Data Wash - A professional data cleaning and analysis tool with Python Flask backend and React frontend.

• File Upload: Support for CSV and Excel files (up to 16MB)
• Data Preview: View first 5 rows or scroll through entire dataset
• Dataset Information: Shape, missing values analysis, and statistical summary
• Data Types: Comprehensive column data type analysis and categorization
• Dataset Description: Detailed column analysis with statistics and sample values
• Column Management: Interactive column dropping with multi-selection
• Missing Value Imputation: Fill missing values using various methods (mean, median, mode, custom values)
• Data Visualization: Interactive plots and charts based on data types

• Smart Plot Selection: Plot types automatically adapt based on selected axes and data types
• Supported Plots:
  • Scatter plots (numeric vs numeric)
  • Line plots (numeric vs numeric)
  • Bar charts (categorical data)
  • Histograms (numeric distributions)
  • Box plots (outlier detection)
• Correlation Analysis: Heatmap and correlation matrix for numeric columns

• Professional Design: Modern gradient backgrounds with glassmorphism effects
• Responsive Layout: Works on desktop and mobile devices with horizontal scrolling for tabs
• Interactive Navigation: 8-tab interface with disabled states and smart navigation
• Real-time Feedback: Loading states, error handling, and success messages
• Data Pagination: Efficient handling of large datasets
• Dynamic Forms: Add/remove functionality for column operations
• Smart Validation: Prevents duplicate selections and validates inputs

AISA/
├── backend/
│   ├── app.py              # Flask API server
│   ├── requirements.txt    # Python dependencies
│   └── uploads/           # File upload directory
├── frontend/
│   ├── public/
│   │   ├── index.html
│   │   └── manifest.json
│   ├── src/
│   │   ├── components/
│   │   │   ├── FileUpload.js
│   │   │   ├── DataPreview.js
│   │   │   ├── DataInfo.js
│   │   │   ├── DataTypeInfo.js
│   │   │   ├── DatasetDescription.js
│   │   │   ├── ColumnDropper.js
│   │   │   ├── MissingValueImputation.js
│   │   │   └── DataVisualization.js
│   │   ├── App.js
│   │   ├── App.css
│   │   ├── index.js
│   │   └── index.css
│   └── package.json
└── README.md

1. Navigate to backend directory:

   cd backend

2. Create virtual environment (recommended):

   python -m venv venv
   venv\Scripts\activate  # Windows

3. Install Python dependencies:

   pip install -r requirements.txt

4. Run the Flask server:

   python app.py

   Server will start on http://localhost:5000

1. Navigate to frontend directory:

   cd frontend

2. Install Node.js dependencies:

   npm install

3. Start the React development server:

   npm start

   Application will open on http://localhost:3000

• POST /api/upload - Upload CSV/Excel file
• GET /api/preview - Get first 5 rows of data
• GET /api/data - Get complete dataset
• GET /api/info - Get dataset information and statistics
• POST /api/plot - Generate custom plots
• GET /api/correlation - Get correlation matrix and heatmap
• POST /api/plot-options - Get available plot types for selected axes
• POST /api/column-analysis - Get detailed analysis for a specific column
• POST /api/drop-columns - Remove selected columns from dataset
• POST /api/impute-missing - Apply missing value imputation rules

• Drag and drop or click to browse for CSV/Excel files
• Maximum file size: 16MB
• Supported formats: .csv, .xlsx, .xls

• View first 5 rows for quick preview
• Switch to full dataset view with pagination
• Scroll through large datasets efficiently

• Dataset Info: View shape, missing values, and statistical summary
• Missing Values: Highlighted columns with missing data and severity levels
• Statistics: Descriptive statistics for all numeric columns

• View all column data types with descriptions
• Filter by type category (numeric, text, datetime, boolean)
• Sort by column name, data type, or category
• Color-coded type indicators

• Select any column for detailed analysis
• View statistics, sample values, and frequency distribution
• Numeric columns show mean, median, std deviation, etc.
• Text columns show most frequent values

• Add Multiple Columns: Use + button to add columns to drop list
• Smart Selection: Prevents selecting the same column twice
• Visual Feedback: See selected columns highlighted
• Batch Operations: Drop multiple columns at once

• Multiple Imputation Methods:
  • Mean: For numeric columns (average value)
  • Median: For numeric columns (middle value)
  • Mode: Most frequent value (works for all types)
  • Forward Fill: Use previous valid value
  • Backward Fill: Use next valid value
  • Custom Value: Specify your own replacement value
• Smart Method Selection: Available methods adapt to data type
• Rule-based System: Add multiple imputation rules
• Preview: See which rules will be applied before execution

• Custom Plots: Select X/Y axes and plot type
• Smart Suggestions: Plot types adapt to your data selection
• Correlation Analysis: View correlation matrix and heatmap
• Interactive Controls: Reset and regenerate plots easily

• Flask: Web framework for API endpoints
• Pandas: Data manipulation and analysis
• Matplotlib & Seaborn: Data visualization
• NumPy: Numerical computing
• OpenPyXL: Excel file support

• React: User interface library
• Axios: HTTP client for API calls
• React Dropzone: File upload component
• CSS3: Modern styling with gradients and animations

• File Processing: Secure file upload with type validation
• Data Streaming: Efficient handling of large datasets
• Error Handling: Comprehensive error management
• Responsive Design: Mobile-first approach
• Performance: Optimized rendering and API calls

• Chrome (recommended)
• Firefox
• Safari
• Edge

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Test thoroughly
5. Submit a pull request

This project is for educational and professional use.

Made with ❤️ for professional data analysis