Data Science And Machine Learning Projects Dojo

Abstract

1. Introduction

Collections of Data Science & ML projects and dojo where I practice Data Science, Machine Learning, Deep Learning and Data Visualization related skills, theories, probability, statistics, etc.

2. Objective

collections of data science, machine learning and data visualization projects with pandas, sklearn, matplotlib, tensorflow2, Keras, various ML algorithms like random forest classifier, boosting, etc

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

3. Key Features / Modules

• Project: Titanic dataset
• 01.ML Basic
• 02.Intro to Feature Engineering
• 03.Explore Data
• 04.Create and Clean Features
• 05.Prepare Features for Modelling
• 06.Compare and Evaluate Models

4. Technology Stack

• NumPy - package for scientific computing with Python
• Pandas - fast, powerful, flexible and easy to use open source data analysis and manipulation tool
• Pandas Profiling - generate reports from dataframe
• Geo Pandas - support for geographic data to pandas objects.
• Scikit-learn - Simple and efficient tools for predictive data analysis
• TensorFlow - An end-to-end open source machine learning platform
• Keras - Deep Learning framework
• NLTK - Natural Language Toolkit

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/ptyadana/Data-Science-and-Machine-Learning-Projects-Dojo.git
cd Data-Science-and-Machine-Learning-Projects-Dojo

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 ptyadana 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.