Data Science Regular Bootcamp

Regular practice on Data Science, Machien Learning, Deep Learning, Solving ML Project problem, Analytical Issue. Regular boost up my knowledge. The goal is to help learner with learning resource on Data Science filed.

Data ScienceJupyter NotebookMIT

Abstract

Data Science Regular Bootcamp is an open-source Data Science project. Regular practice on Data Science, Machien Learning, Deep Learning, Solving ML Project problem, Analytical Issue. Regular boost up my knowledge. The goal is to help learner with learning resource on Data Science filed. Data Scientist | Machine Learning Engineer Website: https://imsanjoykb.github.io/ ResearchGate: https://www.researchgate.net/profile/imsanjoykb Linkedin: https://www.linkedin.com/in/imsanjoykb/ Email: sanjoy.eee32@gmail.com. It is built using Jupyter Notebook, Machine Learning, Deep Learning. The complete source code is publicly available on GitHub under the MIT License, making it a useful reference for students building a Data Science mini project or final-year project.

1. Introduction

Data Scientist | Machine Learning Engineer Website: https://imsanjoykb.github.io/ ResearchGate: https://www.researchgate.net/profile/imsanjoykb Linkedin: https://www.linkedin.com/in/imsanjoykb/ Email: sanjoy.eee32@gmail.com

Database Connection MySql, PostgresSql, Elasticsearch

Data Visualization MatplotLib, Seaborn, Plotly, Bokah

2. Objective

Regular practice on Data Science, Machien Learning, Deep Learning, Solving ML Project problem, Analytical Issue. Regular boost up my knowledge. The goal is to help learner with learning resource on Data Science filed.

This project demonstrates how Jupyter Notebook, Machine Learning, Deep Learning can be applied to a real-world Data Science problem.

4. Technology Stack

Jupyter NotebookMachine LearningDeep Learning

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
  • Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/imsanjoykb/Data-Science-Regular-Bootcamp.git
cd Data-Science-Regular-Bootcamp

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

  • Turn the analysis into an interactive dashboard
  • Automate data refresh with a scheduled job
  • Add a predictive model on top of the analysis

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

  1. What is the source of the dataset and how was missing data handled?
  2. Which exploratory analysis steps revealed the most useful insight?
  3. Why were these particular charts chosen to present the data?
  4. Which statistical or ML technique supports the conclusions?
  5. How could the analysis be automated or refreshed with new data?

9. Source Code & License

This project is developed by imsanjoykb 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.

View Source on GitHubDownload ZIP

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