Skip to content

REQUIREMENTS.md

Latest commit

 

History

History
155 lines (112 loc) · 7.13 KB

REQUIREMENTS.md

File metadata and controls

155 lines (112 loc) · 7.13 KB

PredictStream - Detailed Requirements

This document outlines the detailed requirements and specifications for the PredictStream application. This is a demonstration project meant to showcase a polished, high-quality Streamlit dashboard for data exploration and predictive analytics.

Core Functionality

1. Data Import & Management

  • File Upload: Allow users to upload CSV and Excel files (.xlsx, .xls) with intuitive UI feedback
  • Sample Data: Provide 2-3 sample datasets for users who want to explore without uploading files
  • Data Preview: Show a preview of loaded data with pagination for larger datasets
  • Data Validation: Check for common issues (missing values, inappropriate data types) and provide warnings
  • Data Transformation: Enable basic transformations like:
    • Handling missing values (drop, fill with mean/median/mode)
    • Type conversion (numeric, categorical, datetime)
    • Feature encoding (one-hot, label encoding)
    • Scaling/normalization options (min-max, standard)

2. Exploratory Data Analysis

  • Summary Statistics: Provide automated statistical summaries for numeric and categorical fields
  • Data Quality Assessment: Show missing value percentages, outlier detection, and data type breakdown
  • Correlation Analysis: Generate and visualize correlation matrices with options to highlight strong correlations
  • Distribution Analysis: Create histograms and density plots for numeric variables
  • Category Analysis: Generate bar charts and pie charts for categorical variables
  • Time Series Analysis: If time-based data is detected, provide time series plots and decomposition

3. Data Visualization

  • Interactive Charts: Create interactive visualizations with Plotly
  • Chart Types: Support various visualization types:
    • Scatter plots (with optional trend lines)
    • Bar charts and histograms
    • Box plots and violin plots
    • Heatmaps
    • Pair plots for feature relationships
  • Customization: Allow basic customization of chart appearance (titles, labels, colors)
  • Export: Enable saving visualizations as PNG/JPG files

4. Predictive Modeling

  • Problem Type Detection: Suggest classification or regression based on target variable
  • Model Selection: Offer a curated selection of models:
    • Classification: Logistic Regression, Random Forest, XGBoost
    • Regression: Linear Regression, Decision Trees, Random Forest
  • Feature Selection: Allow users to select features for model training
  • Training-Test Split: Configure train/test split ratio with option for random seed
  • Cross-Validation: Enable k-fold cross-validation for more robust evaluation
  • Hyperparameter Selection: Provide simplified hyperparameter options for each model

5. Model Evaluation

  • Performance Metrics:
    • Classification: Accuracy, Precision, Recall, F1-Score, ROC-AUC
    • Regression: MAE, MSE, RMSE, R-squared
  • Visualizations:
    • Classification: Confusion Matrix, ROC Curve, Precision-Recall Curve
    • Regression: Actual vs Predicted Plot, Residual Plot
  • Feature Importance: Show and visualize importance of features in the model
  • Model Explanation: Provide basic model interpretability using techniques like SHAP values

6. Prediction Capability

  • New Data Prediction: Allow prediction on new data points using UI inputs
  • Batch Prediction: Enable predictions on new datasets (uploaded separately)
  • Export Results: Provide option to download prediction results as CSV

User Interface Requirements

1. Layout & Navigation

  • Multi-Page Structure: Organize functionality across logical pages with clear navigation
  • Responsive Design: Ensure good user experience on different screen sizes
  • Sidebar Navigation: Use sidebar for primary navigation and global controls
  • Progress Indicators: Show loading states during computations

2. User Experience

  • Guided Workflow: Present a clear, step-by-step process for analysis
  • Interactive Elements: Use appropriate Streamlit widgets (sliders, dropdowns, etc.)
  • Tooltips & Help: Provide guidance and explanations for technical concepts
  • Error Handling: Show clear error messages with suggestions for resolution
  • State Persistence: Maintain application state between page navigation

3. Visual Design

  • Consistent Styling: Maintain consistent colors, fonts, and spacing
  • Theming: Support light/dark mode toggle
  • Branding: Include NeurArk branding (logo, colors) in a subtle manner
  • Visual Hierarchy: Prioritize important information through layout and typography

Technical Requirements

1. Environment

  • Python Version: Requires Python 3.11 or later
  • Core Libraries: Compatible with latest versions of Streamlit, Pandas, NumPy, and Scikit-learn
  • Development Tools: Uses pytest for testing

2. Performance

  • Efficient Data Handling: Use appropriate techniques for larger datasets
  • Caching: Implement caching for expensive operations (data processing, model training)
  • Optimization: Balance functionality with performance considerations

3. Code Quality

  • Modular Structure: Organize code into logical modules and functions
  • Documentation: Include comprehensive docstrings and comments
  • Error Handling: Implement robust exception handling
  • Type Hints: Use Python type hints for improved code quality

4. Deployment Considerations

  • Dependencies: Minimize and properly document all dependencies
  • Environment: Ensure compatibility with standard deployment platforms

Non-Functional Requirements

1. Usability

  • Intuitiveness: The application should be usable without extensive training
  • Accessibility: Follow basic accessibility best practices
  • Responsiveness: The UI should respond quickly to user interactions

2. Quality

  • Polish: The application should appear professional and well-crafted
  • Robustness: Handle edge cases and unexpected inputs gracefully
  • Consistency: Maintain consistent terminology and interaction patterns

Demo Scenarios

The application should excel at demonstrating these common scenarios:

  1. Quick EDA: Upload a dataset and quickly generate key insights and visualizations
  2. Model Comparison: Train multiple models on the same dataset and compare their performance
  3. Feature Analysis: Identify the most important features driving predictions
  4. Interactive Exploration: Allow stakeholders to interactively explore data relationships

Limitations (Acceptable for Demo)

As this is a demonstration project, the following limitations are acceptable:

  • Performance with very large datasets (>100MB) may be limited
  • No user authentication or multi-user support
  • No persistent storage of results between sessions
  • Limited to tabular data (no image, text, or specialized data formats)

Development Priorities

  1. User Experience: Focus on creating an intuitive, polished interface
  2. Visual Appeal: Ensure visualizations and UI components look professional
  3. Core Functionality: Implement the core features completely before adding extras
  4. Performance: Ensure the application runs smoothly with reasonable-sized datasets