Top 25 Artificial Intelligence (AI) Project Ideas in 2024

More businesses are banking on AI, with nearly half of enterprise-scale businesses in the US already using the technology. The AI market is also expected to top $400 billion by 2027 and contribute to a 21% increase in the net GDP of the US by 2030.

This is big news for anyone working in the sector or considering joining the AI bandwagon. Taking on a few AI projects is a great way to get started or learn about the new cool things AI can do today.

This is why we have compiled 27 of the best artificial intelligence project ideas you can work on this year. We made sure there’s something for everyone on our list, whether you’re a beginner just dipping your toes in, a seasoned pro looking for a challenge, or somewhere in between.

1. House Rent Predictor

Predicting property rents is a good first project in AI. The objective is straightforward, and the datasets are relatively easy to understand. This project will introduce you to fundamental concepts in AI, such as linear regression, data collection, and data preprocessing.

Your main objective is to create a model that can accept variables such as location, number of bedrooms/bathrooms, floor area, amenities, etc., and return an accurate estimate of the rent.

How to Start:

You can try this take-home assignment on short-term rentals on Interview Query (with an available solution), or you can pick a dataset from Kaggle and follow this tutorial on Medium.

2. Car Price Predictor

Predicting the price of used cars is another excellent beginner AI project with relatable datasets and objectives. This is also a good project to undertake if you want to work for companies such as Carmax and Carvana.

This project aims to build a model that can predict the price of a used car based on variables such as make, model, mileage, fuel type, and location. A secondary objective would be to determine how much different factors affect the prices of used cars.

How to Start:

Pick a used car data set on Gigasheet or Kaggle, and follow this guide. This guide takes you through data preprocessing and exploratory data analysis before diving into predictive analytics using linear regression and more complex models.

3. Sentiment Analysis

User opinions help companies to develop products and services that convert better. However, most user opinion today comes in the form of unstructured data. Sentiment analysis can harness Natural Language Processing to predict from text, e.g. X (Twitter) posts, how users feel about something.

This project aims to develop an algorithm that performs sentiment analysis. The user responses can be classified as positive, negative, or neutral.

How to Start:

Pick a dataset for sentiment analysis from Data.world. This guide explains how you can perform sentiment analysis using machine learning. You can also test your skills using this take-home assignment or check out other sentiment analysis project ideas on Interview Query.

4. Spam Detection

Spam messages continue to be a problem in emails and other forms of messaging. The creators of these messages continue to adapt to bypass existing spam filters. There is also a problem of important messages getting classified as spam.

This project aims to develop a filter that can accurately predict if an email is spam or not.

How to Start:

This guide provides the steps you can follow to develop your spam email detector using machine learning, including where you can find suitable datasets. You can also try this take-home assignment on building a spam article classifier on Interview Query.

5. Resume Parser

A corporate job opening attracts an average of 250 applicants in the US. To process applications faster, hiring managers use resume parsers to extract the most important information from resumes.

This project aims to build a resume parser that uses NLP to extract only the most relevant information from a resume.

How to Start:

SpaCy is a natural language processing (NLP) library in Python. This guide takes you through the steps of using this tool to build a resume parser.

6. Hoax Detector

2024 is an election year, and that makes fake news a major concern. A fake news or hoax detector is an AI model that identifies posts or articles that make false claims. Such a tool can be particularly important for social media companies such as Meta.

The objective of this project is to create a model that can accurately predict if the information in an article is not factual. Natural language processing is essential for such projects since the data is going to be unstructured.

How to Start:

This tutorial offers a good example of how you can implement a hoax detector using the scikit-learn library.

7. Animal Species Identifier

Identifying animal species is vital to wildlife conservation efforts, autonomous driving systems, and even pet owners. In the case of autonomous driving systems, the vehicle can be programmed to respond differently depending on the animal on the road e.g., moose vs squirrel vs bird.

This project aims to develop a model that can accurately classify different animals based on a photo or video. These models use datasets with thousands of images of different animals.

How to Start:

Kaggle provides a wide range of animal species datasets you can use on this project. This guide offers one approach to building this system that utilizes deep learning.

8. GDP Predictor

Governments have a wide scope of responsibilities. This makes it challenging to identify the best sectors of the economy to invest in to improve the GDP. A data-driven system that can accurately inform government investments to create greater GDP growth can be a game-changer.

The objective of this project is to create an AI model that will identify which sector investments are likely to yield the most significant GDP growth.

How to Start:

This dataset on Kaggle contains data on how 96 governments spent their money for each year between 2000 and 2021. You can combine this with machine learning techniques to identify which investment areas have a greater correlation with rising GDPs.

9. Spam Detection With Deep Learning

When working with AI, it’s always a good idea to see if your models can improve on initial results. Improving the accuracy of the spam detection model above by incorporating deep learning is a good example.

The objective here is also to develop an accurate spam message filtering system. However, this project will introduce you to an alternative approach that could yield vastly superior results.

How to Start:

This article is a continuation of the initial guide for building a spam filter. It introduces you to several concepts, including word embedding and bidirectional deep learning models.

10. Object Detector Using PyTorch

Object detection is a common application of AI in many industries. It’s helpful in detecting animals, hazards, vehicles, etc. PyTorch is a Python library that makes it easy to create a deep-learning model that can be used for many forms of object detection or image recognition.

The objective of this project is to detect the various items, people, animals, etc., in an image. It will also introduce you to PyTorch, a powerful machine-learning framework for computer vision.

How to Start:  

Follow the steps in this article to see how PyTorch is used to detect a dog in an image. Repeat the experiment with other images. You can also check out this image recognition project that uses the CIFAR dataset.

11. Object Detection (YOLO and TensorFlow)

This implementation of object detection has the same objectives as the one above. However, the approach is different, and the results may also be different. TensorFlow is preferred over PyTorch by many big companies such as Google and Microsoft.

How to Start:

This article is a step-by-step guide to creating an object detection model using TensorFlow. The technical aspects of the project, such as model parameters, are also well explained.

12. Chatbot

According to Backlinko, 58% of B2B companies already have a chatbot on their site. Chatbots have become an essential way for businesses to improve customer satisfaction. Integrating AI can enable these Chatbots to do more than offer pre-programmed responses to user queries.

The objective of this project is to create a chatbot that utilizes natural language processing to respond to user queries.

How to Start:

This tutorial on mygreatlearning.com offers an excellent introduction to chatbots and takes you through the process of creating text-based and voice-based chatbots.

13. Digit Recognition

Handwriting recognition was one of the pioneering applications of neural networks, specifically convolutional neural networks or CNNs. This technology makes it easier to extract and transform handwritten information from forms and documents. It even makes check processing faster.

The objective of this project is to create a graphical user interface (GUI) where users draw a digit, and the computer returns the actual number.

How to Start:

This tutorial offers a quick guide to get you started on digit recognition using the MNIST dataset.

14. Fake Review Identifier

As much as 42% of the reviews on Amazon are considered fake. The commercial potential for an algorithm that can identify these fake reviews would be significant.

The objective of this project is to develop a model that can detect fake reviews based on the text in the review.

How to Start:

This guide from Practical Data Science offers a step-by-step approach to get you started on this project.

15. Smart Stock Trader

Stock trading applications are essential to companies in financial markets such as Bloomberg. This can be a decent portfolio project when applying to these companies if the project is well executed.

The objective here is to create a semi or fully-automated stock trading bot that uses AI to analyze market data and make DUMMY trades.

How to Start:

This guide for creating an automated AI-based trading system from Towards Data Science is an excellent introduction to creating AI-based stock traders.

16. Loan Default Risk

This is the type of portfolio project that banking institutions such as Citi and Wells Fargo will be interested in. Banks can lose a lot of money on bad loans and AI can help to identify high-risk applicants early.

The goal of this project is to create an algorithm that can accurately identify loan applicants who end up defaulting on their payments.

How to Start:

This article offers one example of how machine learning can be used to identify high-risk loan applicants.

17. Book Recommender System

Companies like Amazon, YouTube, Spotify, and Meta use recommendation systems. These systems are useful for recommending purchases consumers are likely to make or posts and media that keep users on a platform.

The goal of this project is to develop a book recommendation algorithm.

How to Start:

This dataset from Kaggle contains information for more than 270,000 books, and this article shows one way of developing a book recommendation system. You can also practice creating recommender systems by trying this Airbnb take-home assignment on Interview Query.

AI Project Ideas for Experts

18. Deepfake Detector

The rise of generative AIs has given rise to deepfakes. Despite the potential benefits of the technology, there is genuine concern about using deepfakes to disparage others and influence political opinions, especially during elections.

The objective of this project is to create an algorithm that can detect videos that contain voices or faces that have been altered/faked.

How to Start:

Try taking on Kaggle’s deepfake detection challenge. The competition is closed, but the dataset plus code used by various teams is accessible on Kaggle.

19. Heart Disease Predictor

AI is expected to revolutionize diagnostics in the medical field. A promising area of study is the use of artificial intelligence to predict heart disease.

The goal here is to develop an algorithm that predicts if a patient will develop heart disease based on information such as age, gender, occupation, diet, stress levels, and other clinical data.

How to Start:

This study demonstrates how one team developed an AI model to predict heart disease with an accuracy of about 83%. You can also check out other projects where data science and machine learning are used in healthcare on Interview Query.

20. Emotion Detection in Real Time

Emotion detection can be considered an advanced form of sentiment analysis. This technology has potential applications in customer care, autism therapy, and even the detection of deepfakes.

This project aims to develop an algorithm that detects people’s emotions in real-time.

How to Start:

This study explains how some researchers achieved real-time emotion detection using DeepFace.

21. Pneumonia Detection

According to the National Institutes of Health, early detection of pneumonia can save lives. This is yet another diagnostic field where artificial intelligence has shown significant promise.

This project aims to develop an algorithm to detect pneumonia in patients. Different approaches can be taken, e.g. use of lung sounds or chest radiographs.

How to Start:

In this study, researchers used deep learning to predict the presence of pneumonia using lung sounds from an electronic stethoscope.

22. Personality Predictor

Knowing people’s personalities is helpful in marketing, customer care, and even when hiring a new employee. When companies understand a person’s personality, they can adjust their approach to increase the probability of a sale or know which tasks a new employee should be assigned.

For this project, you’re asked to develop an algorithm that can predict an individual’s personality. Approaches that can be taken include using the OCEAN model or classification of customers based on their purchases from a store and other information.

How to Start:

This article shows how the OCEAN model can be combined with machine learning to classify individuals. You can also attempt this take-home assignment on user behavior from IQ.

23. Treatment Policy Recommendation

The use of AI to support clinical decision-making can improve patient outcomes in healthcare facilities. A machine-learning algorithm could be used to recommend interventions at different stages of patient care to accelerate decision-making.

The aim of this project is to develop a deep learning algorithm that can recommend which interventions should be made at different stages when provided with patient information.

How to Start:

This experiment shows how one group used batch reinforcement learning to create a system to support clinical decision-making.

24. Self Driving Car

One of the main goals of artificial intelligence today is enabling true self-driving cars. Self-driving cars are held back by a myriad of issues, including AI’s lack of knowledge that is intuitive to people.

The goal of this project is to introduce you to how AI can be used to achieve certain features in self-driving vehicles.

How to Start:

This repository contains information on how a self-driving car can be developed and tested in a simulator.

25. Conversational Voice-Based AI Chatbot

Conversational AI can handle conversations with a real human being without it being apparent the person is speaking to a bot. This can be very useful in customer support and can also be used to create highly effective intelligent assistants.

The objective of this project is to develop a chatbot that can handle voice-based queries and issue responses that mimic human speech.

How to Start:

In this article, Microsoft’s Daniel Amini briefly explains how he harnessed ChatGPT to create a conversational AI. You can also access the source code for his chatbot project from this repository.

26. Human vs Bot Language Classifier

There are two types of text strings split into two files: human.txt, and machine.txt Please note: Each line is considered a unique data point. Using these two sets of data, make a model to classify the two sets, i.e. given a new point, it will determine which is the appropriate label. You can use any combination of techniques and/or metrics that you like.

How to Start:

At the end you will be asked to submit your code and a presentation. Again, this can be in any format that you like. If you feel most comfortable combining the presentation with the code, then a single Jupyter notebook is completely acceptable. We will mainly be interested in justifications for choices of method and metrics as well as any analysis that guided these choices. Final model accuracy will not play a strong role in our assessment, but the code itself will be considered for the decision to move to the final round.

27. Spam Article Classifier

The team has asked that you prepare a 20 to 30-minute presentation (in this notebook) on one of the topics from below. The purpose of this exercise is to demonstrate your ability to draw insights from data, put insights in business-friendly format and confirm coding knowledge. These topics are similar in nature to projects we run. Please make sure that your presentation is accessible to a general technical audience (aside from the sections of code, of course). 

How to Start:

We send news articles to our researchers to stay up-to-date on technology. Lately our news feeds have been inundated with spam (pure advertisement) articles. We want to identify and eliminate these articles from informative ones. How well can we classify articles as spam or valid?

We webscraped these sources for you but you are welcome to scrape your own if you need to improve results:

• List of sites already blacklisted are included in blacklist.json
• Valid news articles are included in scraped_articles.json

How can I choose a good AI project idea?

Pick an AI project idea that is both feasible and valuable. The project should have a good chance of achieving its stated objectives and a sound business case or social impact. For beginners, it can also be a project that introduces you to essential concepts and tools in AI.

Which programming language should I use for AI Projects?

Programming languages such as Python, R, and C++ can all be used for AI projects. The specifics of the project, the skills of your team, and the availability of relevant compatible tools are what will ultimately dictate which language will be better suited for a specific project.

Python’s ease of use may offer beginners a more accessible entry point into the world of AI.

How will I know if my project is a success?

There are KPIs you can use to determine how successful your project is e.g., model accuracy. You can also allow others to test your model and provide feedback.

Taking on an AI project is an excellent way to sharpen your skills in the rapidly evolving world of artificial intelligence. It also gives you skills and projects you can add to your resume and use to impress during your next interview.

At Interview Query, our goal is to prepare you for your next big opportunity. We offer access to interview questions and Company Interview Guides. You can also get information on salaries for these positions.

Whether you’re a beginner or a seasoned expert in AI, we hope our list of project ideas will help you hit significant milestones in your journey to becoming an AI expert this year.