10 Data Science Project Ideas for Middle School Students

Data Science is one of the best examples of a multidisciplinary field of study that has limitless applications. It combines various rules and practices across mathematics, statistics, artificial intelligence, and computer engineering. As a middle school student, you might find yourself fascinated by the prospect of exploring the theory and applications of big data. 

Nonetheless, taking the first step into data science can be intimidating. To help you get started, we’ve compiled a list of 10 data science project ideas for middle school students. You can undertake most of these as a beginner in middle school, with some coding experience while a solid background and interest in mathematics and statistics will also be helpful. 

You can pursue them through Veritas AI’s Junior AI Fellowship program, a 1-1 mentorship program designed specifically for middle school students to create unique, personalized AI projects! 

Project Idea 1: Movie Recommender System

You might be familiar with over-the-top (OTT) streaming platforms like Netflix, Amazon, and Hulu. Have you wondered how the platforms provide suggestions for the movies and TV shows that you should watch next? Well, the short answer is machine learning-powered recommender systems. 

Machine learning algorithms in recommender systems can be broadly classified into two categories: content-based systems and collaborative filtering systems. Most of the most modern recommender systems utilize a combination of both. MovieLens dataset from GroupLens will be used for this project.

You will get to understand the basics of data analysis and machine learning algorithms. The main learning objective that you would’ve achieved by the end is understanding Matrix factorization, which is one of the most sought-after machine learning recommendation models. 

Level of knowledge required: Beginner to Intermediate

Skills required: Basic understanding of algorithms, machine learning, and data analysis

Coding background required: Introductory Python

Potential drawbacks: Cold-Start Problem - If you are building a brand new recommendation system, you do not have the user data to start with, limiting the applications of the collaborative filtering approach. You will need to use content-based filtering first, before moving on to the collaborative filtering approach.

Project Idea 2: Language Detector

In an increasingly collaborative world, you are bound to come across useful data that is not necessarily available in English or any other language of your choice. When dealing with huge sets of data from overseas, having a language detector at your disposal will come in handy to classify the same for further processing. 

One of the real-world applications is spam detection. Using a language detection service, you will be able to highlight languages that are used in a text and help identify potential suspicious activity. The project will use machine learning models and you can use a small but clean data set from Kaggle for this project. 

Level of knowledge required: Beginner 

Skills required: Basic understanding of machine learning, and knowledge of Python

Coding background required: Introductory Python

Potential drawbacks: The range of the language detector is heavily reliant on the dataset used to develop the detector. Accuracy might vary based on the dataset size and quality.

Project Idea 3: Image Classification using CNN (Convolutional Neural Network)

Billions of images are shared online every day. That naturally brings with it the need to recognize and classify images into sensible groups. The project will introduce you to the concept of a Convolutional Neural Network (ConvNet/CNN), a deep learning algorithm that processes data with a grid pattern, such as images. 

It takes an image as input while assigning importance (learnable weights and biases) to various aspects/objects in the image. It learns feature engineering by itself via filter optimization, which helps it differentiate one image from the other. 

You will be using datasets like CIFAR 10, which is a dataset of labeled images representing the objects or categories you want the model to recognize. Before moving forward, you will need to preprocess images using Python and OpenCV. Using the Keras Sequential API, you can create, train, and implement your image classification model with just a few lines of code. 

Level of knowledge required: Intermediate

Skills required: Basic understanding of machine learning, and knowledge of Python

Coding background required: Introductory Python

Potential drawbacks: Training deep learning models for image recognition requires powerful hardware and significant memory resources. Accuracy might vary based on the dataset size and quality.

Project Idea 4: Fake News Detection

The rapid rise of social media has made sharing everything online easier, including news. You might find yourself getting most of your news and updates on social media, but you can’t always be sure of the content that you are consuming. Fake news is a growing menace in our society, which, if left unchecked, can lead to widespread hatred and even violence. 

By undertaking this project, you will be able to produce a fake news detector, which requires the use of advanced knowledge of Python. The process involved in developing a reliable fake news detector involves - importing libraries and datasets, data reprocessing, converting text into vectors, model training, and finally evaluation, and monitoring. 

You will also be familiarized with the concept of TFIDF-Vectorizer, a statistical formula that helps convert text documents into vectors based on the relevancy of the word. You will also make use of PassiveAggressiveClassifier, a machine-learning algorithm that helps in classifying news as fake or real. 

More details about the technical details and datasets can be found on Kaggle. 

Level of knowledge required: Advanced

Skills required: Advanced understanding of machine learning, statistics, and knowledge of Python

Coding background required: Advanced Python

Potential drawbacks: It has a potential for bias based on the dataset used. Limited to a single language model. Accuracy might vary based on the dataset size and quality.

Project Idea 5: Sports Analysis - NBA Players’ Salary Prediction

NBA players are some of the best-paid athletes in the world. Every season, the discussions around salary negotiations assume significance for each team. For this project, you will predict the salaries of players signing a new contract next season using data only from the previous season.

Data for the project can be obtained using BRScraper, a Python package that allows scraping and easy access to basketball data from Basketball-Reference. After cleaning and preprocessing the data by handling missing values, dealing with outliers, and encoding categorical variables (e.g., team, position) into numerical form, you will need to select the features for each player and the target, which in this case is the player salary. 

You will be using different regression models like - random forest to process the data and the performance of each one of them can be evaluated using the root mean squared error (RMSE) and the coefficient of determination (R²).

This explanation on Medium will help you understand the concepts mentioned above in great detail. 

Level of knowledge required: Intermediate

Skills required: Understanding of machine learning,  statistical modeling, especially regression analysis, and knowledge of Python

Coding background required: Advanced Python

Potential drawbacks: Factors like player marketability, team chemistry, and injury history are difficult to quantify and can be a huge factor in influencing salaries. 

Project Idea 6: Patient Stroke Risk Prediction 

The risk of non-communicable diseases is on the rise, and with lifestyle changes recently, cardiovascular diseases are an ever growing threat. Healthcare professionals will greatly benefit if they can accurately predict at-risk patients, which will allow for the proper allocation of resources and help in the creation of a preventative plan for them. 

You will be working with the Electronic Health Records (EHR) of patients to develop a machine-learning model that accurately predicts the likelihood of a stroke in individuals. You will also be expected to identify key risk factors for stroke and suggest preventive measures that should be adopted to reduce or delay the onset of a stroke.

The dataset that you’ll use in this study can be obtained from Kaggle and comprises electronic health records released by McKinsey & Company. You will undertake Exploratory Data Analysis (EDA) to find any inconsistencies in the data and for visualization of the dataset.

Finally, you will make use of logistic models, random forests, and decision trees to develop a predictive model for stroke risk using the processed dataset, providing an even distribution of stroke and no-stroke cases. This explanation on Kaggle will help you understand the basics of modeling data for stroke cases. 

Level of knowledge required: Intermediate

Skills required: Understanding of machine learning, statistical modeling, and knowledge of Python.

Coding background required: Advanced Python

Potential drawbacks: There is a risk of bias being incorporated in the dataset based on race, ethnicity, socioeconomic status, and geographic location. Feature engineering, which is used to select to identify risk factors, is a time-consuming process and requires advanced coding knowledge. 

Project Idea 7: Sentiment Analysis - IMDb Movie Reviews

Sentiment Analysis allows you to create a model that determines whether the data available is positive, negative, or neutral. To gain more insights into the topic and also gain hands-on experience, you can undertake a simple sentiment analysis of IMDb movie reviews, using a dataset that contains only positive and negative reviews. This project will involve using machine learning and Natural Language Processing (NLP) to determine the sentiment contained within the review. 

You’ll start by using this dataset, which has already labeled 50,000 positive and negative reviews. You’ll need to do a bit of data cleaning and preprocessing, as your model will only evaluate characters from the English alphabet, you need to remove any special characters, and pandas DataFrame will come in handy for doing that. 

After all the data is processed and normalized, you will use the Bag of Words model, which converts text into numerical data for natural language processing in machine learning. It makes use of the process known as vectorization -  the process of translating words into numbers. 

Level of knowledge required: Intermediate

Skills required: Understanding of machine learning, statistical modeling, and knowledge of Python

Coding background required: Introductory Python

Potential drawbacks: The output at the end of the project will only help classify reviews on a binary system, positive or negative. It does not provide in-depth information about the movie in general. The dataset used for the project is only useful for understanding the basics of sentiment analysis in movie or TV show reviews. 

Project Idea 8: Credit Card Approval Prediction

Credit card issuance is one of the most common functions that a bank undertakes daily. The bank has the authority to determine whether or not the consumer’s application for a credit card will be approved or not. How does it make the decision? It takes into consideration the credit score of the applicants which comes in handy to estimate future bankruptcies and credit card loans.

What you’ll be doing in this project is developing a machine-learning model using PyCaret library, to determine if the application is approved or rejected. Applicants will need to be classified as ‘good’ or ‘bad’, which will, in turn, determine the status of their application. You can use the cleaned and processed dataset from Github. You will conduct EDA to gain insights into the dataset, identify patterns, and understand the relationships between different features and the approval status.

This Kaggle post provides valuable insight into the steps you will need to follow to create your credit card approval predictor. 

Level of knowledge required: Intermediate to Advanced

Skills required: Understanding of machine learning, statistical modeling, and knowledge of Python

Coding background required: Introductory Python, familiarity with PyCaret

Potential drawbacks: Credit card data is prone to inconsistencies and may require advanced knowledge of machine learning to process it. Integrating predictive models into existing credit approval workflows and decision-making processes can be challenging.

Project Idea 9: Road Lane Detector

The idea of self-driving cars has always fascinated humankind. You might’ve wondered what goes behind the technology that has allowed Tesla to bring out the much-talked-about Tesla Autopilot feature in their cars. While the technology that goes behind developing a fully functional self-driving car is way too advanced, you can start by creating an algorithm that helps cars automatically detect lane lines. 

You will be using the technique used as the Hough transform, to build a software pipeline for tracking road lanes using computer vision techniques. You’ll also need a bit of coding knowledge as you will develop the model using Python and OpenCV. OpenCV refers to "Open-Source Computer Vision", which is a package that has many useful tools for analyzing images. You can use the dataset from Github. 

You'll also be familiarized with the Canny edge detection algorithm, which is a multistage process that helps detect the edges in an image by reducing noise and retaining important edge features. This Kaggle post shares a detailed step-by-step guide to understanding the lane detector from the basics. 

Level of knowledge required: Intermediate

Skills required: Understanding of machine learning, knowledge of Python and OpenCV

Coding background required: Introductory Python, familiarity with OpenCV

Potential drawbacks: The model is developed to predict lanes under ideal lighting conditions and situations where road lanes are marked diligently. It does not account for varying weather conditions and situations when the road might be covered by debris, snow, rain, or traffic cones.

Project Idea 10: World University Rankings Analysis

Deciding which institute to apply to for your higher education is one of the most important decisions that you, as a student, will make in your life. Multiple institutes release an annual rankings report for world universities. For this project, you need a wide set of data to deliver a robust analysis of university rankings. 

Thus you will be making use of data from the Times Higher Education World University Rankings, Center for World University Rankings (CWUR), and the Shanghai Academic Rankings for World Universities (ARWU). All the datasets that you will be using in this project can be accessed on Kaggle. You will be performing an exploratory analysis of the data from the three reports to understand the relationships between different variables and university rankings.

Using feature engineering you will identify the key features or factors that contribute to a university's ranking. You will develop a model using logistic regression, Support Vector Machines (SVM), and random forest using which universities can be ranked. 

Level of knowledge required: Intermediate to Advanced

Skills required: Understanding of machine learning, statistical modeling, and knowledge of Python

Coding background required: Advanced Python

Potential drawbacks: University rankings often involve subjective or qualitative factors, such as reputation, which can be challenging to quantify and incorporate into predictive models.