10 Fun Machine Learning Project Ideas for Newbies

Machine learning is considered as one of the most significant technological developments in the modern world. “Learning by doing” is a great way to get familiar with machine learning concepts. If you are a newbie, you may think it’s difficult to get started with machine learning projects without prior experience. You may also find it difficult to come up with interesting machine learning ideas. Don’t worry. This article will explore ten easy machine learning projects for beginners to get hands-on experience in machine learning. You can also use data science tools in these projects to carry out the operations and make the development process easier.

Machine Learning projects for beginners

Below are ten cool machine learning projects which beginners can work on.

machine learning project for beginners

1. MNIST handwritten digit classification

The first machine learning project we are going to consider is a supervised learning classification problem. This is a simple and exciting project for anyone who is trying to get started with machine learning.

MNIST data set consists of a large number of images and handwritten digits. There you have to develop a model in order to predict the correct digit when an image of a handwritten digit is given. The MNIST dataset is available to download here.

There are many data science tools such as TensorFlow, Jupyter notebook, which quickly and easily build this model. If you have grasped the concept clearly, it’s only 20 minutes to complete this project.

It is also better to have a basic idea of classification in machine learning for this project. You will learn how to apply the concepts of classifications with Convolutional Neural Networks (CNN) by developing the model. This TensorFlow article will guide you on how to develop the classification model.

2.  Fraud detection using Enron dataset

The Enron scandal and collapse can be considered one of the largest corporate meltdowns due to fraudulent business practices. Enron email database contains a large number of emails (over 500 000) of Enron employees. You can download the Enron email dataset here.

Fraud detection using the Enron dataset is a beginner machine learning project where a newbie can learn many exciting concepts in machine learning. Matplotlib and TensorFlow are some of the data science tools that can be used to make the development faster in this project.

The time which needs to be allocated for this project depends on the understanding of the machine learning concepts like clustering. By working on this project, you will learn several clustering algorithms such as Decision Tree Classifier, K means algorithm, and Gaussian Naive Bayes.

3.  Fake news detection

Nowadays, distinguishing between real and fake news has become a challenging task. But it can be achieved using machine learning concepts and algorithms. Fake news detection is one of the most interesting and easy machine learning project ideas well suited for beginners.

Within this project, you need to develop a model that can differentiate between real and fake news. You can use this dataset to train the Machine Learning model.

Having previous knowledge of Natural Language Processing (NLP) concepts and classification algorithms in machine learning is beneficial for this project. If so, this project will only take a few hours.

This project will also improve your machine learning algorithm and NLP knowledge.

4.  Housing price prediction

Housing price production is one of those simple machine learning projects that test mathematical knowledge of Machine Learning newbies. This project aims to predict the price of a selling house based on factors such as location, type, number of rooms, etc.

You can use this Kaggle dataset to train and test the model. Housing price prediction can be considered as a convenient project as it requires to know only some basic machine learning concepts such as linear regression. The use of data science tools such as Keras and Matplotlibwill make the implementation more comfortable.

This project also consumes only a few hours if you are already familiar with the basic concepts of machine learning. It will also improve your knowledge of data visualization and regression.

5.  Stock Price Prediction

Predicting stock price is one of the medium level machine learning project ideas. It has become an attractive topic for investors and researchers. The objective of this project is to predict the prices of stocks in the stock market.

To develop this model, you should have sufficient knowledge in neural network models such as Recurrent Neural Networks (RNN) and about the workflow of the project. If you are not familiar with  RNN, first do some study about it and then follow this kaggle guide to get a proper idea on how to implement the model.

The use of Tensorflow and Keras data science tools for the implementation will make the process quite easier. This project may last a comparatively longer time than other projects mentioned in this article because it requires an understanding of some deep machine learning concepts.

More importantly, this project will enhance your knowledge of neural network models such as RNN.

6.  Classification of Iris flower

Iris flower classification problem can be called one of the easiest beginner machine learning projects,which is also known as the “Hello world of machine learning projects.”

If you have a doubt on “How do I start a machine learning project?” this is the right place to kick-start.

Iris flower dataset has several attributes, such as the number of petals, sepals length, sepals widths, etc. All the flowers can be classified into one of the species from Virginica, Setosa, or Versicolor. This project aims to develop a machine learning model to classify the flowers into the above-mentioned species.

Iris flower dataset does not need to be preprocessed. You can download the dataset for this project here. Moreover, you can use a number of data science tools for this project, such as R.

It takes only a few minutes to develop the model if you are aware of the basic classification concepts and algorithms in machine learning.

7.  Machine learning gladiator project

This is an appropriate machine learning project for beginners. Beginners can get hands-on experience and practice how to clean data, preprocess data, and how to import data as well.

If you have any doubts about “How do I organize my machine learning project?” you would be able to have perfect solutions for them after completing this project.

You can use data science tools such as the Jupyter Notebook if you are going to implement this project in python or R. For this project, you will need to have basic knowledge in classification, regression, and clustering algorithms.

You can find and download the dataset for this project here, and it may take a few hours to understand and develop the project. By doing this project, you can master the workflow of model building.

8.  Breast Cancer risk prediction

This is also one of the beginner machine learning projects that are related to the healthcare industry. The model that will be implemented in this project and trained using supervised learning methods. The aim of this project is to predict whether the patient has a breast cancer risk or not, based on the symptoms and other relevant background details of the user.

You can download the Breast Cancer Wisconsin (Diagnostic) Data Set here. You can find two predictor classes as malignant and benign in this dataset.

To implement this project successfully, you need to have sufficient knowledge of the Random Forest Machine Learning algorithm. It might take a few hours to understand the features of the project and implement the model. However, the effort is worth it as you will gain a solid understanding of how to use the Random Forest Algorithm in real-world problems.

9.  Sentiment analysis on Twitter

This is a well known medium level machine learning project for beginners. Sentiment analysis is an application of Natural Language Processing.

Social media generates a huge amount of big data every day. Analyzing these data has been the key source to identify customer behaviors. When it comes to Twitter, developing a machine learning model to identify sentiments behind the user’s tweets is a great advantage for businesses and organizations.

beginner machine learning projects

This project also requires a basic understanding of classification algorithms in machine learning as well as concepts of natural language processing. This model can be easily developed using data science tools such as Jupyter notebook and Natural Language Toolkit.

You can get a clear idea of how to develop the model by following this guide.

10.  Movie recommendation system

Nowadays, people prefer to watch movies online rather than watching on TV.  Therefore, developing a machine learning-based recommendation system for movies will become one of the most attractive machine learning project suggestions.

We can use Movielens Dataset, which contains a large number of movie ratings which have been made by Movielens users, to train our model. In order to develop this model, you need to have some knowledge of the Item-Based Collaborative Filtering method, which was developed by Amazon.

While you can download the Movilian dataset here, you can use either Python or R to implement this project.

Data science tools such as R studio and TensorFlow are great tools that can be used for implementing this recommendation system effortlessly. The time required for this project depends on the level of familiarity with the machine learning methods you have chosen to develop this system.

Conclusion

In this article, we have covered some machine learning ideas which are ideal for beginners to get a head start in machine learning. Since machine learning is one of the hot topics among students and professionals in the current IT industry, you can easily find out great machine learning sample projects, refer and identify the methods they have used and practice them. Furthermore, using data science tools such as TensorFlow, Keras, and R studio in these projects, will make the implementation process faster and easier.

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