Overview
Developed an interactive sales forecasting dashboard that combines time series analysis with machine learning to predict future sales trends.
Key Features
- Automated time series forecasting (ARIMA, Prophet, LSTM)
- Interactive visualizations with drill-down capabilities
- Seasonality and trend decomposition
- Multi-region and multi-product forecasting
- Anomaly detection for unusual patterns
Technical Stack
- Analysis: Python, Pandas, NumPy
- Forecasting: Prophet, ARIMA, TensorFlow
- Visualization: Tableau, Plotly Dash
- Database: PostgreSQL
Results
- 94% forecast accuracy for monthly sales
- Improved inventory planning by 25%
- Reduced stockouts by 30%
- Identified seasonal patterns across 15 product categories
Implementation
The dashboard processes historical sales data and generates forecasts using:
- Facebook Prophet for trend and seasonality
- LSTM neural networks for complex patterns
- ARIMA for baseline predictions
- Ensemble methods for final forecasts
Updated daily with new sales data and provides 30-day rolling forecasts with confidence intervals.