tkarim45/Beginner-Data-Science-Projects

This repository is a curated collection of hands-on data science projects tailored for beginners. Whether you're just starting your journey in data science or looking to strengthen your skills, these projects provide a practical and interactive way to apply your knowledge.

What is this?

A GitHub repository that collects beginner-friendly data science projects. Each project is a standalone Jupyter notebook with real datasets, clear explanations, and working code — meant for students, career switchers, and self-learners.

What's inside?

  • 115 projects grouped into 11 categories: Classification, Regression, Time Series, NLP, Clustering, Recommendation Systems, Anomaly Detection, EDA/Visualization, Miscellaneous Applied, Computer Vision, and Robotics.
  • Projects are ordered by difficulty (Beginner → Intermediate → Advanced) and come with a learning path that starts with fundamentals (pandas, sklearn, basic ML) and moves to text/NLP, computer vision/deep learning, and advanced topics.
  • Each project has its own folder with a README, a requirements.txt file, and a working notebook.

How to use it

  1. Prerequisites: Python 3.9+ and Jupyter Notebook/Lab.
  2. Install core libraries: pip install pandas numpy matplotlib seaborn scikit-learn jupyter
  3. Clone the repo: git clone https://github.com/tkarim45/Beginner-Data-Science-Projects.git
  4. Pick a project (e.g., cd "Iris Flower Classification"), install its dependencies (pip install -r requirements.txt), and run jupyter notebook.

Contributing

Contributions are welcome. Rules: one project per PR, include a README + requirements.txt + working notebook, host large datasets externally (>10 MB), and don't commit model binaries.

License

MIT License — free to use for learning.

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