dataVizzz –An AI-Driven Data Visualization & Learning Platform

By Varsha Rao
MARVEL Level 3 | UVCE

🔗 Links:

• Website: dataVizzz Platform
• Demo Video: Watch on Google Drive
• User Manual: View Documentation
• GitHub Repository: Github- datavizzz

About the Project

dataVizzz is an AI-driven, interactive data visualization platform designed to bridge the gap between learning and analysis. It enables users—ranging from students and beginners to small-scale analysts—to upload datasets, generate visualizations, and understand both the charts and the data behind them in a simple, guided manner.

Unlike traditional visualization tools that focus only on output, dataVizzz emphasizes interpretation, education, and usability, making data analysis approachable even for non-experts.

Key Features

Automatic Chart Generation

Supports 107+ visualization types, ranging from basic charts (bar, line, pie) to advanced and uncommon visuals (network graphs, Sankey, Gantt, radar, treemaps, density contours, 3D plots).

Smart Chart Recommendations

Automatically analyzes uploaded datasets and suggests the best-fit chart along with top alternative visualizations based on column types and data structure.

Explain This Chart

Provides a clear explanation of what a selected chart represents, what insights it conveys, and when it should be used in real-world scenarios.

Context-Aware AI Chatbot

Allows users to ask questions about the dataset or visualization and receive guided, beginner-friendly responses with contextual awareness.

Learn About a Chart

Built-in educational descriptions for each chart type, explaining its purpose and ideal usage.

Learn About a Concept

Explains statistical and data concepts such as correlation, variance, standard deviation, and distributions directly inside the application.

Custom Color Themes & Palettes

Enables users to customize background, text, and plot element colors for presentation-ready or branded visuals.

Statistics Summary

Generates descriptive statistics, correlations, missing-value analysis, and data type summaries automatically.

Data Preview

Displays a clean preview of the uploaded dataset to help users understand structure before visualization.

Large File Support

Designed to handle moderately large datasets efficiently using Pandas and optimized processing.

Download Visuals

Allows exporting generated charts as image files for reports and presentations.

No Login Required

Runs locally with no authentication, ensuring privacy and ease of use.

Technology Stack

• Frontend / UI: Streamlit
• Visualization: Matplotlib, Plotly, Seaborn
• Backend Logic: Python (Pandas, NumPy)
• AI / LLM: Groq (LLaMA 3 API), Rule-based Statistical Logic (SLM)
• Environment Management: Python Virtual Environment (venv), python-dotenv
• Image & Export Handling: Pillow
• File Handling: CSV / XLSX via Pandas I/O

How dataVizzz Differs from Existing Tools

Unlike tools such as Power BI, Tableau, or Microsoft Excel, which primarily target enterprise users and require licenses or cloud ecosystems, dataVizzz is:

• Local and Privacy-First – No data upload to external servers
• Learning-Oriented – Focuses on understanding charts, not just creating them
• Highly Flexible – Offers 107+ chart types, including many not commonly available
• Explainable by Design – Integrates education, recommendations, and AI explanations
• Accessible – No login, no subscription, beginner-friendly UI

While competitors emphasize business dashboards, dataVizzz uniquely combines visualization, education, and explainable AI into a single lightweight platform.

Project Journey & Evolution

The project began as a simple visualization tool and progressively evolved through multiple iterations:

1. Started with basic chart rendering
2. Expanded to a modular registry system enabling rapid addition of new plots
3. Introduced learning-centric features such as chart explanations and concept learning
4. Added smart recommendations and statistical summaries
5. Integrated AI for contextual explanations and conversational interaction
6. Improved UI/UX based on continuous feedback from peers and potential users

Each feature was added with a focus on user understanding, not just technical capability.

Target Audience

• Students & Learners – Understanding data visualization and statistics
• Beginners in Data Science – Learning how to choose and interpret charts
• Teachers & Educators – Teaching visualization concepts interactively
• Small-Scale Analysts – Media agencies, social media analysts, and content teams
• Non-Technical Users – Anyone needing quick insights without complex tools

Future Scope

• Transition from Streamlit UI to a React-based frontend for improved UX
• Introduction of a Premium version for analysts and managers
• Multi-file uploads for cross-dataset comparison
• SQL database integration for business analytics users
• Trend analysis and benchmarking against averages, medians, and industry standards
• Advanced AI-generated analytical reports using LLMs
• Performance optimization using containerization and DevOps practices