DataGuard is a web-based data analysis platform that helps users upload datasets, automatically evaluate their quality, identify data problems, clean the data, and understand their datasets through interactive analysis and visualizations.
It is designed to make data preprocessing and exploratory data analysis easier without requiring users to write complex Python code.
Upload datasets directly through the dashboard.
- CSV files
- Excel XLSX files
- Automatic dataset profiling
- Dataset preview
- File information and statistics
DataGuard automatically analyzes uploaded datasets and provides useful insights such as:
- Number of rows and columns
- Data types
- Missing values
- Duplicate records
- Unique values
- Numeric statistics
- Categorical analysis
- Outlier detection
- Column-level insights
- Statistical summaries
DataGuard generates visualizations to help understand the dataset.
Supported analysis includes:
- Histograms
- Categorical bar charts
- Scatter plots
- Correlation analysis
- Numeric distributions
- Category distributions
Charts are generated automatically based on the structure of the uploaded dataset.
Explore individual dataset columns and understand:
- Data type
- Missing values
- Unique values
- Statistical information
- Outliers
- Value distributions
This makes it easier to identify problematic columns and understand the structure of the data.
DataGuard provides automated data-cleaning operations including:
- Duplicate removal
- Missing-value treatment
- Outlier handling
- IQR-based outlier clipping
- Data quality improvement
The goal is to make datasets cleaner and more suitable for further analysis or machine-learning workflows.
Each dataset receives an overall quality score based on detected data-quality problems.
The platform evaluates factors such as:
- Missing data
- Duplicate records
- Outliers
- Dataset structure
The dashboard provides a quick overview of the dataset's health.
DataGuard provides recommendations based on detected problems in the dataset.
For example:
- Remove duplicate records
- Investigate missing values
- Review columns containing outliers
- Check inconsistent data types
- Examine suspicious values
This helps users understand what should be fixed before using the dataset.
Generate analysis reports containing important information about the dataset.
Reports can include:
- Dataset statistics
- Data-quality metrics
- Missing-value information
- Duplicate information
- Outlier information
- Column details
- Cleaning results
DataGuard supports user accounts with:
- Registration
- Login
- Logout
- User profiles
- User-specific datasets
- Analysis history
DataGuard includes a dedicated Admin Portal for managing users, datasets, and platform activity.
| Field | Value |
|---|---|
| Username | admin |
[email protected] |
|
| Password | Admin@12345 |
| Role | admin |
After logging in, the administrator can access the Admin Portal to:
- 👥 Manage users
- 🔑 Manage user roles
- 📂 View and manage datasets
- 🗑️ Delete datasets
- 📜 View activity logs
- 📊 View analysis history
- 🛡️ Monitor platform activity
After starting the application, open: http://127.0.0.1:5000/admin
DataGuard keeps track of dataset analysis activities.
Users can review previous analysis operations and quality information associated with their datasets.
DataGuard is built as a Progressive Web App.
It includes:
- Responsive interface
- Mobile-friendly design
- PWA manifest
- Service worker
- Installable web application
- HTML5
- CSS3
- JavaScript
- Chart.js
- Responsive UI
- Progressive Web App technologies
- Python
- Flask
- Pandas
- OpenPyXL
- SQLite
- Vercel
DataGuard-PWA/
│
├── app.py
├── database.py
├── requirements.txt
├── vercel.json
│
├── templates/
│ ├── dashboard.html
│ ├── dataset_details.html
│ ├── login.html
│ ├── register.html
│ ├── profile.html
│ └── admin/
│
├── static/
│ ├── css/
│ │ └── style.css
│ ├── js/
│ ├── icons/
│ ├── manifest.json
│ └── sw.js
│
├── uploads/
├── reports/
│
└── README.md