The goal of this project is to automate the most common steps in data analysis, such as:
- Loading and cleaning datasets
- Generating visualizations
- Identifying correlations
- Presenting results in a clear and visual manner
We applied this tool to three real-world datasets:
Analyzed user ratings of books to find rating distributions and relationships between book attributes.
Explored country-level data to identify what factors are most correlated with happiness scores.
Investigated how people consume different forms of media and how they might relate to other social indicators.
- Generates correlation heatmaps automatically
- Plots value distributions for any numeric column
- Cleans and preprocesses data without manual coding
- Organizes results neatly by dataset
- Python
- pandas
- seaborn
- matplotlib