20 Machine Learning Project Ideas for High School Students
Machine Learning is one of the most promising fields to be in at the moment. Advancements in the field are already making a difference in the world and are accessible to people around the world.
A great way to get a head start in the field is to pursue a personal project. Opting to undertake a project in your free time will not only help you gain practical, hands-on experience but will also make your college application stand out.
With that being said, here, we will discuss 20 machine learning project ideas for high school students. You will find that most of the projects only require beginner-level knowledge and you can do them even if you are just getting started with your learning journey.
1. Iris flower classification
Iris flower classification is a classic machine learning project and helps beginners gain an understanding of the classification of a dataset using machine learning. It makes use of the Iris flower data set, which was compiled by Ronald Fisher, a biologist, in the 1930s. It describes the biological characteristics of various types of Iris flowers, specifically, the length and width of both the pedals and the sepals.
You will be able to classify iris flowers among three species (Setosa, Versicolor, or Virginica) from the sepals’ and petals’ length and width measurements by undertaking this project.
Level of Knowledge Needed: Beginner
Skills Required: Machine learning, Python programming, familiarity with neural networks, and scikit-learn
Coding Background Requirements: Introductory Python programming and understanding of machine learning concepts.
Potential Drawbacks: Nothing in particular
2. MNIST Handwritten Digit Recognition
MNIST (“Modified National Institute of Standards and Technology”) dataset is a great source of handwritten pictures which is used to train classification models using Machine Learning techniques. Using this dataset you can carry out a project to classify the images of handwritten digits into 10 categories (0 through 9). This project is a great introduction to image processing in machine learning.
Level of Knowledge Needed: Beginner
Skills Required: Machine learning, Python programming, familiarity with neural networks and TensorFlow
Coding Background Requirements: Introductory Python programming and understanding of machine learning concepts.
Potential Drawbacks: You might face some issues in installing TensorFlow in your system
3. Music recommender system
Recommender systems in general, are one of the classical examples of ML, as these systems handle large volumes of data and need to give out results within seconds. Everyone likes music and online streaming platforms have millions of songs in their repositories, thus making a music recommender system would be a practical and fun project for you.
Your objective while undertaking this project is to build a model that can predict the songs a user might like using the dataset and users’ listening history alongside full information of all songs (metadata, audio content analysis, and standardized identifiers).
This report by a group of students from IIT Kanpur is a good way to approach this project.
Level of Knowledge Needed: Beginner
Skills Required: Machine learning, Python programming, familiarity with neural networks
Coding Background Requirements: Introductory Python programming and understanding of machine learning algorithms. Some knowledge of statistical models is also required
Potential Drawbacks: Requires a good amount of labeled training data and computational resources for training complex models.
4. NYC Taxi fare prediction model
New York City Taxi Fare Prediction, was a playground competition, hosted in partnership with Google Cloud and Coursera on Kaggle in 2018. While it was a competition for a limited time, it is nevertheless a great project to pursue, if you are looking to learn about linear analysis and regression on a base level as a beginner.
You can access the dataset on Kaggle and use the pickup and drop locations to predict the fare prices for a cab journey in NYC. Applications of such projects can be implemented in real time and help a user get a fair estimation of the taxi fare at any given time.
Level of Knowledge Needed: Beginner
Skills Required: Machine learning, Python programming, familiarity with neural networks and libraries like Pandas and NumPy.
Coding Background Requirements: Introductory Python programming and understanding of machine learning algorithms.
Potential Drawbacks: Requires a good amount of labeled training data and computational resources for training complex models.
5. House price prediction using neural networks
Buying a house is one of the biggest financial transactions a person makes during his life. Given the rise of property prices in recent years, it is only natural for you to feel the need for a price prediction model. Well, the good news is that you can build a house price prediction model, though not useful for real-time properties, using machine learning.
This is one of the data-heavy projects in which you will get to do everything from scratch, including data preprocessing and preparation techniques to obtain clean data and building machine learning models able to predict house prices based on house features.
This notebook on Kaggle has a wealth of information for understanding and implementing this project.
Level of Knowledge Needed: Beginner
Skills Required: Machine learning, Python programming, familiarity with neural networks, Exploratory Data Analysis (EDA), and regression techniques.
Coding Background Requirements: Introductory Python programming and understanding of machine learning algorithms.
Potential Drawbacks: It requires a good amount of labeled training data and building ML models based on regression techniques can be quite rigorous and exhaustive.
6. Stock price prediction model
The stock market is a place where many people have made their fortunes. If you have ever wondered how a stock market works and what drives the prices of a particular share up or down, you are not alone. While there are a lot of financial concepts that drive the price determination of a stock, using machine learning, it is possible to predict the future price of a stock to a certain degree of surety.
Undertaking this project will help you become familiar with the basics of deep learning and neural networks. You will use the Long Short-Term Memory Network (LSTM) model, which is useful for processing and predicting time-series data.
Level of Knowledge Needed: Beginner
Skills Required: Machine learning, Python programming, familiarity with neural networks.
Coding Background Requirements: Introductory Python programming and understanding of machine learning algorithms. Experience with Python libraries will be useful
Potential Drawbacks: Limited data and it does not account for disruptions in the market like COVID-19
7. Titanic Survival Prediction
This is again, one of the classic projects for a beginner ML enthusiast. The RMS Titanic tragedy is something that everyone has heard of and you might’ve wondered what you would’ve done, had you been present on the ship around a century ago. You will be using the Titanic dataset from Kaggle to predict the survival of passengers based on various attributes like age, sex, ticket class, and more to predict the chances of survival for a passenger.
You will be using logistic regression, which is a simple and efficient method for binary and linear classification problems, because here there are only two outcomes, whether the passenger survives or not.
Level of Knowledge Needed: Beginner
Skills Required: Machine learning, Python programming, familiarity with statistics.
Coding Background Requirements: Introductory Python programming and understanding of machine learning algorithms.
Potential Drawbacks: This is a theoretical project with no real-life applications
8. Optical character recognition (OCR) project
Optical character recognition (OCR) is not a new technology and has been in existence since the 1950s with IBM pioneering the early tech but commercial OCR was available only in the 1990s. In modern times, the conversion of images of typed, handwritten, or printed text into machine-encoded text, whether from a scanned document, a photo of a document, a scene photo, or from subtitle text superimposed on an image all falls under the umbrella of OCR.
With the advent of AI, OCR can be used to extract relevant information from millions of images online. What you will look to accomplish by undertaking this project is to use a readily available dataset to extract all texts from the images.
Level of Knowledge Needed: Beginner
Skills Required: Machine learning, Python programming, image processing.
Coding Background Requirements: Introductory Python programming, understanding of machine learning algorithms, and knowledge of TensorFlow and OpenCV.
Potential Drawbacks: The model is trained using synthetic data with English letters and numbers, so only English is supported. The model is only able to process simple texts and might give out erroneous data for images with low light conditions with the upside-down texts.
9. Plant disease prediction
Plant disease prediction can be one of the best real-life applications of machine learning and AI. The goal of the project is to create a plant disease detection model using images of leaves of various plant species and compare them with the images of diseased plants. You can access the dataset for this project on Kaggle, which contains over 87k images of three different plant species, namely Corn, Potato, and Tomato, along with the diseases that affect each group.
This project will also be a good way for you to familiarize yourself with image processing and data classification. The final goal is to figure out which plants from the given dataset have disease using a convolutional neural network (CNN), which is a category of machine learning model and a type of deep learning algorithm well suited to analyzing visual data.
Level of Knowledge Needed: Beginner
Skills Required: Machine learning, Python programming, familiarity with image processing.
Coding Background Requirements: Introductory Python programming, understanding of machine learning algorithms and Keras
Potential Drawbacks: The dataset is huge and you might need good computational resources for training complex models. The dataset only works for selected plants and in case of new plant species, you will have to start the process from the start.
10. Spam email detection project
For a beginner, classification projects are a great way to understand the basics of ML and another of the projects that works on this principle is spam email detection. Spam emails are at the top of the list of things that people hate the most about going online. Fortunately, you can develop a model to filter out spam emails, given that most of them are very similar and have the same kind of text content.
This project will involve processing all the data in two phases, firstly, you will make use of Python's Natural Language Toolkit (NLTK) to process the raw textual data. Then using Pandas and NumPy, you will classify the emails as Spam or not.
Level of Knowledge Needed: Beginner
Skills Required: Machine learning, Python programming, familiarity with Python libraries.
Coding Background Requirements: Introductory Python programming and understanding of machine learning algorithms.
Potential Drawbacks: With spam email getting ever more sneaky every day and organizations using AI to generate creative emails, this system, based on old datasets might fail in the future.
11. Language translation app
Well, this project is pretty self-explanatory, and if you can undertake this project to build a translator, which is powered by ML & AI. Even Google reported an increase in translation accuracy when it integrated deep learning into its widely used translation app and online platform.
There are thousands of languages online and even more dialects that one needs to account for when developing an online translator. Using machine learning you can build an all-in-one translator but for the sake of this project, a single-language translator will suffice. You can use this dataset from Kaggle for this project, which will help you build an English-to-French translator.
Level of Knowledge Needed: Beginner
Skills Required: Machine learning, Python programming, familiarity with Python libraries and neural networks
Coding Background Requirements: Introductory Python programming and understanding of machine learning algorithms.
Potential Drawbacks: To build an all-language translator, very large computational resources might be required
12. Fake News Detection
Fake news has been in the headlines recently, because of the potential disruptions it can cause in day-to-day activities. With the proliferation of apps like ChatGPT, it is easy to churn out fake news within seconds. While Deepfake videos are an even bigger threat to society, for the sake of the project, dealing with fake news in written form, over an online blog or a social media platform is considered, as this will be easier to undertake as a beginner.
You will be introduced to the concept of natural language processing (NLP) methods during this project. Another approach for the project is to train the model using large datasets of real and fake news, which will again utilize the classification capabilities of ML & AI.
Level of Knowledge Needed: Beginner
Skills Required: Machine learning, Python programming, familiarity with Python libraries and neural networks
Coding Background Requirements: Introductory Python programming and understanding of machine learning algorithms.
Potential Drawbacks: The dataset for training the model can be very large and the process will become tedious and very resource intensive. The model can only detect simple written statements and will fail against memes, which are used intensively.
13. Social Media Sentiment Analysis
Machine learning utilizes various algorithms and processes to ensure that a large amount of data input is converted to the desired output. Utilizing this property of ML, analysis and classification are some of the widespread applications of the technology. One of the projects in which you can apply these abilities of the technology to real life is sentiment analysis. With the amount of time people spend on social media platforms, Social media sentiment analysis has become a popular project.
This is again a text-processing project and will categorize all posts into three categories - Positive, Negative, or Neutral.
Level of Knowledge Needed: Beginner
Skills Required: Machine learning, Python programming, familiarity with Python libraries and neural networks
Coding Background Requirements: Introductory Python programming and understanding of machine learning algorithms.
Potential Drawbacks: The dataset for training the model can be very large and the process will become tedious and very resource intensive. If the post is a lengthy one, the model might struggle to give an output or give multiple results for the same post
14. Sales volume prediction
For any company, keeping track of its sales numbers is crucial, as it not only determines the profitability but also helps out in inventory management during festive periods. While sales managers can look at historical data from previous quarters to make suggestions for the inventory, machine learning algorithms not only make the task easier but using data going back multiple years, the sales volume prediction is much more accurate.
You can use a time-series model, like the one mentioned in the stock price prediction model, or use logistic regression which takes into account various factors like historical sales volume, promotional campaigns, and economic conditions to arrive at a prediction figure.
Level of Knowledge Needed: Beginner
Skills Required: Machine learning, Python programming, familiarity with Python libraries and neural networks
Coding Background Requirements: Introductory Python programming and understanding of machine learning algorithms.
Potential Drawbacks: The dataset for training the model can be very large and the process will become tedious and very resource intensive. The project has no provisions to account for events like COVID-19.
15. Wine quality prediction
Wine quality prediction is another classic project which is suitable for beginners. In this project, you will be using the dataset from Kaggle to predict win quality based on various factors that were obtained from physicochemical tests on the wine samples. They include volatile acidity, fixed acidity, residual sugar, citric acid, chlorides, free sulfur dioxide, total sulfur dioxide, pH, density, sulfates, alcohol content, and type (Red or White).
Python is the choice of language for this project and alongside various libraries like Pandas, NumPy, and SkLearn, you will also be familiarized with the eXtreme Gradient Boosting machine learning algorithm, which helps in making accurate predictions.
Level of Knowledge Needed: Beginner
Skills Required: Machine learning, Python programming, familiarity with Python libraries and data processing
Coding Background Requirements: Introductory Python programming and understanding of machine learning algorithms.
Potential Drawbacks: Nothing as such
16. Facial Expression Recognition
Processing images is slightly different from processing text, but we can undertake several projects here depending on the features that need to be extracted. In the facial expression recognition project, you will be processing images of humans with various emotions, which are assigned values for the sake of processing as follows:- (0=Angry, 1=Disgust, 2=Fear, 3=Happy, 4=Sad, 5=Surprise, 6=Neutral)
While there are multiple approaches that you can choose to pursue this project, the Convolutional Neural Network is one of the most widely used methodologies. The convolution operation allows you to save on computation power while working with images without compromising on the system’s accuracy. It uses pixel values to build a facial expression recognition system.
Level of Knowledge Needed: Beginner
Skills Required: Machine learning, Python programming, familiarity with Python libraries and data processing
Coding Background Requirements: Introductory Python programming and understanding of machine learning algorithms.
Potential Drawbacks: Output of the model and its future applications can be impacted by dubois data used to build the model. There might be ethical issues in developing this model which uses a large set of images of peoples’ faces.
17. Sign Language Recognition
Another project that involves the usage of images is to build a sign language recognizer. American Sign Language (ASL) is the predominant sign language of deaf communities in the United States and you will be undertaking this project to build a model that recognizes which of the 24 ASL Letters, excluding gesture-based J and Z, are being used in the images.
You can undertake this project using the OpenCV and Keras modules of Python. The project, like any image-based project, will have to be done in two phases. The first is the image processing while the second is training the model, which in this project will be the CNN Keras model.
Level of Knowledge Needed: Beginner
Skills Required: Machine learning, Python programming, familiarity with Python libraries and data processing
Coding Background Requirements: Introductory Python programming and understanding of machine learning algorithms.
Potential Drawbacks: Image processing is a crucial step and unless you are comfortable with using OpenCV, the model might not be able to eliminate background interferences.
18. Predicting credit card approval
Issuing credit cards is one of the most time-consuming tasks for a bank. Multiple factors can lead to someone’s application being rejected. Machine learning, with its ability to deal with a large volume of similar data points, can be used to automate the credit card approval process.
You can undertake this project using Python and this dataset from the UC Irvine Machine Learning repository. One of the first challenges that you will face is data cleaning as there might be incomplete applications which will result in missing data errors.
Level of Knowledge Needed: Beginner
Skills Required: Machine learning, Python programming, familiarity with Python libraries and data processing
Coding Background Requirements: Introductory Python programming and understanding of machine learning algorithms.
Potential Drawbacks: The dataset for training the model can be very large and the process will become tedious and very resource intensive.
19. Disease Prediction
Advancements in tech have been very useful in healthcare. Be it the use of robots in surgery or the easy access to their own data for patients. One of the latest ways in which machine learning is being utilized in healthcare is disease prediction. The system categorizes patients into the known disease dataset by using anonymous patient data, which includes various symptoms. With the dataset on Kaggle, it is possible to predict 42 different types of diseases using 132 parameters.
Level of Knowledge Needed: Beginner
Skills Required: Machine learning, Python programming, familiarity with Python libraries and data processing
Coding Background Requirements: Introductory Python programming and understanding of machine learning algorithms.
Potential Drawbacks: Nothing as such
20. Image classifier
We will finish the list with another classification project, in which you will build a model, which should be able to identify and classify a given set of images into desired classes. For this project, you will be using this dataset from Kaggle and build a simple model, using neural network algorithms to build a model that classifies images as either a cat or a dog. You can also use TensorFlow for image classification.
Level of Knowledge Needed: Beginner
Skills Required: Machine learning, Python programming, familiarity with Python libraries and data processing
Coding Background Requirements: Introductory Python programming and understanding of machine learning algorithms.
Potential Drawbacks: Nothing as such
If you’re looking to build unique projects in the field of AI/ML, consider applying to Veritas AI!
Veritas AI was founded by Harvard graduate students, and through the programs, you get a chance to learn the fundamentals of AI and computer science while collaborating on real-world projects. You can also work 1-1 with mentors from universities like Harvard, Stanford, MIT, and more to create unique, personalized projects. In the past year, we had over 1000 students learn data science and AI with us. You can apply here!
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