Can u help me write a frontend dashboard for me based on these descriptions:

Based on your comprehensive financial monitoring system plan, I'll propose an interactive dashboard design that showcases both your data science expertise and business analytics acumen. Here's a detailed design proposal:

🏗️ Dashboard Architecture & Navigation

**Main Navigation Structure**

🏠 Executive Summary → 📊 Financial Analytics → 🔍 Anomaly Detection → 📈 Forecasting → 🤖 Model Hub → ⚙️ System Health

📋 Page-by-Page Design Proposal

1. **Executive Summary Dashboard**

The "CEO View" - High-level KPIs and insights

Top Row - Hero Metrics (4 large cards)

• Total Expenses YTD with % change vs prior year
• Budget Variance with traffic light indicators
• Active Anomalies with severity breakdown
• Forecast Confidence with model performance score

Second Row - Interactive Charts (2 columns)

• Left: Monthly expense trends with overlaid forecast bands
• Right: Department spending heatmap with drill-down capability

Third Row - Insights Panel

• Alert Feed: Real-time anomaly alerts with risk scores
• Key Findings: AI-generated insights from recent analysis
• Recommended Actions: Data-driven recommendations

Bottom Row - Quick Access Widgets

• Recently flagged transactions table
• Top spending categories
• Budget utilization gauges

2. **Financial Analytics Dashboard**

Deep-dive analytical capabilities

Left Sidebar - Filters & Controls

• Date range picker with preset options
• Multi-select for departments/accounts
• Transaction amount thresholds
• Account category filters

Main Content Area (3x2 grid)

• Top Left: Revenue vs Expenses trend analysis with statistical annotations
• Top Right: Cash flow analysis with seasonal decomposition
• Middle Left: Account balance evolution with statistical process control charts
• Middle Right: Vendor spending analysis with Pareto charts
• Bottom Left: Transaction volume patterns with time series clustering
• Bottom Right: Budget vs Actual variance analysis with confidence intervals

Right Panel - Statistical Insights

• Correlation matrix heatmap
• Distribution analysis plots
• Statistical test results panel
• Hypothesis testing interface

3. **Anomaly Detection Center**

Showcase ML and statistical modeling expertise

Header - Detection Status Bar

• Model performance metrics (precision, recall, F1-score)
• Detection confidence thresholds with sliders
• Real-time processing status

Main Grid (2x3 layout)

• Top Left: Anomaly timeline with severity coding and filtering
• Top Right: 3D scatter plot of anomalies in feature space
• Middle Left: Model explanation dashboard (SHAP values, feature importance)
• Middle Right: Statistical control charts with out-of-control points
• Bottom Left: Anomaly investigation panel with transaction details
• Bottom Right: Model comparison metrics and A/B testing results

Investigation Panel (Expandable)

• Transaction details with contextual information
• Similar transactions finder
• Manual labeling interface for model improvement
• Investigation notes and resolution tracking

4. **Forecasting Laboratory**

Time series expertise and predictive analytics

Control Panel (Top)

• Forecast horizon selector (1 month to 2 years)
• Model selection (SARIMA, Prophet, GP, Ensemble)
• Confidence interval settings
• Seasonality toggles

Main Visualization Area

• Primary Chart: Interactive time series with historical data, forecasts, and uncertainty bands
• Model Comparison Panel: Side-by-side forecasts from different algorithms
• Decomposition View: Trend, seasonal, and residual components
• Accuracy Metrics Dashboard: RMSE, MAE, MAPE across different horizons

Advanced Analytics Panels

• Scenario Modeling: What-if analysis with parameter sliders
• Feature Impact: How external factors influence predictions
• Forecast Reconciliation: Bottom-up vs top-down forecasting
• Model Diagnostics: Residual analysis and validation metrics

5. **Model Hub & MLOps Center**

Demonstrate MLOps and model management skills

Model Registry (Left Panel)

• List of deployed models with versions
• Performance metrics comparison
• Model lineage and data dependencies
• Deployment status indicators

Experiment Tracking (Center)

• MLflow experiments browser with sorting/filtering
• Hyperparameter comparison charts
• Training progress visualization
• Model performance evolution over time

Model Monitoring (Right Panel)

• Data drift detection plots
• Model performance degradation alerts
• Feature importance evolution
• Prediction distribution monitoring

Bottom Section - Model Insights

• A/B testing results dashboard
• Champion vs challenger model comparison
• Model explainability interface (SHAP, LIME)
• Bias and fairness monitoring

6. **System Health & Operations**

Data engineering and operational excellence

Data Pipeline Status (Top)

• ETL pipeline health indicators
• Data quality scorecards
• Processing latency metrics
• Error rate monitoring

Infrastructure Monitoring (Middle)

• System performance metrics
• API response times
• Database performance
• Model serving latency

Data Quality Dashboard (Bottom)

• Data completeness over time
• Schema drift detection
• Data profiling comparisons
• Quality rule violations

🎨 Design System & Visual Elements

**Color Palette**

• Primary: Deep blue (#1f2937) for headers and navigation
• Accent: Emerald green (#10b981) for positive metrics
• Warning: Amber (#f59e0b) for attention items
• Danger: Red (#ef4444) for alerts and anomalies
• Neutral: Gray scale for backgrounds and text

**Interactive Components**

• Hover Effects: Dynamic tooltips with detailed information
• Click-through: Drill-down from summary to detail views
• Filters: Real-time data filtering with visual feedback
• Export Options: PDF reports, Excel downloads, PNG charts

**Advanced Visualizations**

• 3D Scatter Plots: For high-dimensional anomaly detection
• Network Graphs: For transaction relationship mapping
• Sankey Diagrams: For fund flow visualization
• Waterfall Charts: For budget variance breakdown
• Box Plots: For distribution analysis
• Heatmaps: For correlation and pattern detection

🚀 Key Differentiating Features

**Technical Sophistication**

1. Real-time Updates: WebSocket connections for live data
2. Progressive Loading: Intelligent data fetching and caching
3. Mobile Responsive: Adaptive layouts for all devices
4. Accessibility: WCAG 2.1 compliant design

**Business Intelligence**

1. Natural Language Interface: Query data using plain English
2. Automated Insights: AI-generated findings and recommendations
3. Custom Alerting: User-defined thresholds and notifications
4. Collaborative Features: Comments, annotations, and sharing

**Analytics Excellence**

1. Statistical Annotations: P-values, confidence intervals on charts
2. Uncertainty Visualization: Proper representation of prediction intervals
3. Model Interpretability: Built-in explainable AI features
4. Comparative Analysis: Side-by-side model and metric comparisons

This dashboard design showcases your technical depth while maintaining business relevance, demonstrating both your data science capabilities and understanding of financial operations. The progressive disclosure of complexity allows users to start with executive summaries and drill down into technical details as needed.

I'll create a comprehensive financial monitoring dashboard that showcases your data science expertise and business analytics acumen. This will include all six main sections: Executive Summary, Financial Analytics, Anomaly Detection, Forecasting, Model Hub, and System Health, with modern visualizations and interactive components.