patterns in the training data that map the input data attributes to the target (the answer that you want to predict), and it outputs an ML model that captures these patterns. You can use the ML model to get predictions on new data for which you do not know the target.

Basically, Training a model simply means learning (determining) good values for all the weights and the bias from labeled examples.

Let us now look at how the testing and analyzing step can be performed.

9. Testing and analyzing your created model:

Screenshot By Author
Screenshot By Author

With the construction and fitting of the model now complete we need to test and analyze the models we have.

We can make use of graphs to validate these tests and make sure the model is performing as desired. For machine learning projects you can make your own custom graphs with the help of the matplotlib library offered by python.

However, you can make use of the tensorboard option available in TensorFlow for the validation and checking of the training implementation.

These graphs should be able to provide a detailed approach to how the models built will perform. Further analysis can also be done by using the methods of AB testing before the deployment of your models.

If you are interested to learn more then feel free to check out this article on the next word prediction and innovative chatbots.

10. Deployment of your models:

Photo by Bench Accounting on Unsplash
Photo by Bench Accounting on Unsplash

The deployment stage is the final stage of any model constructed.

Once you have successfully completed building your model, this is an optional step if you want to keep it with yourself or deploy it so that you can target a wider audience.

The methods of deployment vary from deploying it as an application that can be transferred across, or by using the AWS cloud platform provided by amazon for deployment, or by making use of an embedded system.

If you want to deploy something like a security camera, then you can consider using something like the raspberry pi alongside a camera. If you are interested in learning more about face recognition to only grant access to the authorized owner, then consider the following article provided below.

Smart Face Lock System

With all these 10 steps taken care of, you should be ready to start and finish all your data science or machine learning projects!


Photo by Sean O. on Unsplash
Photo by Sean O. on Unsplash

Conclusion:

In this article, we covered the step by step roadmap and the approach towards every single machine learning project.

Each of these steps is significant in building the perfect architecture of successful data science or machine learning project.

Also, it is essential to follow each of the mentioned steps in the same order as presented above, with maybe very few exceptions for selective projects.

Just to summarize all the steps involved, make sure you conduct thorough research before deciding on your project. Then formulate a plan before you start the implementation of your project. Collect all the data required and start working on the task at hand.

Perform an exploratory data analysis so that you have a brief idea about the data and datasets you have. Then pre-process your data accordingly and start with the construction of your structure. Build the required models and start training them.

Test the models you have trained and analyze them thoroughly. Finally, your model should now be ready for deployment. Deploy your model to reach a wider audience.

Check out some of my other articles that you might enjoy reading!

OpenCV: Complete Beginners Guide To Master the Basics Of Computer Vision With Code!

Step By Step Guide: Proportional Sampling For Data Science With Python!

10 Most Popular Programming Languages For 2020 and Beyond

5 Best Python Project Ideas With Full Code Snippets And Useful Links!

Thank you all for sticking on till the end. I hope all of you enjoyed reading the article. Wish you all a wonderful day!


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