Finding machine learning datasets is tenacious indeed, but it doesn’t have to be! In this article, we’ve shared multiple datasets you can use for machine learning projects. We’ve also shared details on what every dataset contains along with a link to them. Our list includes datasets of different fields and various sizes so you can choose one according to your interests and expertise.
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Apart from that, we’ve shared project ideas for different datasets too so you can start working on a project right away. Working on projects will help you test your knowledge of machine learning algorithms. Let’s get started:
Machine Learning Datasets Project Ideas
1. Email Dataset of Enron
This dataset contains around 5,00,000 emails of more than 150 users. All of these emails are of a company called Enron, and most of the emails present in this dataset are of its senior management team. If you want to work on a natural language processing project, then you should begin here.
Enron’s email dataset is widely popular for NLP projects, and you’ll get to learn a lot from this. You can create a K-means clustering model and use it to identify any fraudulent activities through the texts of the emails. K-means clustering is an unsupervised ML algorithm and separates items into k amount of clusters according to their similarities.
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2. Image Dataset of Flickr
Flickr is an image hosting service with millions of users worldwide. This dataset has 30,000 images with different captions. You can use this dataset to create a caption generator for images. This dataset is quite famous for image analysis and image description through text.
You can create a CNN (Convolutional Neural Network) model that analyses images and generates a caption according to the features it identifies in a particular one. You can train the model through the thousands of captions available in the dataset. Building a caption generator will give you a lot of experience in learning image analysis works and how you can use it in real-world cases.
3. The Iris Dataset (Beginner-level)
If you haven’t worked on a machine learning project before, then you should start here. The Iris dataset is a popular choice among ML students because of its simplicity and size. It contains information on the three species of iris (a flower) such as its sepal and petal size.
Another name for this dataset is Fisher’s iris dataset because of its origin. Ronald Fisher had used this dataset in his 1936 paper.
The Iris dataset has four columns with 150 rows. You can create a classification model with this dataset. A classification model separates items into different classes according to their attributes, and creating one can help you learn the difference between unsupervised and supervised learning too.
4. The Parkinson’s Dataset
Parkinson’s dataset is accessible among students who want to use machine learning in the medical field. It is among the best datasets for machine learning projects of the medical sector as it contains 195 cases along with 23 attributes.
Parkinson’s disease is a disorder of the nervous system, and it affects basic movement. The slow movement, loss of balance, and stiffness are some of the most prominent symptoms of this disease. You can use this dataset to create a model that separates patients from healthy people by analyzing their symptoms and attributes to determine whether they have Parkinson’s or not.
The use of machine learning in the healthcare sector is getting more popular every day. So if you’re interested in using your machine learning expertise in that sector, you should start here. You can take inspiration from these applications of machine learning in healthcare.
5. The Mall Customers Dataset
This dataset has information on people visiting a mall. It contains multiple variables such as customer IDs, annual incomes, ages, spending scores, and gender. The dataset has divided customers into different categories according to their behaviors and tendencies.
You can use this dataset to create a classification model that segregates customers according to their gender, spending score, or annual income. This dataset is perfect for a customer segmentation project, which is a popular application of AI and ML in business.
Companies use customer segmentation to devise marketing strategies and enhance their advertisements. Working on this project will help you in understanding how you can use machine learning algorithms for accurate customer segmentation.
Read: Python Project Ideas
6. Uber Rides Dataset
This is among the best machine learning datasets for visualization projects. The Uber Rides dataset contains information on uber rides that took place between April 2014 and September 2014. Around 4.5 million uber rides took place at that time, so the dataset is quite humongous. The dataset contains information on the locations related to those rides and other relevant data.
You can use the data present in this dataset to create beautiful data visualization. Data visualizations help in gaining valuable insights from large pools of data. Apart from that, data visualizations help make better decisions according to the uncovered insights. You can take inspiration from these data visualization projects to get started.
7. Google Trends and its Data
Google Trends is a tool that allows you to analyze Google searches and find trending topics people are googling about. It’s a free yet powerful tool and can provide you with a lot of data on people’s search patterns and trends.
Google Trends allows you to find how many searches a particular keyword and its related terms got for a specific time. You can also use it to get data specific to a demographic.
If you plan on using machine learning for data analysis, then this is an enormous dataset to get started. You can get as much data you want on any topic you desire. Google Trends is excellent for a beginner who hasn’t worked on many machine learning projects.
8. The Kinetics Dataset
If you’re interested in using AI for recognizing human interactions, then this is the right dataset for you. Analyzing human actions and interactions, is a vital part of computer vision, the field of artificial intelligence which studies images and videos. Becoming adept in computer vision will help you in working on object identification, facial recognition, and other relevant applications of the same.
This dataset has nearly 650k videos that have human-human interactions (such as hugging and shaking hands) as well as human-object interactions (such as playing the guitar). It has 700 action classes where each class has at least 600 clips. Every clip has human annotation along with a single action class. The duration of every video in this dataset is around 10 seconds.
9. GTSRB Data
GTSRB stands for German Traffic Sign Recognition Benchmark, and it’s a great project to perform multiclass classification. This dataset has more than 50k images along with information on them. The dataset also has 40 classes, and the real traffic sign events in this dataset are unique within it.
It’s among the best datasets for machine learning projects when you consider its use cases. You can study image classification and create a framework to classify different traffic signs.
Classification of traffic signs can be a crucial part of an autonomous vehicle (self-driving car), so if you’re interested in the applications of AI in the automotive sector, you should work on this project.
You can start with a small section of this dataset if you don’t have much experience in working on ML projects.
10. The Boston Houses Dataset
The Boston Housing Dataset is among the most popular datasets for machine learning projects. It’s suitable for pattern recognition projects and is a great way to exercise your ML knowledge. This dataset contains the US Census Service gathered information on the housing in the Boston Mass area and has around 500 cases. In the dataset, there are 14 variables, including the per capita crime rate, the average number of rooms in a house, and others.
Because it has very few cases (506 to be exact), it’s suitable for new machine learning professionals and students. You can use this dataset to create a model that predicts the prices of houses in that region according to the data you found.
You can train the model with the prices of houses present in this dataset and then use it to predict future prices according to the conditions of a specific area. With this dataset, you can work on many similar project ideas of regression and real estate.
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Time to Work on Machine Learning Projects
Now that you have an extensive list of datasets for machine learning projects, you can now start working on one. We hope you found this list useful.
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What are datasets in machine learning?
In machine learning and data mining, a dataset is a collection of examples. It is a labeled set of examples used for machine learning or for the application of statistical methods. An example can be a single observation or an entire collection of observations. It is always easier to identify patterns in a dataset. Data is a collection of examples. It is the heart of machine learning and data mining. It is always easier to find patterns in a dataset.
What are the types of datasets?
Datasets have different types: a. Time Series Datasets - This describes a dataset from a particular time period is considered a time series dataset. b. Cross-section Datasets - This describes datasets which are a collection of observations from different but similar elements in the same time period. c. Mixed Datasets - This describes datasets which are a combination of time series and cross -sectional dataset. d. Components Datasets - This describes a collection of data set which is used to solve a specific problem. e. Transaction Datasets Describes a collection of data set which is used to find patterns, associations and relationships among the various entities. f. Graph Datasets - This describes a collection of data set which is used to draw a graph or map the elements in a network.
What are training and testing datasets in machine learning?
Training dataset is the set of examples used to train a model. This dataset is used to build the mathematical function, or model, f(x) that maps input data x to output y. The testing datasets are different from the training dataset. The testing dataset is a set of examples not used to train the classifier that is used to evaluate the performance of the classifier. Since the classifier is trained on the training examples, the performance of the classifier on the testing dataset is not fully known.