Data Dashboard
Interactive EDA & Visualization App
Client
Self-initiated
Year
2024
Role
Data Scientist · Developer
Stack
Python · Streamlit · Pandas · Matplotlib · Seaborn · NumPy
Status
Complete
Context
Data exploration, no code required.
EDA often means rewriting the same boilerplate for every new dataset. This Streamlit dashboard replaces that with an interactive UI: upload a CSV and instantly get statistics, visualizations, missing-value reports, and downloadable summaries. Built for non-technical users who need insights without writing Python.
The Vision
Self-service data analysis.
The dashboard turns manual EDA into a self-service tool. Upload any CSV, preview the data, view descriptive stats, generate six types of plots (histogram, bar, box, heatmap, scatter, pairplot), handle missing values, and export a summary report.
The Product
Your data, analyzed instantly.
Single-page Streamlit app with upload, preview, statistics, 6 visualization types, missing data handling (drop or fill with mean), and CSV report export. All in one clean interface.
Architecture
Stack.
Framework
Interactive web framework for data dashboards.
Analysis
Data manipulation, statistics, and missing value handling.
Visualization
6 plot types: histogram, bar, box, heatmap, scatter, pairplot.
Export
Download summary statistics as a CSV report.
Outcome
Feature summary.
Challenge
Making data analysis accessible to non-coders.
The hardest part wasn’t the code — it was deciding which features to expose and which to automate. Too many options overwhelm users; too few make the tool useless. The sweet spot: upload CSV, auto-generate descriptive stats, offer 6 plot types, handle missing data in one click, and export results. Everything else stays under the hood.
Performance
Performance
6
Plot Types
Streamlit
Framework
Auto-handle
Missing Data
CSV Report
Export
Dataset
Input
Any CSV file
Visualizations
Histogram, Bar, Box, Heatmap, Scatter, Pairplot
Missing Values
Detect, Drop, or Fill
Report
Downloadable CSV summary