A professional, interactive web application for exploring and analyzing your data. Built with Streamlit and designed for both technical and non-technical users.
This dashboard helps you understand your data through automatic analysis and interactive visualizations. Upload a CSV or Excel file, and the application will clean your data, calculate statistics, detect patterns, and create charts to help you make sense of it all.
Dataset Health Score Get an instant assessment of your data quality. The dashboard calculates a score from 0 to 100 based on missing values, duplicates, outliers, and data consistency.
Plain English Insights Statistical jargon can be confusing. This tool explains what's happening in your data using simple language.
Data Cleaning Transparency When you upload data, the dashboard automatically cleans it by removing duplicates and filling in missing values. A detailed summary shows exactly what was changed.
Statistical Validity Warnings Running statistical tests on small samples can give misleading results. The dashboard warns you when your sample size is too small or when groups are unbalanced.
Interactive Visualizations Create histograms, scatter plots, line charts, box plots, and bar charts. All charts are interactive, allowing you to zoom, pan, and hover for details.
Correlation Analysis Discover relationships between variables with an interactive heatmap and ranked list of the strongest correlations in your dataset.
Outlier Detection Automatically identify unusual values using the IQR method.
Hypothesis Testing Test your assumptions with built-in normality tests and t-tests.
Upload a CSV or Excel file through the sidebar and explore the different sections and tabs to see various analyses. A sample dataset is included in sample_data.csv if you want to try the dashboard right away.
data-analysis-dashboard/
├── app.py # Application entry point
├── requirements.txt # Python dependencies
├── sample_data.csv # Example dataset
└── src/
├── dashboard.py # Main UI and orchestration
├── data_manager.py # Data loading and cleaning
├── stats_manager.py # Statistical calculations
└── visualizer.py # Chart creation
The application uses an object-oriented architecture with clear separation of concerns. All calculations use industry-standard methods including IQR for outlier detection, Pearson's correlation coefficient, and Shapiro-Wilk normality tests.
- Python: 3.8+
- Framework: Streamlit
- Data Libraries: pandas, numpy, scipy
- Visualization: plotly
- Excel Support: openpyxl