Fun machine learning projects for beginners

Fun machine learning projects for beginners


As Machine Learning takes over the world, and there is no doubt that it will do so, the youth of the world inclines towards it more and more. The fascination associated with Artificial Intelligence in them just keeps on growing. This makes it quite appropriate for us to learn about a few fun projects of Machine Learning that beginners can look up and perform.

We shall start off with something that most of the beginners will not be quite familiar with, but something that they’ll want to for sure work on as well.

Now not all these need to necessarily be done by anyone who knows Machine Learning, but them knowing these projects and having done them will boost their chances of success in this field. Even if they are already established campaigners, these projects will surely help in expanding their knowledge further.

So let us start off with 10 such Machine Learning projects for beginners that are fun:

Here are 10 crazy cool project ideas for you to build with Python


Working on Movie Recommendations

It is safe to say that the usage of television screens in their usual fashion has diminished to a great degree. Streaming platforms have taken over in an overwhelming manner. Obviously, these platforms have to have fantastic recommendation engines so that their users use their platform for as long as possible and ignore the other platforms while doing so.

Therefore, predicting or figuring out what next to recommend to the viewership can be a rather tedious task, and here is where Machine Learning kicks in. By keeping track of the users’ past history and their past preferences, Machine Learning can be incorporated to predict what a user would desire to see in the future.

Such a task can be done using the Movielens Dataset from their website . Believe it or not, this dataset contains data from over 6,000 users on top which it also houses data of above a million movie ratings of 3,900 films! So, in essence, this is definitely an enjoyable yet fulfilling project for a beginner in the ML side of things.

A picture depicting the different strategies for recommender systems. Source: https://www.analyticsvidhya.com/blog/2020/11/create-your-own-movie-movie-recommendation-system/

A picture depicting the different strategies for recommender systems. Source: https://www.analyticsvidhya.com/blog/2020/11/create-your-own-movie-movie-recommendation-system/


Human Activity Recognition with Smartphones

Next up, we stay in a similar arena, so to speak. However, we look at a Machine Learning project that requires a totally different methodology of how to solve it. It is indubitably evident that tons and tons of us utilize our smartphones for daily activities and much more throughout the day.

Keeping the above in mind, being able to classify what the user is doing while using their smartphone could undoubtedly be of great use. This is because so much data is present to work with due to the extensive usage.

Many smartphones of today, therefore, are automated to detect what activities we are engaged in, whether it be running, cycling, and so on. For starters, a dataset containing fitness activity records of people collected through smartphones equipped with inertial sensors can be utilized. Classification models that accurately predict future activities can really help out vendors in how to market their products or perhaps when to give out certain advertisements and much more.

This type of project can also assist beginners in grasping the concept of multi-classification Machine Learning problems. This is obvious since, at one period of time, there is a possibility of multiple activities taking place.


House Price Prediction

Now, if you are someone who has already started their Machine Learning journey, you have most likely already heard of this as it is an extremely simple project, but one that shows Machine Learning concepts really well. This makes it excellent for beginners. Simplicity coupled together with conceptuality is precisely what one should look for in any project, let alone here.

This data set is available very readily on kaggle . With this project, the concepts of linear regression really shine through, and even a novice of Machine Learning would understand it as the mathematics involved is very straightforward. Obviously, more advanced Deep Learning models using Keras can also be implemented.

Aside from the excellent Machine Learning modeling, data visualization through Matplotlib can also be done. This makes a beginner’s implementation quite worthwhile. Another advantage of this project is that it is not a very large-scale one. Hence, its implementation does not take much time, nor does it require much computational power to achieve a good level of accuracy.


Fake News Detection

This is one that a beginner has probably heard of but never actually applied themselves. Now, this is for the type of beginners that are serious about their Machine Learning careers as it requires knowledge of Natural Language Processing, NLP, yet that is exactly what makes it fun as well.

The application of deciphering the difference between real and fake news is an absolute must to have in this day and age. Machine Learning again comes to the rescue and provides a methodology to overcome this problem too. Solving this intriguing and fun problem is not too hard with proper Machine Learning concepts.

The data set for this problem can be found quickly through, yet again our very beloved kaggle. This project does not take too much time either if the one who is implementing a model for it has some knowledge of NLP and, obviously, classification algorithms of Machine Learning.

A proposed model for fake news detection Source: https://www.researchgate.net/figure/Conceptual-Fake-News-Detection-Model_fig1_338360362

A proposed model for fake news detection Source: https://www.researchgate.net/figure/Conceptual-Fake-News-Detection-Model_fig1_338360362


Classification of Iris flower

Here is yet another essential Machine Learning project that beginners should definitely go for. It is also one of the easiest. No preprocessing of data is needed in this one, and the data can be found here . This project does not really take much time to complete either. In fact, it may just take a few minutes if you know some basic classification concepts and algorithms of Machine Learning.

All that is required in this project is to predict the species a particular Iris flower belongs to, given its attributes.


Breast Cancer Risk Prediction

Perhaps you, as a beginner, are interested in the healthcare side of things. Hence, this project is just right for you. In this project, supervised learning methods are incorporated. The aim here is to predict whether a patient possesses the risk of breast cancer or not, based on their symptoms and other history.

The dataset is available from a website that you are probably used to seeing very regularly by now here . Obviously, to properly complete this project, some knowledge of Decision Trees, Random Forests, and Ensemble Learning will be needed, but all the learning is very much worth it. It may therefore take a few hours to implement the model for this project. However, being able to use Random Forests and seeing its advantages in real-world problems is definitely very necessary, and frankly, fun since the logic is so obvious.


MNIST handwritten digit classification

The nature of this problem, digit classification, makes it fun itself from the beginning for any beginner. However, it is yet another great supervised learning classification project for beginners.

The MNIST dataset can be downloaded here .

It consists of many images of handwritten digits. The model that must be implemented is one that predicts the correct digit when the image of a handwritten digit is given.

Interestingly enough, this project will ensure that a beginner utilizes Deep Learning too, with TensorFlow coming into play. Concepts of Machine Learning and, obviously, Convolutional Neural Networks, CNN, must also be known by the ones who implement this beforehand. Given the fact that such knowledge is known, then this project will barely take an hour. Otherwise, it could clearly take much longer.

With the amount of things and concepts that are being cleared here, it shows that this is yet another project worth a beginner’s efforts. Its task is one that will also prove to be a desirable one for beginners.

Sample images from the MNIST dataset. Source: https://en.wikipedia.org/wiki/MNIST_database

Sample images from the MNIST dataset. Source: https://en.wikipedia.org/wiki/MNIST_database


Object Detection

Well, after the above project, it should not come as a surprise that we introduce the Object Detection project for beginners as well. Obviously, this is one of the fundamentals of Computer Vision, and it provides important information for the semantic understanding of images and videos. The application of such a project extends to image classification, human behavior analysis among a whole host of others such as image retrieval, security, surveillance, and so on.

Another excellent application of object detection is in automatic driving cars. Yes, indeed, object detection is a very hot aspect of Machine Learning, where Deep Learning and Computer Vision play a significant role.

In such a project, knowledge of neural networks and, obviously, the OpenCV library of python will surely be required. It is indeed more taxing than other projects, but again nothing out of the ordinary. Datasets that can be used for this purpose are this , which focuses on large-scale object detection, segmentation, and captioning, and this which focuses on the recognition of dogs and cats since the dataset consists of a collection of images and annotations labeling various breeds of dogs and cats.

Object detection can be incorporated into much more complex projects and applications as mentioned above and is certainly a good dive into Computer Vision and Machine Learning working together to literally do wonders. Something as interesting as this can simply not be missed out on by any beginner.

You can read much more about this topic on our object tracking article.


Sentiment Analysis on Twitter

Yet another fun project for a beginner is sentiment analysis on Twitter. Any firm in this day and age has to know what its customers/clients think of them in order for them to improve and come up with innovative ideas to go ahead of their competitors. NLP makes a comeback here, without which such a project would simply not be possible.

The thing about social media is that it provides a boatload of data day in and day out, and this opens the doors for companies to observe data regarding themselves. Now for system analysis on Twitter, the goal is to identify customer behaviors, or more like customer sentiments - their emotions and feelings towards your firm. In this project, however, it’s more of a general theme, where the job that needs to be implemented is only whether a person establishes a positive, negative, or neutral sentiment in their tweet.

Now this project will most definitely require an understanding of classification algorithms of Machine Learning as well as Natural Language Processing. The libraries of python will facilitate this well along with the jupyter notebook, perhaps. In fact, python and the jupyter notebook can assist in all the projects mentioned above.

This project is thus enjoyable since one is studying human behavior, so to speak. On top of that, your general knowledge and your Machine Learning knowledge really get expanded to a great degree. The dataset that can be used to implement a model for this project is here .

We also made a fun implementation of this project analyzing Elon Musk’s tweets .


Fraud Detection

The application of fraud detection is now more critical than ever since everyone is so socially so accessible, and security risks are more prominent than ever too. One of the most significant corporate meltdowns due to fraudulent business activities and practices is the Enron Scandal and Collapse.

The Enron email database consists of over 500,000 emails from Enron employees, and this data can simply be downloaded from here .

Now fraud detection by utilizing this dataset is beneficial for a beginner of Machine Learning, since they can learn many exciting concepts and can thrive in the future using this knowledge as their base. Here again, python’s TensorFlow library can help with modeling, and Matplotlib can help with data visualization.

The time that such a project will take, just as was the case for the others, will depend quite heavily on your current knowledge level. However, this project will increase a beginner’s knowledge level to a huge level since one will learn several clustering algorithms such as the K-means algorithm, Gaussian Naive Bayes, and the well-renowned Decision Tree Classifier. Therefore, as a beginner, do not leave out fraud detection on your checklist of projects.


Conclusion

To sum the entire discussion that we have had on all these projects, it is extremely evident that python shall be the goto language, as it consists of all the required Machine Learning libraries.

Python also includes libraries for Deep Learning, Natural Language Processing, and Computer Vision which are all part of Machine Learning in one way or the other. There will be disagreements of many Machine Learning enthusiasts as to what project should be done first by a beginner, but in essence, just starting and not looking back is what is important.

Machine Learning is like a building, where every block is not only necessary for the next, but it builds upon the previous block, albeit the fact that it may seem unrelated.

The more a beginner or really any person delves into Machine Learning, the more they want to. There is just so much to learn and so much to take home from. There is so much to change the future and to take advantage from financially or for research purposes.

So, sometimes and for some, it may just be about taking a leap of faith and starting. All the projects will simply follow suit.

Thanks for reading!