10 Data Science Project Ideas for High School Students

If you’re in high school and are fascinated by the possibilities of big data, then you should consider pursuing a project in the field of data science.

Pursuing an independent project will help you build key skills like critical thinking, creativity, coding, data interpretation, visualization, and of course greatly enhance your domain knowledge. 

In this blog post, we’ve shared a list of 10 great data science project ideas. You can pursue most of these as a beginner in high school, with some amount of coding experience and a solid background in mathematics and statistics. You can also pursue them through Veritas AI’s AI Scholars or AI Fellowship Programs. 

If you are a true beginner and just getting started, here’s our guide on ways to learn data science. We’ve also shortlisted programs and competitions for you. 

Project Idea 1: Predicting House Prices

You can develop models to predict house prices based on various features such as location and number of bedrooms. It is a simple yet doable project that allows you to apply regression techniques to real estate data.

You can find datasets on platforms such as Kaggle (here is a dataset you can use!), UCI Machine Learning Repository or government housing data sources. 

What you will need to do
- Collect data from housing datasets like the Ames Housing dataset or the Boston Housing dataset.
- Clean and reprocess the data. This involves handling missing values, dealing with outliers, and encoding categorical variables.
- Analyze the data, using R, Stata, or MS Excel to identify features that influence house prices.
- For building ML models you can use Python libraries such as xgboost or scikit learn.
- Feature engineering: select the most relevant features (variables) for your model. You may also create new features that could potentially improve your model's performance.
- Choose the right model: Linear regression is typically a good starting point for beginners.
- Develop a predictive model using regression techniques to estimate house prices.

Prior knowledge required
- Basic understanding of the real estate market and factors influencing house prices.
- Proficiency in data analysis and regression techniques.

Potential drawbacks
- The complexity of accounting for all factors that influence house prices.
- Requires access to up-to-date and comprehensive housing datasets.

Why this project is good for beginners
Data science in the real estate sector is still in its nascence, and a project like this will allow you to potentially come up with a unique method of predictive modeling for this field.

Who will benefit from this project
Real estate agents, homebuyers, and policymakers looking to understand the dynamics of the housing market.

Project Idea 2: Analyzing Sentiment of Social Media Content

You can analyze social media content to gauge public opinion on various topics. Using Natural Language Processing (NLP) techniques, you can classify sentiments as positive, negative, or neutral and identify key themes within the data.

This could include features like deep-diving into how influencers build follower bases, factors they have in common, or the shared characteristics of internet trolls and cyberbullies - there are a lot of possibilities to branch out!

What you will need to do
- Collect data from social media platforms.
- Use NLP techniques to analyze the data.
- Create visualizations to represent the findings.

Check out Textblob to analyze social media language. While it is beginner friendly, it may not always be the most accurate. It’s a good starting point, though!

Prior knowledge required
- Basic understanding of programming languages such as Python. (Note: here’s our guide on resources to learn Python.)
- Fundamental understanding of Natural Language Processing.

Potential drawbacks
- Data privacy and ethical considerations - you need to be careful while finding and utilizing aggregated data to make sure that it cannot be used to identify individuals.
- The complexity of accurately interpreting sentiments - while the data will be straightforward, the real challenge will be encountered in interpreting and visualizing it without bias.

Why this project is good for beginners
It offers hands-on experience in data analysis and natural language processing, which are fundamental skills in data science. Remember the cyberbully example we gave earlier? That’s best done using more advanced methods. Here’s a blog we loved where someone has covered this comprehensively!

Who will benefit from this project
Marketing agencies, businesses, and policymakers looking to understand public opinion on various topics. There are companies that create simpler, usable versions of these tools to help sales and marketing teams in other companies measure the efficacy of marketing campaigns.

Project Idea 3: Using AI in Healthcare Applications

You can apply AI to analyze medical images for the detection of various medical conditions. This project involves exploring the scope, development, and ethics of deep learning models in enhancing the efficiency of the diagnostic process. You could analyze medical images to find early signs of illnesses, and inconsistencies in the bloodstream, and try and classify the origin of these inconsistencies

These projects can also be executed at a bunch of different levels of expertise, but to start with, you might want to look at pathology classification for medical images. Veritas AI alumni have previously worked on similar projects to classify the origin of blood clots, to analyze neuromuscular systems, and predict breast cancer from genes. 

What you will need to do
- Develop deep learning models for analyzing medical images.
- Test the models with real medical images and make necessary adjustments for accuracy.
- Explore the ethical considerations of using AI in healthcare.

Prior knowledge required
- Basic understanding of AI and Computer vision.
- Knowledge of medical imaging and healthcare.

Potential drawbacks
- Requires access to medical images for testing - this can be requested at a nearby university with a medical faculty, a hospital, or a clinic.
- Ethical considerations regarding the use of AI in healthcare - this is a highly sensitive topic intersecting with philosophy and morality.

Why this project is good for beginners
It offers an opportunity to work on cutting-edge healthcare technology and develop solutions with real-world benefits. The pathological classifications can be performed with basic computer vision and machine learning knowledge. You can access plenty of open-source resources to help you perform the analysis. There has also been a lot of previous projects similar to this, so you won’t be going in blind, and will have some context as to what you’re supposed to do.

Who will benefit from this project
Healthcare providers and patients looking to enhance the efficiency of the diagnostic process through AI. It also helps make sense of vast amounts of data generated by medical devices.

Project Idea 4: Predicting Analytics for Retail Sales

You can develop a predictive analytics model to help retailers forecast sales and optimize inventory management. By analyzing historical sales data such as customer trends, sales channels and which categories of products are performing well, you can help retailers make data-driven decisions. 

You can also help policy-makers make better decisions on health, social benefits and a whole lot more (if you are able to collect data across retailers and stores).

What you will need to do
- Collect and analyze historical sales data.
- Develop a predictive analytics model using machine learning techniques.
- Test the model with real data and make necessary adjustments for accuracy.

Prior knowledge required
- Basic understanding of data analysis and machine learning.
- Knowledge of programming languages such as Python.

Potential drawbacks
- Requires access to large datasets of sales data.
- The complexity of developing an accurate predictive model.

Why this project is good for beginners
It provides an opportunity to work on a project with real-world applications, helping retailers optimize their operations through data analysis. Moreover, you get a chance to work with big datasets and create models that can be replicated for other projects across various industries.

Who will benefit from this project
Retailers looking to optimize inventory management and forecast sales more accurately and public policy decision makers.

Project Idea 5: Predicting Forest Cover Type 

In this project, you will develop models to predict the forest cover type for a given area based on various cartographic variables. This is not a project for an absolute beginner because you will do a fair bit of exploratory data analysis, feature engineering and get into some machine learning. However, if you have some programming and data analysis experience and a longer timeline for a project, then this is a good choice. It involves the use of machine learning techniques such as decision trees, k-nearest neighbors (KNN), or support vector machines (SVMs).

Note: What type of machine learning concepts might you need?

A basic understanding of machine learning concepts like supervised learning (classification in this case), feature engineering, model evaluation, and hyperparameter tuning should be good enough!

What you will need to do
- Collect data from the UCI Machine Learning Repository's Forest Cover Type dataset.
- Perform data pre-processing and exploratory data analysis (EDA) to understand the data better.
- Develop a predictive model using suitable machine learning techniques to predict forest cover types.

Prior knowledge required
- Basic understanding of forestry and environmental science.
- Proficiency in data analysis and machine learning techniques.

Potential drawbacks
- The complexity of developing an accurate predictive model for forest cover types.
- Requires a deep understanding of the various cartographic variables involved.

Why this project is good for beginners
It offers an opportunity to work on a project with environmental implications, which still remains one of the few fields where there’s still tremendous scope for discovering new applications and data interpretation methods.

Who will benefit from this project
Environmentalists, foresters, and policymakers looking to understand and preserve forest ecosystems.

Project Idea 6: Predicting Air Quality in Your Hometown

You can try to predict air quality indicators, such as AQI, in different locations through predictive modeling. It involves analyzing historical data and meteorological inputs to develop predictive models using techniques like regression analysis and time series analysis.

If you are a true beginner, start with a simplified version of the project, such as predicting air quality based on historical data without considering future forecasts. Try to utilize existing resources instead of reinventing the wheel. Look for open-source libraries that can help guide you through the process of air quality prediction.

What you will need to do
- Collect data from sources like the U.S. EPA Air Quality System (AQS) or other environmental protection agencies worldwide.
- Analyze the data to identify patterns and trends in air quality over time.
- Develop predictive models to forecast air quality indicators.

Prior knowledge required
- Basic understanding of environmental science and air quality indicators.
- Proficiency in data analysis and predictive modeling techniques.

Potential drawbacks
- The complexity of predicting air quality indicators with high accuracy.
- Requires access to comprehensive and reliable data sets on air quality.

Why this project is good for beginners
It offers a forward-looking project that challenges you to apply data modeling techniques to one of the most pressing environmental issues of our time.

Who will benefit from this project
Environmental agencies, policymakers, and researchers looking to understand and address air quality issues in the future.

Project Idea 7: Identifying Plant Diseases 

You can develop a model capable of identifying various plant diseases based on leaf images. It involves using Convolutional Neural Networks (CNNs) for image classification to analyze the PlantVillage dataset, which contains images of healthy and diseased plant leaves. You could identify these diseases from features like the color of the leaves, the texture and the shape of the leaves in plants. 

Note: You’ll see variations of these projects being undertaken in many science fairs! You can take a look at this blog to see how a student worked on a similar project!

What you will need to do
- Collect data from the PlantVillage dataset available on public platforms.
- Use CNNs to analyze the images and identify patterns related to plant diseases.
- Develop a predictive model to identify various plant diseases based on leaf images.

Prior knowledge required
- Basic understanding of plant pathology and image processing.
- Proficiency in data analysis and machine learning techniques.

Potential drawbacks
- The complexity of developing an accurate image classification model for plant diseases.
- Requires access to a comprehensive dataset of plant leaf images.

Why this project is good for beginners
With food security under threat globally, and the dwindling quality and availability of farmland, research like this can be helpful towards making farming safer and yields more reliable. There is also plenty of previous research available on this topic, so you have some context on where to start with a project like this. 

Who will benefit from this project
Farmers, agricultural researchers, and policymakers looking to address plant diseases more effectively.

Project Idea 8: Classifying Art Styles 

You can attempt to classify paintings into different art styles such as renaissance, baroque, and iImpressionism using features extracted from images. You’ll create and train a model to identify art styles. This is not for an absolute beginner.

If you want to create a basic model that can distinguish between just a few art styles, it might be more manageable for you as a beginner. Remember that you’ll also have to tweak confusions out of your models so it’ll be a deep-dive for you into the art styles as well. 

You can use the fast.ai library for this which works on top of PyTorch.

What you will need to do
- Collect images from public domain art datasets like WikiArt.
- Extract features from the images using Convolutional Neural Networks (CNN).
- Feature engineering: select the most relevant features (variables) for your model. You may also create new features that could potentially improve your model's performance.
- Develop a classification model to categorize paintings into different art styles.

Prior knowledge required
- Basic understanding of art history and different art styles.
- Proficiency in Python and data manipulation.
- Proficiency in image processing, CNNs and other machine learning techniques.

Potential drawbacks
- Requires access to a comprehensive dataset of art images.
- The complexity of extracting distinguishing features for different art styles.
- This requires knowledge of different art styles as well as various machine learning techniques that can prove to be a hindrance. 

Why this project is good for beginners
It offers a unique intersection of art and technology, allowing you to explore data science applications in the art world.

Who will benefit from this project
Art historians, curators, and enthusiasts looking to categorize and analyze art through a new lens.

Project Idea 9: Predicting Earthquakes in Frequently Affected Regions

You can develop models to predict earthquakes (i.e. the magnitude or how ‘destructive’ an earthquake is). This is not a novel idea by any means and significant work has already been done on it, but that doesn’t mean that you can't take existing datasets and think of more solutions or even examine the existing solutions and try to apply it to other natural calamities. It's a somewhat complex topic, but it will provide you with the opportunity to find and work on available datasets to predict seismic activities.

If this is interesting to you, look at Kaggle’s LANL earthquake prediction challenge where people use existing datasets of seismic activity to try and predict when an earthquake will happen.

What you will need to do
- Collect and analyze earthquake datasets.
- Develop predictive models using machine learning techniques.
- Validate the models using historical earthquake data.

Prior knowledge required
- Basic understanding of geophysics and seismology.
- Proficiency in data analysis and machine learning techniques.
- Proficiency with Python (it’ll help with things like understanding the CatBoost algorithm).

Potential drawbacks
- The complexity of predicting natural phenomena with high accuracy.
- Requires access to comprehensive and reliable data sets.
- You will need to be interested in and have specific knowledge of geophysics and seismology to undertake this project. 

Why this project is good for beginners
It offers a challenging yet fascinating entrypoint for data science applications in disaster management, providing you an opportunity to work on a unique, high-impact niche. Since there has already been a lot of work done on this topic, as a beginner, you get the chance to really dive deep and learn from past research and models. 

Who will benefit from this project
Seismologists, urban planners, and governments looking to enhance their earthquake preparedness and response strategies. For a long time, it was believed that predicting earthquakes is nearly impossible!

Project Idea 10: Detecting Anomalies in Credit Card Transactions

This project focuses on detecting potentially fraudulent credit card transactions. With the rise in digital transactions, ensuring the security and authenticity of each transaction is paramount. You will work with real transaction data to identify patterns that suggest fraudulent activity.

What you will need to do
- Collect data from the Credit Card Fraud Detection dataset on Kaggle.
- Analyze the data to understand the patterns of genuine vs. fraudulent transactions.
- Implement anomaly detection techniques such as Isolation Forest or One-Class SVM to identify potentially fraudulent transactions.

Prior knowledge required
- Basic understanding of financial transactions and credit card operations.
- Proficiency in data analysis and anomaly detection techniques.

Potential drawbacks
- The challenge of achieving high accuracy in fraud detection without generating too many false positives.
- Ethical considerations regarding user privacy and data security.

Why this project is good for beginners
Fraud detection is a classic data science problem, and as a beginner, the data analysis required for this is one of the first skills you will be expected to learn. A project like this will get you a solid head start. Since this is a project that has been researched before, you can refer to older materials if you need any guidance and if possible, try figuring out more efficient ways of performing the analysis.

Who will benefit from this project
Banks, credit card companies, and individuals looking to enhance the security and authenticity of credit card transactions.

Bonus project - Predicting Disease Outbreaks from News Articles (this one is not beginner-friendly!)

You can develop a model that’ll analyze news articles to predict potential disease outbreaks based on reported symptoms and locations. It utilizes techniques such as Natural Language Processing (NLP), topic modeling, and sentiment analysis. A Veritas AI student previously worked on a similar project to detect tweets relating to natural disasters using Natural Language Processing. 

What you will need to do
- Collect data from news datasets like GDELT or Kaggle's COVID-19 Open Research Dataset.
- Use NLP  to analyze the data and identify patterns related to disease outbreaks.
- Develop a predictive model to forecast potential disease outbreaks based on the analysis.

Prior knowledge required
- Basic understanding of epidemiology and public health.
- Proficiency in data analysis and natural language processing techniques.

Potential drawbacks
- The complexity of analyzing large volumes of news data accurately.
- Ethical considerations regarding the prediction of disease outbreaks.

Who will benefit from this project
Public health agencies, governments, and researchers attempting to monitor and respond to disease outbreaks more effectively.

These project ideas offer diverse options for you to get started with thinking like a data scientist. Each of these projects can be executed at various levels of expertise, but we’ve tried to keep most of them beginner-friendly, interspersed with a few slightly-more challenging options! In data science, you learn by doing and solving, so we highly recommend that you don’t skimp out on project work. 

A great way to work on a hands-on data science project is to apply to independent programs created specifically for high school students. One program you should consider is Veritas AI!

Founded by Harvard graduate students, Veritas AI is a program that teaches you the foundations of data science and AI through real-world, collaborative projects. You can also work 1-1 with mentors from universities like Harvard, MIT, Stanford, and CMU to create unique, personalized projects in data science and AI. Last year, we had over 1000 students learn AI with us. You can find the application form here!