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General Forecasting Tool - Project Requirements Document (v3.0)

Defines requirements for a no-code analytics platform with file upload, API integration, model training, and deployment.

May 2, 2026
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What this file does

Defines requirements for a no-code analytics platform with file upload, API integration, model training, and deployment.

When to use it

  • Starting a no-code data analysis tool project
  • Planning a platform with automated ML model deployment
  • Documenting a multi-phase analytics product roadmap
  • Specifying performance and security targets for a data platform

Assumes this stack

ReactNode.jsPythonMongoDBDockerKubernetes

General Forecasting Tool - Project Requirements Document (v3.0)

1. Executive Summary

A no-code analytics platform enabling users to perform advanced data analysis through an intuitive interface, supporting both file-based and live data sources, with automated model deployment capabilities. The platform will provide comprehensive data processing, analysis, and visualization features with robust security and performance.

2. Core Features

2.1 Data Management

  • File Upload: Support for CSV, Excel, JSON, XML
  • API Integration: No-code API configuration
  • Database Connectivity: SQL and NoSQL support
  • Data Validation: Real-time data quality checks
  • Data Transformation: Built-in ETL capabilities

2.2 Analysis Capabilities

  • Time Series Analysis: ARIMA, Exponential Smoothing
  • Regression Analysis: Linear, Logistic, Polynomial
  • Classification: Decision Trees, Random Forest, SVM
  • Clustering: K-Means, Hierarchical, DBSCAN
  • Anomaly Detection: Statistical, ML-based methods

2.3 Visualization

  • Interactive Charts: Line, Bar, Pie, Scatter
  • Advanced Visualizations: Heatmaps, Treemaps, Network Graphs
  • Dashboard Customization: Drag-and-drop interface
  • Real-time Updates: Streaming data visualization
  • Export Options: PNG, PDF, CSV

2.4 Model Management

  • Model Training: Automated hyperparameter tuning
  • Model Deployment: One-click deployment
  • Model Monitoring: Performance tracking
  • Version Control: Model versioning
  • Explainability: SHAP values, Feature Importance

3. Technical Requirements

3.1 Core Technologies

graph TD
    subgraph Tech Stack
        A[Frontend] --> B[React]
        A --> C[Redux]
        A --> D[Chart.js]
        
        E[Backend] --> F[Node.js]
        E --> G[Express]
        E --> H[Python]
        
        I[Database] --> J[MongoDB]
        I --> K[Redis]
        I --> L[PostgreSQL]
        
        M[Infrastructure] --> N[Docker]
        M --> O[Kubernetes]
        M --> P[NGINX]
    end

3.2 Performance Requirements

  • Response Time: < 2 seconds for 95% of requests
  • Concurrency: Support 50+ concurrent users
  • Data Handling: Process datasets up to 10GB
  • Uptime: 99.9% availability
  • Scalability: Horizontal scaling capability

3.3 Security Requirements

  • Authentication: OAuth2.0, JWT
  • Authorization: Role-based access control
  • Data Protection: AES-256 encryption
  • Audit Logs: Comprehensive activity tracking
  • Compliance: GDPR, CCPA, HIPAA

4. Implementation Phases

Phase 1: Core Platform (Weeks 1-6)

gantt
    title Phase 1: Core Platform
    dateFormat  YYYY-MM-DD
    section Architecture
    System Design          :a1, 2023-11-01, 7d
    API Specification      :a2, after a1, 5d
    Database Design        :a3, after a1, 5d
    
    section Frontend
    UI Framework Setup     :b1, 2023-11-01, 3d
    Core Components        :b2, after b1, 10d
    Basic Navigation       :b3, after b2, 5d
    
    section Backend
    API Development        :c1, after a2, 10d
    Data Processing        :c2, after a3, 10d
    Basic Analysis         :c3, after c1, 7d

Phase 2: Advanced Features (Weeks 7-12)

gantt
    title Phase 2: Advanced Features
    dateFormat  YYYY-MM-DD
    section Analysis
    Time Series Models     :a1, 2023-12-15, 10d
    Regression Models      :a2, after a1, 7d
    Classification Models  :a3, after a2, 7d
    
    section Visualization
    Chart Library          :b1, 2023-12-15, 7d
    Dashboard Builder      :b2, after b1, 10d
    Real-time Updates      :b3, after b2, 7d
    
    section Integration
    API Connector          :c1, 2023-12-15, 10d
    Database Integration   :c2, after c1, 7d
    Data Validation        :c3, after c2, 5d

Phase 3: Deployment & Optimization (Weeks 13-18)

gantt
    title Phase 3: Deployment & Optimization
    dateFormat  YYYY-MM-DD
    section Deployment
    Containerization       :a1, 2024-01-26, 7d
    CI/CD Pipeline         :a2, after a1, 5d
    Load Testing           :a3, after a2, 5d
    
    section Optimization
    Performance Tuning     :b1, 2024-01-26, 10d
    Security Hardening     :b2, after b1, 7d
    Accessibility Audit    :b3, after b2, 5d
    
    section Documentation
    User Guides            :c1, 2024-01-26, 7d
    API Documentation      :c2, after c1, 5d
    Technical Manual       :c3, after c2, 5d

5. Project Timeline

gantt
    title Project Timeline
    dateFormat  YYYY-MM-DD
    section Phases
    Core Platform          :a1, 2023-11-01, 2023-12-15
    Advanced Features      :a2, 2023-12-15, 2024-01-26
    Deployment & Optimization :a3, 2024-01-26, 2024-03-01
    
    section Milestones
    MVP Release            :milestone, m1, 2023-12-15, 0d
    Feature Complete       :milestone, m2, 2024-01-26, 0d
    Production Release     :milestone, m3, 2024-03-01, 0d
    
    section Dependencies
    a1 --> a2
    a2 --> a3

6. Risk Management

6.1 Technical Risks

  • Performance bottlenecks with large datasets
  • Integration challenges with external APIs
  • Model training and deployment complexity

6.2 Mitigation Strategies

  • Implement distributed computing for large data
  • Use API gateway for integration management
  • Containerize models for consistent deployment

7. Success Metrics

  • User Adoption: 100+ active users in first month
  • Performance: < 2s response time for 95% of requests
  • Reliability: 99.9% uptime in production
  • Accuracy: > 90% model accuracy for core algorithms
  • Satisfaction: > 4.5/5 user satisfaction rating

8. Future Enhancements

  • Mobile App: Native iOS and Android applications
  • AI Assistant: Natural language query interface
  • Collaboration: Real-time team collaboration
  • Marketplace: Pre-built model marketplace
  • Automation: AI-driven workflow automation

What's inside

8 sections covering features, tech stack, phases, timeline, risks, and success metrics

Change this for your project

  • Replace bhadkamkar9snehil/GeneralAnalyser with your repository name
  • Replace 2023-11-01 with your actual project start date
  • Replace 100+ active users with your own adoption target
  • Replace > 90% model accuracy with your accuracy benchmark

Where it goes

Keep it in your repository where the agent or team that needs it will read it.

Worth borrowing

  • Mermaid Gantt charts for phased delivery timelines
  • Risk management section pairing each risk with a mitigation strategy
  • Success metrics table with measurable targets for adoption, performance, and satisfaction

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