Looking for some hands-on data science projects to sharpen your skills this weekend? Whether you're a beginner or an experienced practitioner, working on real-world projects is one of the best ways to learn. Below, we've curated 10 fantastic data science projects from a comprehensive list, spanning various domains like NLP, computer vision, time series forecasting, and MLOps.
Each project comes with a clear objective, relevant technologies, and a link to detailed instructions—so you can dive right in!
🔥 1. Digit Recognition using CNN for MNIST Dataset
Domain: Computer Vision / Deep Learning
Tech Stack: Python, TensorFlow/Keras, CNN
Why Try This?
The MNIST dataset is perfect for beginners to explore Convolutional Neural Networks (CNNs). You'll learn how to preprocess image data, build a CNN model, and evaluate its performance.
📊 2. Time Series Forecasting with Facebook Prophet
Domain: Time Series Analysis
Tech Stack: Python, Facebook Prophet, Cesium
Why Try This?
Time series forecasting is crucial in finance, sales, and IoT. This project teaches you how to use Facebook Prophet, a powerful forecasting tool by Meta, to predict future trends.
🤖 3. Text Classification with Transformers (RoBERTa & XLNet)
Domain: NLP / Transformers
Tech Stack: Python, Hugging Face, PyTorch
Why Try This?
Transformers like RoBERTa and XLNet dominate NLP tasks. This project walks you through fine-tuning these models for text classification, a skill useful in sentiment analysis, spam detection, and more.
🛒 4. Market Basket Analysis using Apriori & FP-Growth
Domain: Recommendation Systems
Tech Stack: Python, Scikit-learn, Pandas
Why Try This?
Ever wondered how Amazon recommends products? This project uses association rule mining (Apriori & FP-Growth) to uncover product purchase patterns—essential for retail analytics.
📈 5. Loan Default Prediction with Explainable AI
Domain: Finance / ML Interpretability
Tech Stack: Python, LightGBM, SHAP
Why Try This?
Banks need to understand why a loan might default. This project combines LightGBM with SHAP values to build a model that’s both accurate and interpretable.
🏡 6. House Price Prediction with Regression Models
Domain: Regression / Predictive Analytics
Tech Stack: Python, Scikit-learn, Pandas
Why Try This?
A classic ML project! Predict house prices using linear regression, Ridge, and Lasso, while learning feature engineering and model evaluation.
🚀 7. Deploy an ML Model with Streamlit & PyCaret
Domain: MLOps / Deployment
Tech Stack: Python, PyCaret, Streamlit
Why Try This?
Model deployment is a must-have skill. This project shows how to build and deploy an ML app quickly using PyCaret for automation and Streamlit for the UI.
🎭 8. Fake News Detection with NLP & Deep Learning
Domain: NLP / Deep Learning
Tech Stack: Python, TensorFlow, LSTM
Why Try This?
Fake news is a growing problem. Learn how to classify news articles as real or fake using LSTM networks, a type of recurrent neural network (RNN).
🛠️ 9. Build a CI/CD Pipeline for ML with Jenkins
Domain: MLOps / Automation
Tech Stack: Jenkins, Docker, Python
Why Try This?
CI/CD pipelines automate ML workflows. This project teaches you how to set up Jenkins for ML model testing and deployment, a valuable skill in production environments.
🏎️ 10. Real-Time Streaming Pipeline with Spark & Kafka
Domain: Big Data / Real-Time Analytics
Tech Stack: PySpark, Kafka, AWS
Why Try This?
Real-time data processing is key in IoT and finance. This project guides you in building a Spark Streaming pipeline with Kafka for live data analysis.
These projects cover diverse data science domains—from NLP and computer vision to MLOps and big data. Pick one that excites you and start coding this weekend!
💡 Pro Tip: If you're a beginner, start with MNIST Digit Recognition or House Price Prediction. If you're advanced, try Transformer-based NLP models or real-time Spark pipelines.
Happy coding! 🚀
Top comments (0)