RecceLabDocumentation

Appearance

Model Documentation -- Statistical Models ​

1. Overview ​

The system implements two main statistical models: a Prophet-based time series forecasting model and a campaign performance analysis model. These models are designed to provide accurate predictions and insights for marketing campaign performance.

2. Prophet Time Series Model ​

2.1 Model Architecture ​

2.2 Implementation Details ​

  • Model Class: ProphetPredictionModel
  • Database Integration: MongoDB
  • Key Methods:
    • get_by_id: Retrieve prediction by ID
    • get_by_campaign_id: Get predictions for specific campaign
    • create: Store new prediction
    • update: Update existing prediction
    • delete: Remove prediction

2.3 Model Parameters ​

  • Growth: Linear
  • Seasonality:
    • Daily
    • Weekly
    • Yearly
  • Holidays: Custom holiday effects
  • Changepoint Prior: 0.05
  • Seasonality Prior: 10.0

3. Campaign Performance Model ​

3.1 Model Architecture ​

3.2 Implementation Details ​

  • Model Class: CampaignModel
  • Database Integration: MongoDB
  • Key Methods:
    • get_by_id: Retrieve campaign by ID
    • get_by_company_id: Get campaigns for company
    • get_by_company_id_and_status: Filter campaigns by status
    • count_by_company_id: Count company campaigns
    • get_paginated: Paginated campaign retrieval
    • get_aggregated: Aggregated campaign data
    • create: Create new campaign
    • update: Update existing campaign
    • delete: Remove campaign

3.3 Performance Metrics ​

  • Key Metrics:
    • Click-through Rate (CTR)
    • Conversion Rate
    • Cost per Acquisition (CPA)
    • Return on Ad Spend (ROAS)
  • Trend Analysis:
    • Daily trends
    • Weekly patterns
    • Monthly comparisons

4. Data Processing Pipeline ​

4.1 Data Collection ​

  • Real-time data ingestion
  • Historical data integration
  • Data validation

4.2 Data Preprocessing ​

  • Missing value handling
  • Outlier detection
  • Feature engineering

4.3 Data Storage ​

  • MongoDB collections
  • Indexed queries
  • Data partitioning

5. Model Updates and Maintenance ​

5.1 Retraining Schedule ​

  • Prophet Model: Weekly
  • Campaign Model: Daily

5.2 Performance Monitoring ​

  • Real-time metrics tracking
  • Alert system for anomalies
  • Automated retraining triggers

6. Integration with Frontend ​

6.1 API Endpoints ​

  • Prediction retrieval
  • Campaign data access
  • Performance metrics

6.2 Data Visualization ​

  • Time series plots
  • Performance dashboards
  • Trend analysis charts

7. Security and Privacy ​

7.1 Data Protection ​

  • Encryption at rest
  • Secure data transfer
  • Access control

7.2 Compliance ​

  • GDPR compliance
  • Data retention policies
  • Audit logging