Abstract
Data Science Portfolio is an open-source AI & Machine Learning project. Portfolio of data science projects completed by me for academic, self learning, and hobby purposes. A collection of data-science projects — completed for academic, self-learning, and hobby purposes — presented as Jupyter notebooks, plus a few R analyses published on RPubs. It is built using Jupyter Notebook, Python, Pandas, scikit-learn, Machine Learning. The complete source code is publicly available on GitHub under the MIT License, making it a useful reference for students building an AI & Machine Learning mini project or final-year project.
1. Introduction
A collection of data-science projects — completed for academic, self-learning, and hobby purposes — presented as Jupyter notebooks, plus a few R analyses published on RPubs.
Most notebooks read the small datasets under data/. Two fetch their data on first run and cache it: the digit-recognition notebook downloads MNIST via torchvision, and the stock-market notebook pulls tech-stock prices via yfinance (with a vendored snapshot as a fallback).
Questions or collaboration? Reach me at contact@sajalsharma.com, or see what I am working on now at sajalsharma.com.
2. Objective
Portfolio of data science projects completed by me for academic, self learning, and hobby purposes.
This project demonstrates how Jupyter Notebook, Python, Pandas can be applied to a real-world AI & Machine Learning problem.
4. Technology Stack
5. System Requirements
General requirements for this technology stack — check the README for exact versions.
- Python 3.8 or later with Jupyter Notebook / JupyterLab (or Google Colab)
- pip for dependencies
- Python 3.8 or later
- pip / virtualenv for dependencies
- VS Code, PyCharm or Jupyter Notebook
- Git (to clone the repository)
6. Installation & Setup
git clone https://github.com/sajal2692/data-science-portfolio.git
cd data-science-portfolioFull setup instructions are in the project README.
7. Future Enhancements
Suggested extensions you can add to make this your own project.
- Deploy the model as a web app with Streamlit, Flask or FastAPI
- Compare against an additional model and report the metric difference
- Add explainability (SHAP / Grad-CAM)
8. Viva / Review Questions
Common questions examiners ask for projects in this domain.
- What dataset does the project use and how was it pre-processed?
- Which algorithm / model architecture is used and why was it chosen over alternatives?
- How are training and testing data split, and how is overfitting avoided?
- Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
- How would you deploy this model for real users?
9. Source Code & License
This project is developed by sajal2692 and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.
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