Data science and machine learning projects offer a host of career opportunities in 2025. Data science beginners and professionals can grab this opportunity to initiate or upskill their careers. GitHub repositories are an innovative and collaborative space for data scientists, both aspiring and seasoned. It is a code-hosting platform that simplifies version control and attracts individual users, educators, data science students, and organizations to build a community and collaborate.
GitHub is one of the best learning portals, offering inspiration, user-friendliness, support for private or public repositories, and an amazing community. In this blog, I will discuss some beginner-friendly and advanced data science projects on GitHub for those of you looking to have fun with data science.
Top 5 Data Science Projects on GitHub for Beginners
1. The Enron Email Dataset
The first GitHub data science project that is beginner-friendly is learning about the Enron Email Dataset. By exploring this, you will get a basic idea of standard data science tasks.
Problem Statement
This project involves surveying the internal communications email dataset from the Enron Corporation. This organization is known worldwide for its massive corporate fraud that led to its bankruptcy. In this case, you will be finding patterns and classifying emails to detect any fraud.
Dataset
As per the data, this dataset is from Enron Corporation. A huge dataset consisting of over 600K emails from the Enron Corp employees. This offers an opportunity to all aspiring data scientists to explore one of the largest corporate fraud cases, the Enron Fraud, by understanding patterns in the data of the company.
Step-By-Step Guide
- To acquaint yourself with the Enron dataset, clone the original repository. At this step, review the dataset or any available documentation, know the data types, and trace the elements.
- Once your initial analysis is over, get on to data preprocessing. Since it is a massive dataset, there will be unnecessary elements that will require data cleaning. There might also be missing values that you need to work around.
- Now you must execute exploratory data analysis (EDA), which may require you to develop visualizations to better understand the data distribution.
- You can perform a statistical analysis to understand correlations between data elements or anomalies.
For an in-depth exploration of the Enron dataset, you can check out GitHub.
2. Credit Card Fraud Transactions
As a GitHub data science project for beginners, studying credit card fraud detection is a great area. This will help you practise and perfect your identification skills for data patterns and anomalies.
Problem Statement
This project is all about identifying patterns in data containing information about credit card transactions. The results will provide you with common patterns or aspects that all fraudulent transactions possess.
Dataset
You have the freedom to choose any credit card dataset of your choice. For instance, you can take credit card transactions of all Europeans from 2013’s September. This dataset consists of 284,807 total transactions, out of which 492 are fraudulent transactions. The dataset is linked here.
Step-By-Step Guide
- To understand the structure, start with data exploration. Then, find any missing values in the dataset, working with the Pandas library.
- After understanding the dataset, pre-process data, manage the missing values, and remove unwanted variables. Use feature engineering to create new features.
- Now you need to train the machine learning model by considering various algorithms, such as random forests, SVM, regression, and others. You can refine them to get the best results.
- Monitor its performance on different metrics, like recall, F1-score, etc.
Find the GitHub link to the repository that will help you understand credit card fraud detection.
Also Read: AI in Jobs & Workplace – The Workplace Automation
3. Sentiment Analysis of X’s (Twitter) Data
Twitter’s aspects make it one of the most popular platforms for extensive data. This makes it a great source for beginners to practice data science and machine learning tasks on GitHub.
Problem Statement
Modern times require a comprehensive sentiment analysis of online posts by Twitter users. This project uses NLP (natural language processing) to monitor and analyze sentiments behind online posts on Twitter.
Dataset
This GitHub data science project requires you to collect data from Twitter using Python, MySQL, Streaming Twitter API, and Tweepy. After that, you need to analyze the sentiments to detect any specific opinions or emotions. This detection can help organizations to understand customer sentiment, make informed decisions on customer engagement, and improve their experience.
Step-By-Step Guide
- Use the Twitter API to collect specific data based on tweets, users, or keywords. After collection, eliminate unrequired noise and others, like irrelevant special characters.
- You can omit stop (words without value) words, like ‘and’, ‘or’, etc. Also, you can conduct lemmatization, which means converting different forms of a single word into one form. For example, ‘roll’, ‘rolling’, and ‘rolled’ come under one lemma ‘roll’.
- The next step is tokenization, i.e., you need to break the data into smaller units of individual words or tokens.
- After tokenization, using a machine learning model, you need to classify the sentiment of each token. For this step, use Naive Bayes, RNNs, or others.
You can check out this GitHub repository for analyzing Twitter data sentiments.
4. Medical Diagnosis Using Deep Learning
As deep learning has a high computational capability, it is popularly used in complicated applications. Therefore, if your GitHub data science project incorporates deep learning, it will be great for your data analyst portfolio.
Problem Statement
The goal of this GitHub project is to recognize different pathologies in chest X-rays utilizing deep-learning convolutional models. With this project, you will get an overall idea of how ML and deep learning are used in radiology.
Dataset
You will work with chest X-rays and utilize the GradCAM model interpretation method to analyze over 15 different kinds of pathologies, Edema, Cardiomegaly, etc. You will aim to use deep learning-based DenseNet-121 models for categorization.
For this project, you will work with a public dataset of chest X-rays from over 32,700 patients with over 108,948 frontal view X-rays. You will also require a subset of roughly 1000 images.
Step-By-Step Guide
Start by downloading the dataset and pre-processing it to normalize pixels, resize images, and other required things. Once done, your data is ready for training.
Now use PyTorch or TensorFlow to train DenseNet121, a deep learning model.
Using DenseNet121, you can detect the pathology and any other underlying issues.
Also, you can examine the F1-score, accuracy, and precision metrics of your model. With accurate training, the model can achieve results in accuracy as high as 0.9 (the ideal being closer to 1).
You can use this GitHub repository to assist you with medical analysis using deep learning.
Also Read: The Dark Side of Deep Learning: Risks & Limitations
5. Categorization of Music Genre with Machine Learning
It is one of the most interesting GitHub data science projects for beginners using machine learning. As it is all music, this project can be quite challenging, as getting the right dataset can be very time-consuming.
Problem Statement
The sole purpose of this project is to educate you on working with non-standard data types like musical data. Additionally, you will learn to categorize this data type based on varied aspects.
Dataset
This project requires collecting musical data and using it to test and train machine learning models. Unfortunately, musical data can be subject to copyright, so we use the Million Song Dataset (MSD) to make things easier. This free dataset consists of metadata and audio features of over a million songs belonging to different categories, like Pop, Hip-Hop, Rock, Classical, Country, etc. You will also require a music streaming platform for the ‘sounds’.
Step-By-Step Guide
- Collect the vast music data and preprocess it. This can be done by transforming audio files into feature vectors that can be utilized as input.
- Now, you must study the data to understand features, like pitch, frequency, etc. For this study part, you can use the Mel Frequency Cepstral Coefficient method, rhythm features, and others. You can categorize the songs later on using these features.
- Select a suitable machine learning model, and it could be CNN or multiclass SVM. This selection should be based on your dataset’s size and preferred accuracy.
Use this GitHub repository to help you segment customers.
Top 5 Advanced Data Science Projects on GitHub
1. Detecting Fraudulent Transactions
In this GitHub data science project, you will be using the machine learning model to determine whether a transaction is fraudulent or not.
The steps involved are:
- The first step is to collect and pre-process the data. Once done, you will move on to perform descriptive statistics operations, median, mode, skewness, standard deviation, etc.
- The next step is feature engineering, which requires a mind map that assists in developing new features, enhancing exploratory data analysis.
- You will be filtering the data to remove any unneeded columns and rows that are not part of the business.
- To understand the data, you will conduct univariate analysis, bivariate analysis, and multivariate analysis.
- Prepare the data and transform it for ML modeling by encoding, oversampling, and rescaling.
- To decrease model overfitting, you must perform dimensionality reduction of the dataset.
- Now, train the ML algorithms to accurately predict the data.
- To enhance the overall score and model performance, fine-tuning the hyperparameters is required.
- Finally, the model is tested and its performance is analyzed using unseen data.
- Create the Flask API, save the model and functions to implement in the API.
To learn in more detail about this project, use this GitHub link.
2. MediaPipe Real-Time Hand Tracking
One of the newer technological innovations, MediaPipe, is Google’s open-source framework that’s used to process media. As it is cross-platform, it can run on Android, iOS, YouTube servers, and the web.
This is a Python-based project that uses MediaPipe to track real-time hand landmarks from webcam feed data. It offers an interactive interface for hand landmarks visualization and also shows the index fingertip with a highlighted circle for enhanced visibility.
For further information about this project, click on this GitHub link.
3. Detection of Rotten Fruits (DRF) With Python’s Image Processing
Fruits and vegetables are always preferred to be fresh and vibrant. No consumer wants rotten, insect-infested, and decayed fruits and vegetables. To make the availability of fresh produce possible, an accurate fruit-detecting system is needed.
The Detection of Rotten Fruits (DRF) is a desktop program that utilizes the power of artificial intelligence and computer vision to help farmers and vendors identify diseased or low-quality fruits.
To get on with DRF, you can use this GitHub source code.
4. Prediction of Heart Stroke
To aid the needs of modern healthcare, this data science project is developed to determine a person’s probability of suffering from heart failure or stroke. For this, the users add personal and health data to the medical device. Based on this data, the application predicts the probability of heart failure. The model then uses this information to show a detailed patient status. Additionally, this model provides users with possible precautionary measures and suggestions to visit a healthcare professional for further help.
This application is useful for:
- Healthcare institutions, like hospitals and clinics.
- Healthcare professionals.
- Medical devices.
The application utilizes data ingestion to get data from user inputs. To accurately predict any heart issues, it uses pipeline retraining for retraining the model.
Use this GitHub link to learn in detail about this data science project.
5. Face Mask Detection Using an Artificial Network
Ever since the world was hit by the COVID-19 pandemic, wearing face masks in public has become a crucial aspect of human life for enhanced personal safety and disease control. Today, Artificial Network in Face Mask Detection is utilized to determine whether a person is wearing a face mask or not.
To carry out this identification, the software can be paired with any new or existing IP mask detection cameras. The application users can upload faces and phone numbers to get notified when someone nearby is not wearing a mask. Also, the administrators can be notified in case the camera records an unidentified person.
To get first-hand experience of this GitHub data science project, click here.
Why Are Data Science Projects So Useful Today?
Data science is ever-evolving; its applications are becoming more diverse and impactful in today’s global business and general human well-being. It constantly adds value to our everyday life, whether by surveying new datasets or creating more complex models.
This inclusive environment has attracted more people to take data science up as a profession. GitHub is a comprehensive platform for data science beginners and professionals to build a community or join an existing one. As an aspiring data scientist or an existing professional, you can use the GitHub Machine Learning projects to display your project or learn from others’ work. By going through the aforesaid list of top data science projects, you can explore varied applications and limitations. Use them to gain in-depth knowledge on data science workflows, like data pre-processing, exploration, visualizations, and modeling.
