A collection of exploratory data analysis (EDA) projects on different datasets. Each folder contains a Jupyter notebook, a short write-up of questions explored, and links to data sources.
Key Features
- Multiple Datasets: Analysis of various datasets to understand different data patterns
- Jupyter Notebooks: Interactive analysis with code, visualizations, and insights
- Documentation: Clear write-ups explaining the questions explored and findings
- Data Sources: Links to original datasets for reproducibility
Technologies Used
- Python: Core programming language for data analysis
- Jupyter Notebooks: Interactive development environment
- Pandas: Data manipulation and analysis
- Matplotlib/Seaborn: Data visualization
- NumPy: Numerical computing
Project Structure
Each analysis includes:
- Exploratory data analysis notebook
- Summary of key findings
- Data source references
- Visualization outputs
This project demonstrates my ability to conduct thorough exploratory data analysis, identify patterns in data, and communicate findings effectively through visualizations and documentation.