This project focuses on using time series forecasting to predict future sales demand based on historical data.
It demonstrates how analytics can empower operations teams to optimize production, inventory, and workforce planning through accurate demand predictions.
This project replicates a real-world Operations Analyst use case — helping businesses plan proactively using data-driven forecasts.
The model leverages Holt–Winters Exponential Smoothing to capture both trend and seasonality in e-commerce sales data.
- Analyze monthly sales patterns and detect seasonality
- Build and validate a forecasting model
- Generate a 12-month forward demand forecast
- Translate forecasts into operational recommendations
- Enable proactive resource and inventory management
- Data Preparation: Load and aggregate e-commerce sales data by month
- Exploratory Analysis: Visualize historical sales and trends
- Model Building: Apply Holt–Winters method to model trend and seasonality
- Forecasting: Predict the next 12 months of demand
- Insights & Recommendations: Present key operational takeaways
- Python (Google Colab)
- Libraries: pandas, matplotlib, seaborn, statsmodels
- Dataset: E-commerce Sales Data (
train.csv)
- Clear seasonality patterns with recurring Q4 demand peaks
- Holt–Winters model achieved stable forecasts with low error metrics
- Predicted ~12–15% sales growth YoY
- Recommended inventory and workforce scaling for peak months
| File | Description |
|---|---|
Forecasting_Demand_Planning.ipynb |
Jupyter/Colab notebook containing full code, visuals, and insights |
train.csv |
Dataset used for modeling |
README.md |
Project summary and documentation |
- Built an automated forecasting pipeline using Holt–Winters model
- Derived actionable business insights for operations optimization
- Demonstrated an end-to-end Operations Analyst workflow
- Ready-to-scale solution for similar business forecasting scenarios