This document outlines all the tasks that need to be completed to build the PredictStream application. These tasks are organized by Pull Request (PR) milestones to ensure a structured development process with regular validation points.
After completing a milestone, create a pull request with your changes for review before moving to the next milestone. Each PR should contain a manageable set of features that can be tested together. IMPORTANT: Each PR must include appropriate tests to verify the functionality being added.
- Create repository structure
- Set up README.md
- Create requirements.txt
- Create main application entry point (app.py) with basic structure
- Set up project configuration
- Add sample datasets in data directory
- Implement basic UI theme and layout
- Create utility module structure
- Setup testing framework and basic test structure
- Create tests for the initial application structure
- Implement file upload functionality (CSV/Excel)
- Create data validation and error handling
- Implement data preview with pagination
- Add data type detection and conversion
- Set up session state management for data persistence
- Create data summary functionality
- Implement sidebar navigation for data options
- Add sample data loader option
- Create unit tests for data loading and validation
- Implement integration tests for data import workflow
- Create summary statistics generator
- Implement data quality assessment
- Create correlation analysis functionality
- Add distribution analysis for numeric variables
- Implement categorical variable analysis
- Add missing value visualization
- Create data profile report generator
- Implement data insights summary
- Write tests for all EDA functions
- Create test cases with different data types and edge cases
- Set up visualization framework
- Implement histogram/density plots
- Create scatter plot functionality
- Add bar chart and pie chart generators
- Implement box plots and violin plots
- Create heatmap functionality
- Add visualization customization options
- Implement visualization export capability
- Write tests for all visualization functions
- Test visualization rendering with different data inputs
- Create feature selection interface
- Add train/test split functionality
- Implement cross-validation
- Create model selection interface for classification
- Implement Logistic Regression
- Implement Random Forest Classifier
- Add hyperparameter selection interface
- Create model training progress indicators
- Implement model caching for performance
- Write tests for model training pipeline
- Create test cases for classification models with sample datasets
- Extend model selection interface for regression
- Implement Linear Regression
- Implement Decision Tree Regressor
- Implement Random Forest Regressor
- Create problem type detector (classification/regression)
- Add regression-specific hyperparameter options
- Implement model comparison functionality
- Create model serialization/save functionality
- Write tests for regression modeling functions
- Test model auto-detection with different datasets
- Create performance metrics calculator
- Implement confusion matrix for classification
- Add ROC curve generator for classification
- Create precision-recall curve for classification
- Implement actual vs predicted plots for regression
- Add residual plot generator for regression
- Create feature importance visualization
- Implement SHAP value calculator and visualizer
- Write tests for all model evaluation metrics
- Test visualization of model interpretability features
- Create interface for single prediction
- Implement batch prediction functionality
- Add prediction results visualization
- Create prediction export capability
- Implement model export functionality
- Add report generation feature
- Create project save/load functionality
- Write tests for prediction functionality
- Test export functions with different formats and data sizes
- Add tooltips and help text throughout application
- Implement progress indicators for long-running operations
- Enhance error handling and user feedback
- Add light/dark mode toggle
- Implement responsive design adjustments
- Create "getting started" guide or tutorial
- Add sample use cases or walkthroughs
- Write tests for UI components and interactions
- Test application with different screen sizes
- Complete comprehensive test suite for all components
- Implement end-to-end tests for full application workflows
- Add performance benchmarking tests
- Create detailed code documentation
- Add in-app help functionality
- Perform performance optimization
- Final bug fixing and polish
- Complete final testing and validation
- Create
pages/directory and refactorapp.pyinto multiple pages - Implement sidebar navigation linking to each page
- Add
static/directory with placeholderlogo.png - Integrate NeurArk colors and logo throughout the UI
- Update README with new project structure
- Write tests verifying that pages load correctly
- Implement missing value handling options (drop, fill with mean/median/mode)
- Add feature encoding choices (one-hot and label encoding)
- Provide scaling/normalization utilities (min-max and standard)
- Create UI controls for applying transformations
- Write unit tests for transformation functions
- Add integration tests covering transformation workflow
- Implement pair plot visualization for feature relationships
- Enable export of visualizations as PNG and JPG
- Add UI selection for export format
- Write tests for pair plot generation and image export
- Extend model selection to include XGBoost for classification and regression
- Provide basic hyperparameter options for XGBoost models
- Update training utilities to support XGBoost
- Write tests covering XGBoost training and evaluation
- Detect datetime columns and offer time series plotting tools
- Implement decomposition plots for trend/seasonality analysis
- Write tests for time series detection and visualization
- Create separate page for single and batch predictions
- Add page for generating and downloading analysis reports
- Ensure navigation links include new pages
- Write integration tests for prediction and report pages
- Integrated evaluation metrics and plots into the Data Explorer page
- Implemented modeling page with model selection, training, cross-validation, and export functionality
- Added histogram, box plot, violin plot, and heatmap UI with export options
- Added logging utilities and integrated logging across the app
- Improve data-loading functions to validate inputs and raise descriptive errors
- Add checks for missing columns and types in transformation utilities
- Surface error messages in UI pages using
st.error - Add tests for new error handling in data and transform modules
- Implement helper functions in
utils/data.pyfor uploading/validating files - Provide wrapper storing uploaded data in
st.session_state - Replace repetitive upload code across pages
- Enforce maximum file size during upload
- Add tests covering oversized file uploads
- Extend sidebar and Plotly figure styling for both themes
- Add global theme toggle stored in session state
- Update tests to verify theme CSS output
- Create utils/time_series.py with ARIMA and naive forecast functions
- Add UI controls on Time Series page for model selection and forecast horizon
- Write tests for forecasting utilities
- Create comprehensive commit messages that clearly describe changes
- Focus on one PR milestone at a time
- Update this TODO file as you complete tasks
- Submit a PR when all tasks in a milestone are completed
- Every PR must include tests for the new functionality
- Address any review comments before moving to the next milestone
- Use GitHub issues to track bugs or additional feature requests
- Ensure test coverage is maintained or improved with each PR