Homebreadcumb forward arrow iconBlogbreadcumb forward arrow iconArtificial Intelligencebreadcumb forward arrow iconTop 10 Machine Learning Datasets Project Ideas For Beginners [2024]

Top 10 Machine Learning Datasets Project Ideas For Beginners [2024]

Last updated:
3rd Oct, 2022
Read Time
9 Mins
share image icon
In this article
Chevron in toc
View All
Top 10 Machine Learning Datasets Project Ideas For Beginners [2024]

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. 

Top Machine Learning and AI Courses Online

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. 

Ads of upGrad blog

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. 

Link to Dataset

Trending Machine Learning Skills

Enrol for the Machine Learning Course from the World’s top Universities. Earn Masters, Executive PGP, or Advanced Certificate Programs to fast-track your career.

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. 

Link to Dataset

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. 

Link to Dataset

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.

Link to Dataset

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. 

Link to Dataset

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.

Link to Dataset

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. 

Link to Dataset

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. 

Link to Dataset

Read: Machine Learning Project Ideas

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. 

Link to Dataset

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. 

Link to Dataset

Ads of upGrad blog

Popular AI and ML Blogs & Free Courses

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. 

If you’re interested to learn more about machine learning, check out IIIT-B & upGrad’s PG Diploma in Machine Learning & AI which is designed for working professionals and offers 450+ hours of rigorous training, 30+ case studies & assignments, IIIT-B Alumni status, 5+ practical hands-on capstone projects & job assistance with top firms.


Pavan Vadapalli

Blog Author
Director of Engineering @ upGrad. Motivated to leverage technology to solve problems. Seasoned leader for startups and fast moving orgs. Working on solving problems of scale and long term technology strategy.
Get Free Consultation

Selectcaret down icon
Select Area of interestcaret down icon
Select Work Experiencecaret down icon
By clicking 'Submit' you Agree to  
UpGrad's Terms & Conditions

Our Popular Machine Learning Course

Frequently Asked Questions (FAQs)

1What 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.

2What 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.

3What 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.

Explore Free Courses

Suggested Blogs

15 Interesting MATLAB Project Ideas & Topics For Beginners [2024]
Diving into the world of engineering and data science, I’ve discovered the potential of MATLAB as an indispensable tool. It has accelerated my c
Read More

by Pavan Vadapalli

09 Jul 2024

5 Types of Research Design: Elements and Characteristics
The reliability and quality of your research depend upon several factors such as determination of target audience, the survey of a sample population,
Read More

by Pavan Vadapalli

07 Jul 2024

Biological Neural Network: Importance, Components & Comparison
Humans have made several attempts to mimic the biological systems, and one of them is artificial neural networks inspired by the biological neural net
Read More

by Pavan Vadapalli

04 Jul 2024

Production System in Artificial Intelligence and its Characteristics
The AI market has witnessed rapid growth on the international level, and it is predicted to show a CAGR of 37.3% from 2023 to 2030. The production sys
Read More

by Pavan Vadapalli

03 Jul 2024

AI vs Human Intelligence: Difference Between AI & Human Intelligence
In this article, you will learn about AI vs Human Intelligence, Difference Between AI & Human Intelligence. Definition of AI & Human Intelli
Read More

by Pavan Vadapalli

01 Jul 2024

Career Opportunities in Artificial Intelligence: List of Various Job Roles
Artificial Intelligence or AI career opportunities have escalated recently due to its surging demands in industries. The hype that AI will create tons
Read More

by Pavan Vadapalli

26 Jun 2024

Gini Index for Decision Trees: Mechanism, Perfect & Imperfect Split With Examples
As you start learning about supervised learning, it’s important to get acquainted with the concept of decision trees. Decision trees are akin to
Read More

by MK Gurucharan

24 Jun 2024

Random Forest Vs Decision Tree: Difference Between Random Forest and Decision Tree
Recent advancements have paved the growth of multiple algorithms. These new and blazing algorithms have set the data on fire. They help in handling da
Read More

by Pavan Vadapalli

24 Jun 2024

Basic CNN Architecture: Explaining 5 Layers of Convolutional Neural Network
Introduction In the last few years of the IT industry, there has been a huge demand for once particular skill set known as Deep Learning. Deep Learni
Read More

by MK Gurucharan

21 Jun 2024

Schedule 1:1 free counsellingTalk to Career Expert
footer sticky close icon