An end-to-end retail analytics system that forecasts product demand using machine learning and optimizes inventory using operations research formulas — deployed as an interactive streamlit simulation dashboard.
Retail businesses lose $1.75 trillion globally due to overstocking and stockouts (IHL Group, 2023). Traditional manual stock planning cannot handle thousands of products across multiple stores. This project builds a data-driven system to predict demand and automate inventory decisions.
| Problem | This System's Solution |
|---|---|
| Overstock (excess inventory costs money) | EOQ calculation minimizes order costs |
| Stockout (lost sales, lost customers) | Reorder point alert ensures timely replenishment |
| Manual forecasting is slow & inaccurate | XGBoost model achieves <10% MAPE |
| No visibility into inventory health | Taipy dashboard shows real-time status |
Companies like D-Mart, Amazon, Walmart, Flipkart, and Reliance Retail use similar systems for:
- Demand sensing and weekly procurement planning
- Category-level inventory optimization
- Festival/seasonal stock preparation
- Supplier replenishment automation
| Component | Tool |
|---|---|
| Language | Python 3.10+ |
| Data Processing | Pandas, NumPy |
| Machine Learning | XGBoost, Scikit-learn |
| Visualization | Matplotlib, Seaborn, Plotly |
| Dashboard & Simulation | Taipy (stateful, scenario-ready) |
| Notebooks | Jupyter |
| Version Control | Git + GitHub |
Raw CSV Data → Preprocessing → Feature Engineering → XGBoost Model
↓
30-Day Forecasts
↓
Inventory Optimization Engine
(Safety Stock, Reorder Point, EOQ)
↓
streamlit Interactive Dashboard
(Charts + Scenario Simulation)
Retail-Sales-Forecasting-Inventory-Optimization/
├── data/raw/ # Synthetic dataset
├── data/processed/ # Cleaned + featured data
├── notebooks/ # Jupyter EDA and modeling notebooks
├── src/ # Modular Python source code
├── models/ # Saved ML models (.pkl)
├── outputs/ # Charts, forecasts, recommendations
├── app/ # streamlit dashboard
├── images/ # Screenshots for README
├── reports/ # Business summary
├── main.py # Run full pipeline
└── requirements.txt
# Clone repository
git clone https://github.com/YOUR_USERNAME/Retail-Sales-Forecasting-Inventory-Optimization.git
cd Retail-Sales-Forecasting-Inventory-Optimization
# Create virtual environment
python -m venv retail_env
source retail_env/bin/activate # Windows: retail_env\Scripts\activate
# Install dependencies
pip install -r requirements.txt# Run full pipeline (data → model → inventory → charts)
python main.py
# Launch interactive streamlit dashboard
cd app && python dashboard.py
# → Open http://localhost:5000| Feature | Description |
|---|---|
| Rows | ~109,500 daily sales records |
| Time Period | January 2021 – January 2024 (3 years) |
| Stores | 5 (Mumbai, Delhi, Bangalore, Chennai, Hyderabad) |
| Products | 20 across 5 categories |
| Synthetic Patterns | Seasonality, weekend effects, festival spikes, stockouts |
| Model | RMSE | MAE | MAPE |
|---|---|---|---|
| XGBoost | ~4.2 | ~3.1 | ~8.3% |
| Random Forest | ~5.1 | ~3.8 | ~10.2% |
MAPE < 15% is considered good for retail demand forecasting.
Safety Stock = Z × σ_demand × √(lead_time)
Reorder Point = (avg_daily_demand × lead_time) + safety_stock
EOQ = √(2 × annual_demand × ordering_cost / holding_cost)
- Generate 3-year synthetic retail dataset
- Clean and preprocess data (handle nulls, outliers, types)
- Engineer 25+ features (lag, rolling, seasonality, price)
- Train XGBoost forecasting model (time-series split)
- Compute safety stock, reorder points, EOQ per product
- Launch streamlit dashboard and test what-if scenarios
- Multi-store ensemble forecasting
- Real-time dashboard with live data feed
- Prophet model for better trend+seasonality decomposition
- Promotional impact modeling (price elasticity)
- Weather and event-based demand adjustment
- ERP system integration (SAP/Oracle)
- Anomaly detection for unusual sales spikes
- Time-series feature engineering for ML
- XGBoost regression for demand forecasting
- Operations research inventory formulas (EOQ, ROP, Safety Stock)
- streamlit for building stateful business dashboards
- End-to-end ML pipeline design
- Professional GitHub documentation
[CH S K CHAITANYA]
Open to Data Analyst, Business Analyst, Retail Analytics, and Data Science roles.