An end-to-end AI-based system designed to forecast retail sales and optimize inventory management using machine learning techniques. This project simulates real-world retail scenarios and provides actionable insights through data analysis and interactive dashboards.
Retail businesses face several operational challenges:
• Demand uncertainty • Overstocking and understocking • Inefficient inventory planning • Revenue loss due to stockouts
Accurate demand forecasting and inventory optimization are essential to improve efficiency and reduce losses.
This system uses machine learning to:
• Predict future sales trends • Analyze historical retail data • Optimize inventory decisions • Reduce stockouts and excess inventory
It implements a complete pipeline from data simulation to visualization.
• End-to-end ML pipeline (Data → Preprocessing → Training → Prediction) • Synthetic data simulation with realistic patterns • Feature engineering (trend, seasonality, promotions) • Sales forecasting using machine learning models • Inventory optimization logic • Interactive dashboard using Streamlit • Modular and scalable project structure
• Input: Historical retail sales data • Output: Predicted future demand and optimized inventory levels
• Python • Pandas, NumPy • Scikit-learn • Matplotlib / Seaborn • Streamlit
Retail-Sales-Forecasting-Inventory-Optimization/
├── data/ │ └── retail_data.csv ├── outputs/ │ └── images/ ├── notebooks/ │ └── simulation.ipynb ├── src/ │ ├── data_preprocessing.py │ ├── feature_engineering.py │ ├── model.py │ ├── inventory.py │ ├── visualization.py │ ├── app/ │ └── app.py │ ├── main.py ├── requirements.txt └── README.md
The interactive dashboard provides insights into sales trends, inventory levels, and key performance metrics.
• Synthetic retail data is generated using simulation • Data preprocessing and feature engineering are applied • Machine learning model is trained • Future sales are predicted • Inventory optimization logic is applied • Results are visualized through a dashboard
- Clone Repository
git clone https://github.com/Nikhatjahan85/Retail-Sales-Forecasting.git cd Retail-Sales-Forecasting
- Install Dependencies
pip install -r requirements.txt
- Run Main Pipeline
python main.py
- Run Dashboard
streamlit run app.py
• Real-time sales forecasting • Advanced models (LSTM, XGBoost) • Multi-store inventory optimization • Cloud deployment (AWS, Azure) • API integration for live data
Nikhat Jahan GitHub: https://github.com/Nikhatjahan85�