Learn with Data Science Projects GitHub 2025: Beginner to Pro

Data science transforms unprocessed information into valuable knowledge. The method integrates statistical analysis with computer programming to find solutions. Python serves as a fundamental tool because its straightforward learning curve enables users to access Pandas, NumPy and Scikit-learn libraries which support all stages of data processing and AI model development.   

Businesses need professionals who possess data management abilities. The job market has created a strong need for data analysts, machine learning engineers and AI specialists. Data science education enables students to find work while they acquire skills. 

In this blog, we’ll explore the best data science projects GitHub 2025. You’ll see beginner, intermediate, and advanced projects that will help you to turn theoretical knowledge into practical ones. 

Now let’s start by exploring the curated list of Beginner Data Science Projects GitHub in Python. 

For others who want more systematic learning, upGrad's Data Science Courses provide a balance of theory and practical projects, as well as mentoring by experienced faculty and industry professionals. 

Beginner Level Data Science Projects GitHub 

We will now start with the beginner data science projects GitHub first, if you have a good grasp on Python and libraries used for Data Science you are good to go. 

1. House Price Prediction 

You’ll build a machine learning model to predict house prices using features like location, size, and condition with regression algorithms. 

Tools and Technologies Used: 

• Python 
• Pandas 
• NumPy 
• Matplotlib/Seaborn 
• Scikit-learn 

Project Outcome: 
You’ll be able to estimate house prices with higher accuracy, reduce guesswork in real estate decisions, and gain hands-on experience in applying regression models to real-world data. 

Check out this Project- House Price Prediction Using Regression Algorithms 

Begin your data science journey with upGrad’s industry-aligned programs. Learn from leading experts, master essential tools and techniques, and build job-ready skills through hands-on projects and real-world applications. 

• MS in Data Science from Liverpool John Moores University 
• Generative AI Mastery Certificate for Data Analysis 
• Executive Post Graduate Certificate Programme in Data Science & AI From IIITB 

2. Wine Quality Prediction 

You will predict wine quality using the WineQT dataset and regression models based on features like acidity, sugar, alcohol, and pH. 

Tools and Technologies Used: 

• Python 
• Pandas 
• NumPy 
• Matplotlib/Seaborn 
• Scikit-learn 

Project Outcome: 
You’ll learn to apply regression for quality prediction and interpret how chemical properties affect wine scoring. 

Check out this Project- Wine Quality Prediction Model 

3. Heights and Weights 

You’ll use the Heights and Weights dataset to explore the relationship between height and weight. A Simple Linear Regression model will be trained to predict weight from height. 

Tools and Technologies Used: 

• Python 
• Pandas 
• NumPy 
• Matplotlib/Seaborn 
• Scikit-learn 
• Random Forest Regressor 

Project Outcome: 
You’ll learn how to build and evaluate a basic regression model, gaining hands-on practice in prediction and performance measurement with real data. 

Check out this Project- Analyzing the Heights and Weights Dataset Using Linear Regression 

4. Email Classification 

You’ll build a machine learning model to classify emails as spam or not spam using datasets like SpamAssassin, Enron Spam Subset, and LingSpam. The project involves text vectorization and NLP techniques for effective classification. 

Tools and Technologies Used: 

• Python 
• Pandas 
• CountVectorizer / TfidfVectorizer 
• NumPy 
• Matplotlib/Seaborn 
• Scikit-learn 

Project Outcome: 
You’ll understand how spam filters work, gain hands-on experience with Python classification models, and apply NLP methods to real-world email data. 

Check out this Project- Email Classification Using Machine Learning and NLP Techniques 

5. Speech Emotion Recognition 

You’ll build a Speech Emotion Recognition (SER) model that classifies emotions like fear, sadness, anger, and happiness from audio recordings. 

Tools and Technologies Used: 

• Python 
• Pandas 
• NumPy 
• Librosa 
• Matplotlib/Seaborn 
• Scikit-learn 

Project Outcome: 
You’ll learn how to process audio data, apply machine learning to speech, and develop a model capable of detecting human emotions from voice. 

Check out this Project- Speech Emotion Recognition Project Using ML 

6. Age and Gender Detection 

You’ll build an age and gender detection model using computer vision and pre-trained deep learning models. With OpenCV’s DNN module and Caffe models, the system predicts gender (male/female) and age ranges from facial images. 

Tools and Technologies Used: 

• Python 
• OpenCV 
• NumPy 

Project Outcome: 
You’ll gain experience in applying computer vision for real-world tasks like surveillance, interactive systems, and targeted advertising without manual model training. 

Check out this Project- Build an Accurate Age and Gender Detection Model Using Python 

7. Driver Drowsiness Detection 

You’ll build a driver drowsiness detection system using deep learning. The project uses the Dataset with 41,000+ facial images, classified as Drowsy or Non-Drowsy. Transfer learning with MobileNetV2 will be applied for efficient model training. 

Tools and Technologies Used: 

• Python 
• Pandas 
• NumPy 
• TensorFlow / Keras 
• Matplotlib 
• OS / Glob / Pathlib 
• OpenCV (cv2) 
• Scikit-learn 

Project Outcome: 
You’ll learn how to apply transfer learning in computer vision and build a real-world safety application that can detect driver fatigue from facial images. 

Check out this Project- Driver Drowsiness Detection Using Pretrained Model 

After completing these beginner level data science projects GitHub, you will build a good foundation of data science journey. Now take the next step towards the intermediate projects. 

Intermediate Level Data Science Projects GitHub 

These intermediate-level projects help you move beyond the basics and advance in your data science journey. 

8. Heart Disease Prediction 

You’ll build a heart disease prediction model using patient data like cholesterol, blood pressure, and age. Models such as Logistic Regression and Random Forest will be trained on the UCI Heart Disease dataset. 

Tools and Technologies Used: 

• Python 
• Pandas 
• NumPy 
• Matplotlib 
• Scikit-learn 
• LogisticRegression 
• RandomForestClassifier  
• Accuracy, Precision, Recall, F1-Score  

Project Outcome: 
You’ll learn to apply machine learning for healthcare, gain experience with classification models, and build a tool that supports early and accurate diagnosis. 

Check out this Project- Heart Disease Prediction Using Logistic Regression and Random Forest 

9. Breast Cancer Classification and Prediction 

You’ll build a breast cancer classification and prediction model using logistic regression. The model analyzes health data to determine whether a tumor is malignant or benign. 

Tools and Technologies Used: 

• Python 
• Pandas 
• NumPy 
• Matplotlib 
• Scikit-learn 
• LogisticRegression 

Project Outcome: 
You’ll understand how machine learning supports early detection in healthcare and gain practical experience applying classification models to medical data. 

Check out this Project- Breast Cancer Classification and Prediction with Logistic Regression 

10. IPL Match Winner Prediction 

You’ll work on an IPL match winner prediction project using machine learning. The model uses past match data such as teams, venue, toss, and decisions to predict outcomes, applying Logistic Regression in Python. 

Tools and Technologies Used: 

• Python 
• Pandas 
• NumPy 
• LabelEncoder (from sklearn) 
• LogisticRegression 

Project Outcome: 
You’ll learn how to apply machine learning in sports analytics, analyze cricket data, and build a predictive model that forecasts match results. 

Check out this Project- IPL Match Winner Prediction using Logistic Regression 

11. Bollywood Movie Analysis and Success Prediction 

You’ll analyze Bollywood movie data to study factors like genre, budget, lead actors, and release timing that influence success. A machine learning model will then be built to predict whether a movie will be a hit or flop. 

Tools and Technologies Used: 

• Python 
• Pandas 
• NumPy 
• Matplotlib or Seaborn 
• RandomForestClassifier 
• Pipeline & ColumnTransformer  

Project Outcome: 
You’ll gain experience in data analysis and classification, while learning how real-world features drive outcomes in the film industry. 

Check out this Project- Bollywood Movie Analysis and Success Prediction with Machine Learning 

12. Startup Funding Analysis 

You’ll analyze India’s startup funding data to identify patterns, clean and preprocess the dataset, and study factors that drive investment decisions. 

Tools and Technologies Used: 

• Python 
• Pandas 
• NumPy 
• Matplotlib or Seaborn 
• Scikit‑learn basics  

Project Outcome: 
You’ll gain experience in data cleaning, trend analysis, and predictive modeling while understanding how funding evolves in the startup ecosystem. 

Check out this Project- Startup Funding Analysis and Prediction: A Machine Learning Project 

13. Crop Production Prediction 

You’ll build a crop production prediction model using machine learning. The project uses features like crop type, season, state, and area, applying a Random Forest Regressor to estimate yields. 

Tools and Technologies Used: 

• Python 
• Pandas 
• NumPy 
• Matplotlib or Seaborn 
• Categorical data handling 
• Scikit-learn 
• Regression basics 

Project Outcome: 
You’ll learn how to preprocess agricultural data, train regression models, and make data-driven predictions that support food security and farming decisions. 

Check out this Project- Crop Production Prediction using Random Forest Regressor 

14. Literacy Rate Prediction 

You’ll work on literacy rate prediction using district-wise data from India. Regression models like Linear Regression, Random Forest, and Gradient Boosting will be applied to predict literacy levels based on socio-economic and demographic features. 

Tools and Technologies Used: 

• Python 
• Pandas 
• NumPy 
• Matplotlib or Seaborn 
• Regression Algorithms 

Project Outcome: 
You’ll gain experience in preprocessing large datasets, applying multiple regression models, and evaluating predictions to understand factors influencing literacy. 

 Check out this Project- Literacy Rate Prediction and Analysis with Python 

15. Indian Rainfall Analysis 

You’ll analyze historical rainfall data across India to identify seasonal trends, regional variations, and rainfall patterns. A Linear Regression model will then be built to predict annual rainfall using the first five months of data. 

Tools and Technologies Used: 

• Python 
• Pandas 
• NumPy 
• Matplotlib or Seaborn 
• Data Preprocessing 
• Regression Algorithms 

Project Outcome: 
You’ll learn to combine exploratory data analysis with regression modeling, gaining insights into rainfall trends and building predictive tools for agriculture and water planning. 

 Check out this Project- Indian Rainfall Analysis and Prediction Using Linear Regression 

16. Sign Language MNIST Classification 

You’ll build a sign language recognition model using the Sign Language MNIST dataset. The project involves classifying 24 ASL hand gesture images (A–Y, excluding J and Z) using Convolutional Neural Networks (CNNs). 

Tools and Technologies Used: 

• Python 
• Pandas 
• NumPy 
• Matplotlib or Seaborn 
• Keras/TensorFlow 

Project Outcome: 
You’ll gain hands-on experience with computer vision and CNNs, learning how to develop models that translate hand gestures into letters to aid communication for the deaf community. 

 Check out this Project- How to Build a CNN Model for Sign Language MNIST Classification? 

Finished with the intermediate projects? Now it’s time to move to the advanced level. 

Advanced Level Data Science Projects GitHub 

Take your skills further with these advanced data science projects on GitHub, designed to challenge and enhance your expertise. 

These advanced projects will push your skills to the next level, exposing you to diverse tools, complex datasets, and real-world problem-solving. 

Ever wondered why Python is used by so many techies for data science? Let’s find out. 

Why Python is the Go-To Language for Data Science 

Python has become the preferred language for data science due to so many reasons, let’s explore the advantages of using Python:

• Easy to Learn: Python has simple, readable syntax that beginners can grasp quickly. 
• Rich Libraries: Offers tools like Pandas, NumPy, and Scikit-learn for data analysis and manipulation. 
• Machine Learning Support: Integrates with TensorFlow and PyTorch for AI and ML applications. 
• Data Visualization: Libraries like Matplotlib, Seaborn, and Plotly make charting and plotting easy. 
• Large Community: Strong community support ensures plenty of tutorials, forums, and resources. 
• Integration: Works well with databases, web applications, and cloud platforms. 
• Flexible: Suitable for quick prototypes as well as production-ready solutions. 

Conclusion 

These Python data science projects GitHub span three levels of difficulty from beginner to advanced and provide practical work with actual datasets and machine learning methods. The completion of these projects will enhance your abilities while building self-assurance and crafting an outstanding portfolio that demonstrates your professional skills.

Explore our Popular Data Science Courses

upGrad’s Exclusive Data Science Webinar for you –

ODE Thought Leadership Presentation