This project presents exploratory data analysis and multidimensional data visualization techniques using Python and multiple analytical datasets.
The solution focuses on statistical analysis, graphical data interpretation, financial visualization, optimization function visualization, regression analysis, and multidimensional exploratory analytics.
- Exploratory Data Analysis (EDA)
- Statistical data visualization
- Financial risk-return analysis
- Multidimensional scatter plot analysis
- Boxplot and distribution analysis
- Regression visualization
- 3D optimization function visualization
- Graph and network visualization
- Comparative analytical workflows
The project uses multiple public and built-in datasets for exploratory data analysis and visualization tasks, including:
- Iris dataset
- Abalone dataset
- Zoo dataset
- Synthetic optimization datasets
- Financial portfolio datasets
The datasets were used to demonstrate different visualization techniques, statistical relationships, multidimensional analysis, and graphical interpretation workflows.
The project includes:
- Distribution analysis
- Correlation analysis
- Scatter plot visualization
- Regression analysis
- Financial portfolio visualization
- Optimization landscape visualization
- Statistical comparison analysis
- Multidimensional data interpretation
- Graph-based visualization techniques
This visualization presents portfolio optimization scenarios using multidimensional financial analysis.
Faceted scatter plot analysis for identifying relationships between biological characteristics in the Abalone dataset.
Boxplot visualization used for statistical comparison, distribution interpretation, and outlier detection.
Three-dimensional visualization of optimization landscapes used in analytical and optimization-oriented workflows.
Regression-based analysis demonstrating relationships between selected numerical variables.
Graph-based visualization presenting Hamiltonian cycle representation and network traversal concepts.
The project demonstrates:
- Scatter plots
- Faceted visualizations
- Statistical boxplots
- Regression plots
- 3D surface visualizations
- Financial data visualizations
- Graph and network visualizations
- Comparative analytical charts
- Python
- Pandas
- NumPy
- Matplotlib
- Seaborn
- Scikit-learn
- Exploratory Data Analysis (EDA)
- Data Visualization
- Statistical Analysis
- Jupyter Notebook
The goal of this project is to demonstrate practical skills in exploratory data analysis, multidimensional visualization, statistical interpretation, and analytical storytelling using Python-based visualization tools.
The solution successfully demonstrates:
- Exploratory data analysis workflows
- Statistical visualization techniques
- Multidimensional analytical interpretation
- Financial data visualization
- Optimization function visualization
- Regression analysis workflows
- Graph-based analytical visualization
- Practical data storytelling techniques
Paulina Broda