A curated repository of Python notebooks exploring programming concepts, data analysis techniques and applied statistics using Jupyter.
This repository contains a collection of Jupyter notebooks developed as part of continuous academic and practical learning in the fields of:
- Python programming
- Data manipulation and cleaning
- Statistical analysis
- Exploratory data analysis (EDA)
- Basic machine learning workflows
Each notebook is documented with markdown cells, explaining the logic, methodology and output where applicable. The purpose of this archive is to demonstrate hands-on proficiency with core data science tools and analytical reasoning.
The notebooks use the following Python libraries:
pandas # Data manipulation
numpy # Numerical computing
matplotlib # Static visualizations
seaborn # Statistical visualizations
scikit-learn # Machine learning (basic usage)
statsmodels # Statistical modeling