A Streamlit-based app that enables anyone (even non-technical users) to quickly upload datasets (CSV/XLSX) and interactively explore them — no coding required!
It automatically generates summaries, charts, insights, correlation heatmaps, and allows one-click export to PDF reports.
Works fully offline once dependencies are installed.
- 📂 Easy Upload – Drag & drop CSV or Excel files.
- 📑 Dataset Summary – Automatically compute column stats, data types, and missing values.
- 📊 Interactive Visualizations – Plotly-powered charts (bar, line, scatter, histogram, pie, box, heatmap).
- 💡 Automated Insights – AI-style dataset observations.
- 📈 Correlation Analysis – Detect numeric relationships via heatmaps.
- 📥 One-Click Report Export – Generate styled PDF dataset reports (summary + insights + charts).
- 🌍 Offline Mode – No internet required after setup.
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Clone the Repository
git clone https://github.com/Hassan-07-code/dataset_visualizer.git cd one_click_dataset_visualizer -
Install dependices
pip install -r requirements.txt
streamlit run app.py
│ ├── app.py # Main Streamlit entry point ├── assets/ # Static assets (logo, images, etc.) │ ├── modules/ # Core processing functions │ ├── data_loader.py # Handles dataset upload │ ├── data_summary.py # Generates summary/statistics │ ├── visualization.py # Creates Plotly charts │ ├── export_report.py # Builds styled PDF reports │ ├── pages/ # Streamlit multi-page UI │ ├── 1_🏠_Home.py # Upload datasets │ ├── 2_📑_Data_Summary.py # Dataset overview │ ├── 3_📊_Charts.py # Interactive visualizations │ ├── 4_💡_Insights.py # Automated insights │ ├── 5_📤_Export_Report.py # Export as PDF │ ├── tests/ # Unit & integration tests │ ├── test_export_report.py │ ├── test_visualization.py │ ├── requirements.txt # Python dependencies └── README.md # Project documentation